Blending algorithms model land cover change by using highly resolved spatial data from one sensor and highly resolved temporal data from another. Because the data are not usually observed concurrently, unaccounted spatial and temporal variances cause error in blending algorithms, yet, to date, there has been no definitive assessment of algorithm performance against spatial and temporal variances. Our objectives were to: (i) evaluate the accuracy of two advanced blending algorithms (STARFM and ESTARFM) and two simple benchmarking algorithms in two landscapes with contrasting spatial and temporal variances; and (ii) synthesise the spatial and temporal conditions under which the algorithms performed best. Landsat-like images were simulated on 27 dates in total using the nearest temporal cloud-free Landsat-MODIS pairs to the simulation date, one before and one after. RMSD, bias, and r(2) estimates between simulated and observed Landsat images were calculated, and overall variance of Landsat and MODIS datasets were partitioned into spatial and temporal components. Assessment was performed over the whole study site, and for specific land covers. Results addressing objective (i) were that: ESTARFM did not always produce lower errors than STARFM; STARFM and ESTARFM did not always produce lower errors than simple benchmarking algorithms; and land cover spatial and temporal variances were strongly associated with algorithm performance. Results addressing objective (ii) indicated ESTARFM was superior where/when spatial variance was dominant; and STARFM was superior where/when temporal variance was dominant. We proposed a framework for selecting blending algorithms based on partitioning variance into the spatial and temporal components and suggested that comparing Landsat and MODIS spatial and temporal variances was a practical method to determine if, and when, MODIS could add value for blending. Crown Copyright (c) 2013 Published by Elsevier Inc. All rights reserved.

Recent improvements in instrumentation have made possible the acquisition of high spectral resolution image data corresponding to spectral reflectance signatures. It would seem that sufficiently high spectral resolution, of order 0.01 m, would be adequate to permit unique discrimination of virtually any soils, vegetation types, and rocks, but we show by example that this is not necessarily the case.

Remotely sensed data, with high spatial and temporal resolutions, can hardly be provided by only one sensor due to the tradeoff in sensor designs that balance spatial resolutions and temporal coverage. However, they are urgently needed for improving the ability of monitoring rapid landscape changes at fine scales (e.g., 30 m). One approach to acquire them is by fusing observations from sensors with different characteristics (e.g., Enhanced Thematic Mapper Plus (ETM+) and Moderate Resolution Imaging Spectroradiometer (MODIS)). The existing data fusion algorithms, such as the Spatial and Temporal Data Fusion Model (STDFM), have achieved some significant progress in this field. This paper puts forward an Enhanced Spatial and Temporal Data Fusion Model (ESTDFM) based on the STDFM algorithm, by introducing a patch-based ISODATA classification method, the sliding window technology, and the temporal-weight concept. Time-series ETM+ and MODIS surface reflectance are used as test data for comparing the two algorithms. Results show that the prediction ability of the ESTDFM algorithm has been significantly improved, and is even more satisfactory in the near-infrared band (the contrasting average absolute difference [AAD]: 0.0167 vs. 0.0265). The enhanced algorithm will support subsequent research on monitoring land surface dynamic changes at finer scales.

陆面数据同化的核心思想是在陆面过程模型的动力框架内,融合不同来源和不同分辨率的直接与间接观测,准确一致地估计地表水分和能量循环的各个分量.作者已初步建立了中国陆面数据同化系统;同化方法采用了集合Kalman滤波,模型算子使用CoLM和SiB2等SiB2等陆面过程模型,观测算子采用针对土壤(包括融化和冻结)、积雪等不同地表状态的微波辐射传输模型.该系统同化被动微波遥感观测如SSM/I,TMI和AMSR以及土壤和积雪的常规观测,已计算得到中国西北干旱区和青藏高原的土壤水分、土壤温度、积雪和冻土的同化资料.文中阐述了在同化系统的构建、同化算法的研究、陆面过程模型和微波辐射传输模型的集成以及大气驱动数据制备等方面的进展,讨论了陆面数据同化的特点和面临的问题,展望了未来的研究重点和同化方法在陆地表层系统科学研究中的应用前景.

This paper presents the analysis of radiative transfer assumptions underlying moderate resolution imaging spectroradiometer (MODIS) leaf area index (LAI) and fraction of photosynthetically active radiation (FPAR) algorithm for the case of spatially heterogeneous broadleaf forests. Data collected by a Boston University research group during the July 2000 field campaign at the Earth Observing System (EOS) core validation site, Harvard Forest, MA, were used for this purpose. The analysis covers three themes. First, the assumption of wavelength independence of spectral invariants of transport equation, central to the parameterization of the MODIS LAI and FPAR algorithm, is evaluated. The physical interpretation of those parameters is given and an approach to minimize the uncertainties in its retrievals is proposed. Second, the theoretical basis of the algorithm was refined by introducing stochastic concepts which account for the effect of foliage clumping and discontinuities on LAI retrievals. Third, the effect of spatial heterogeneity in FPAR was analyzed and compared to FPAR variation due to diurnal changes in solar zenith angle (SZA) to asses the validity of its static approximation.

Landsat imagery with a 3002m spatial resolution is well suited for characterizing landscape-level forest structure and dynamics. While Landsat images have advantageous spatial and spectral characteristics for describing vegetation properties, the Landsat sensor's revisit rate, or the temporal resolution of the data, is 1602days. When considering that cloud cover may impact any given acquisition, this lengthy revisit rate often results in a dearth of imagery for a desired time interval (e.g., month, growing season, or year) especially for areas at higher latitudes with shorter growing seasons. In contrast, MODIS (MODerate-resolution Imaging Spectroradiometer) has a high temporal resolution, covering the Earth up to multiple times per day, and depending on the spectral characteristics of interest, MODIS data have spatial resolutions of 25002m, 50002m, and 100002m. By combining Landsat and MODIS data, we are able to capitalize on the spatial detail of Landsat and the temporal regularity of MODIS acquisitions. In this research, we apply and demonstrate a data fusion approach (Spatial and Temporal Adaptive Reflectance Fusion Model, STARFM) at a mainly coniferous study area in central British Columbia, Canada. Reflectance data for selected MODIS channels, all of which were resampled to 50002m, and Landsat (at 3002m) were combined to produce 18 synthetic Landsat images encompassing the 2001 growing season (May to October). We compared, on a channel-by-channel basis, the surface reflectance values (stratified by broad land cover types) of four real Landsat images with the corresponding closest date of synthetic Landsat imagery, and found no significant difference between real (observed) and synthetic (predicted) reflectance values (mean difference in reflectance: mixed forest x00 = 0.086, σ = 0.088, broadleaf x00 = 0.019, σ = 0.079, coniferous x00 = 0.039, σ = 0.093). Similarly, a pixel based analysis shows that predicted and observed reflectance values for the four Landsat dates were closely related (mean r 2 = 0.76 for the NIR band; r 2 = 0.54 for the red band; p < 0.01). Investigating the trend in NDVI values in synthetic Landsat values over a growing season revealed that phenological patterns were well captured; however, when seasonal differences lead to a change in land cover (i.e., disturbance, snow cover), the algorithm used to generate the synthetic Landsat images was, as expected, less effective at predicting reflectance.

With the availability of multisensor, multitemporal, multiresolution and multifrequency image data from operational Earth observation satellites the fusion of digital image data has become a valuable tool in remote sensing image evaluation. Digital image fusion is a relatively new research field at the leading edge of available technology. It forms a rapidly developing area of research in remote sensing. This review paper describes and explains mainly pixel based image fusion of Earth observation satellite data as a contribution to multisensor integration oriented data processing.

采用中分辨率与低分辨率数据相结合的遥感估算方法是获取大范围作物种植面积的主要趋势之一。在MODIS时序影像和TM影像支持下，采用小波变换融合方法获取30 m分辨率的NDVI时间序列信息，构建主要秋季作物的NDVI标准生长曲线，以最小距离分类器进行分层分类，获得河南原阳县玉米种植面积总量信息和空间分布，并以野外实测地块样本进行精度评价。由于充分利用MODIS影像的时间序列优势和TM影像的空间分辨率优势，通过秋季作物之间的物候差异，有效地区分出各种秋季作物的空间分布，基于融合NDVI时间序列信息玉米种植面积遥感估算的位置和面积精度分别达到79%和89%，远高于常规的监督分类，为大范围玉米种植面积遥感估算提供了一种可行的方法。

Multi-sensor image fusion using the wavelet approach provides a conceptual framework for the improvement of the spatial resolution with minimal distortion of the spectral content of the source image. This paper assesses whether images with a large ratio of spatial resolution can be fused, and evaluates the potential of using such fused images for mapping the Brazilian Savanna. Three types of wavelet transforms were used to perform the fusion between MODIS and Landsat TM images. Five quality measures were defined to assess the quality of the fused images. The results showed that it was possible to perform the fusion of MODIS and TM images and the pyramidal in Fourier space wavelet transform provided the best quality measures for the fused images. Classification results showed that fused images could be used for mapping the Brazilian Savanna with an accuracy level comparable to the Landsat TM image.

The resulting image features the color of NOAA NDVI and topographic details of MSS intensity. This work offers relevant techniques to extract information from satellite imagery to apply to plant dynamics investigation.

In this paper we demonstrate a new approach that uses regional/continental MODIS (MODerate Resolution Imaging Spectroradiometer) derived forest cover products to calibrate Landsat data for exhaustive high spatial resolution mapping of forest cover and clearing in the Congo River Basin. The approach employs multi-temporal Landsat acquisitions to account for cloud cover, a primary limiting factor in humid tropical forest mapping. A Basin-wide MODIS 250m Vegetation Continuous Field (VCF) percent tree cover product is used as a regionally consistent reference data set to train Landsat imagery. The approach is automated and greatly shortens mapping time. Results for approximately one third of the Congo Basin are shown. Derived high spatial resolution forest change estimates indicate that less than 1% of the forests were cleared from 1990 to 2000. However, forest clearing is spatially pervasive and fragmented in the landscapes studied to date, with implications for sustaining the region's biodiversity. The forest cover and change data are being used by the Central African Regional Program for the Environment (CARPE) program to study deforestation and biodiversity loss in the Congo Basin forest zone. Data from this study are available at http://carpe.umd.edu .

Constrained and unconstrained algorithms of the multisensor multiresolution technique (MMT) are discussed. They can be applied to unmix low-resolution images using the information about their pixel composition from co-registered high-resolution images. This makes it possible to fuse the low- and high-resolution images for a synergetic interpretation. The constrained unmixing preserves all the available radiometric information of the low-resolution image. On the other hand, the unconstrained unmixing may be preferable in case of noisy data. An analysis of the MMT sensitivity to sensor errors showed that the strongest requirement is the accuracy of geometric co-registration of the data; the co-registration errors should not exceed 0.1-0.2 of the low-resolution pixel size. Applications of the constrained and unconstrained algorithms are illustrated on examples of unmixing and fusion of the multiresolution reflective and thermal bands of a real TM/LANDSAT image as well as of a simulated image of the future ASTER/EOS-AMI sensor.

Linear regression procedures can be applied to derive spectral endmembers using satellite images and superimposed abundance estimates of known components. A common problem, however, is represented by the spatial variability of the spectral endmembers to estimate, which may be caused by variations in several environmental factors (topography, water availability, soil type, etc.). This problem is currently addressed by a modified multivariate regression procedure that can define spatially variable spectral endmembers. The procedure is based on a local calibration of the regression statistics (mean vectors and variance/covariance matrices), which is obtained by weighting the values of the training pixels according to their distance from each pixel examined. The locally found regression statistics are then used to extrapolate pure class spectral endmembers, which are therefore different for each image pixel. An experiment was carried out using multitemporal NOAA-AVHRR NDVI profiles and class abundance estimates of Tuscany region in central Italy. The results show that the spatially variable spectral endmembers are far more accurate than conventional fixed endmembers to recompose the original NDVI imagery. Finally, it is discussed how these spatially variable pure class NDVI values can serve for data integration and as input for agro-meteorological applications and ecosystem simulation modeling.

The potential of multitemporal coarse spatial resolution remotely sensed images for vegetation monitoring is reduced in fragmented landscapes, where most of the pixels are composed of a mixture of different surfaces. Several approaches have been proposed for the estimation of reflectance or NDVI values of the different land-cover classes included in a low resolution mixed pixel. In this paper, we propose a novel approach for the estimation of sub-pixel NDVI values from multitemporal coarse resolution satellite data. Sub-pixel NDVIs for the different land-cover classes are calculated by solving a weighted linear system of equations for each pixel of a coarse resolution image, exploiting information about within-pixel fractional cover derived from a high resolution land-use map. The weights assigned to the different pixels of the image for the estimation of sub-pixel NDVIs of a target pixel i are calculated taking into account both the spatial distance between each pixel and the target and their spectral dissimilarity estimated on medium-resolution remote-sensing images acquired in different periods of the year. The algorithm was applied to daily and 16-day composite MODIS NDVI images, using Landsat-5 TM images for calculation of weights and accuracy evaluation. Results showed that application of the algorithm provided good estimates of sub-pixel NDVIs even for poorly represented land-cover classes (i.e., with a low total cover in the test area). No significant accuracy differences were found between results obtained on daily and composite MODIS images. The main advantage of the proposed technique with respect to others is that the inclusion of the spectral term in weight calculation allows an accurate estimate of sub-pixel NDVI time series even for land-cover classes characterized by large and rapid spatial variations in their spectral properties.

The 16-day revisit cycle of Landsat has long limited its use for studying global biophysical processes, which evolve rapidly during the growing season. In cloudy areas of the Earth, the problem is compounded, and researchers are fortunate to get two to three clear images per year. At the same time, the coarse resolution of sensors such as the Advanced Very High Resolution Radiometer and Moderate Resolution Imaging Spectroradiometer (MODIS) limits the sensors' ability to quantify biophysical processes in heterogeneous landscapes. In this paper, the authors present a new spatial and temporal adaptive reflectance fusion model (STARFM) algorithm to blend Landsat and MODIS surface reflectance. Using this approach, high-frequency temporal information from MODIS and high-resolution spatial information from Landsat can be blended for applications that require high resolution in both time and space. The MODIS daily 500-m surface reflectance and the 16-day repeat cycle Landsat Enhanced Thematic Mapper Plus (ETM+) 30-m surface reflectance are used to produce a synthetic "daily" surface reflectance product at ETM+ spatial resolution. The authors present results both with simulated (model) data and actual Landsat/MODIS acquisitions. In general, the STARFM accurately predicts surface reflectance at an effective resolution close to that of the ETM+. However, the performance depends on the characteristic patch size of the landscape and degrades somewhat when used on extremely heterogeneous fine-grained landscapes.

Investigating the temporal and spatial pattern of landscape disturbances is an important requirement for modeling ecosystem characteristics, including understanding changes in the terrestrial carbon cycle or mapping the quality and abundance of wildlife habitats. Data from the Landsat series of satellites have been successfully applied to map a range of biophysical vegetation parameters at a 30 m spatial resolution; the Landsat 16 day revisit cycle, however, which is often extended due to cloud cover, can be a major obstacle for monitoring short term disturbances and changes in vegetation characteristics through time. The development of data fusion techniques has helped to improve the temporal resolution of fine spatial resolution data by blending observations from sensors with differing spatial and temporal characteristics. This study introduces a new data fusion model for producing synthetic imagery and the detection of changes termed Spatial Temporal Adaptive Algorithm for mapping Reflectance Change (STAARCH). The algorithm is designed to detect changes in reflectance, denoting disturbance, using Tasseled Cap transformations of both Landsat TM/ETM and MODIS reflectance data. The algorithm has been tested over a 185 185 km study area in west-central Alberta, Canada. Results show that STAARCH was able to identify spatial and temporal changes in the landscape with a high level of detail. The spatial accuracy of the disturbed area was 93% when compared to the validation data set, while temporal changes in the landscape were correctly estimated for 87% to 89% of instances for the total disturbed area. The change sequence derived from STAARCH was also used to produce synthetic Landsat images for the study period for each available date of MODIS imagery. Comparison to existing Landsat observations showed that the change sequence derived from STAARCH helped to improve the prediction results when compared to previously published data fusion techniques.

Due to technical and budget limitations, remote sensing instruments trade spatial resolution and swath width. As a result not one sensor provides both high spatial resolution and high temporal resolution. However, the ability to monitor seasonal landscape changes at fine resolution is urgently needed for global change science. One approach is to lend the radiometry from daily, global data (e.g. MODIS, MERIS, SPOT-Vegetation) with data from high-resolution sensors with less frequent coverage (e.g. Landsat, CBERS, ResourceSat). Unfortunately, existing algorithms for blending multi-source data have some shortcomings, particularly in accurately predicting the surface reflectance of heterogeneous landscapes. This study has developed an enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) based on the existing STARFM algorithm, and has tested it with both simulated and actual satellite data. Results show that ESTARFM improves the accuracy of predicted fine-resolution reflectance, especially for heterogeneous landscapes, and preserves spatial details. Taking the NIR band as an example, for homogeneous regions the prediction of the ESTARFM is slightly better than the STARFM (average absolute difference [ AAD] 0.0106 vs. 0.0129 reflectance units). But for a complex, heterogeneous landscape, the prediction accuracy of ESTARFM is improved even more compared with STARFM ( AAD 0.0135 vs. 0.0194). This improved fusion algorithm will support new investigations into how global landscapes are changing across both seasonal and interannual timescales.

针对ETM 空间分辨率高和MODIS 时间分辨率高的特点, 选择官厅水库上游为实验区, 基于对STARFM方法的改进, 构建不同时空分辨率NDVI 的时空融合模型-STAVFM, 使用该模型对ETM NDVI 与MODIS NDVI 融合, 构建了高时空分辨率NDVI 数据集。研究结果表明, STAVFM 根据植被变化特点定义了有效时间窗口, 在考虑物候影响的同时改进了时间维的加权方式, 通过MODIS NDVI 时间变化信息与ETM NDVI 空间差异信息的有机结合, 实现缺失高空间分辨率NDVI 的有效预测(3 景预测NDVI 与实际NDVI 的相关系数分别达到了0.82、0.90 和0.91), 从而构建高时空分辨率NDVI 数据集, 其时间上保留了高时间分辨率数据的时间变化趋势, 空间上又反映高空间分辨率数据的空间细节差异。

While data like HJ-1 CCD images have advantageous spatial characteristics for describing crop properties, the temporal resolution of the data is rather low, which can be easily made worse by cloud contamination. In contrast, although Moderate Resolution Imaging Spectroradiometer (MODIS) can only achieve a spatial resolution of 250 m in its normalised difference vegetation index (NDVI) product, it has a high temporal resolution, covering the Earth up to multiple times per day. To combine the high spatial resolution and high temporal resolution of different data sources, a new method (Spatial and Temporal Adaptive Vegetation index Fusion Model [STAVFM]) for blending NDVI of different spatial and temporal resolutions to produce high spatial temporal resolution NDVI datasets was developed based on Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). STAVFM defines a time window according to the temporal variation of crops, takes crop phenophase into consideration and improves the temporal weighting algorithm. The result showed that the new method can combine the temporal information of MODIS NDVI and spatial difference information of HJ-1 CCD NDVI to generate an NDVI dataset with both high spatial and high temporal resolution. An application of the generated NDVI dataset in crop biomass estimation was provided. An average absolute error of 17.2% was achieved. The estimated winter wheat biomass correlated well with observed biomass (R2 of 0.876). We conclude that the new dataset will improve the application of crop biomass estimation by describing the crop biomass accumulation in detail. There is potential to apply the approach in many other studies, including crop production estimation, crop growth monitoring and agricultural ecosystem carbon cycle research, which will contribute to the implementation of Digital Earth by describing land surface processes in detail.

Image fusion involves merging two or more images in such a way as to retain the most desirable characteristics of each. When a panchromatic image is fused with multispectral imagery, the desired result is an image with the spatial resolution and quality of the panchromatic imagery and the spectral resolution and quality of the multispectral imagery. Standard image fusion methods are often successful at injecting spatial detail into the multispectral imagery but distort the colour information in the process. Over the past decade, a significant amount of research has been conducted concerning the application of wavelet transforms in image fusion. In this paper, an introduction to wavelet transform theory and an overview of image fusion technique are given, and the results from a number of wavelet-based image fusion schemes are compared. It has been found that, in general, wavelet-based schemes perform better than standard schemes, particularly in terms of minimizing colour distortion. Schemes that combine standard methods with wavelet transforms produce superior results than either standard methods or simple wavelet-based methods alone. The results from wavelet-based methods can also be improved by applying more sophisticated models for injecting detail information; however, these schemes often have greater set-up requirements.

Our framework is the synthesis of multispectral images (MS) at higher spatial resolution, which should be as close as possible to those that would have been acquired by the corresponding sensors if they had this high resolution. This synthesis is performed with the help of a high spatial but low spectral resolution image: the panchromatic (Pan) image. The fusion of the Pan and MS images is classically referred as pan-sharpening. A fused product reaches good quality only if the characteristics and differences between input images are taken into account. Dissimilarities existing between these two data sets originate from two causes-different times and different spectral bands of acquisition. Remote sensing physics should be carefully considered while designing the fusion process. Because of the complexity of physics and the large number of unknowns, authors are led to make assumptions to drive their development. Weaknesses and strengths of each reported method are raised and confronted to these physical constraints. The conclusion of this critical survey of literature is that the choice in the assumptions for the development of a method is crucial, with the risk to drastically weaken fusion performance. It is also shown that the Amelioration de la Resolution Spatiale par Injection de Structures concept prevents from introducing spectral distortion into fused products and offers a reliable framework for further developments.

All three described scaling techniques can be integrated as the relevant methodological steps within a complex multi-source approach. We present this concept of combining numerous optical remote sensing data and methods to generate inputs for ecosystem process models.

To carry out functioning and dynamic vegetation studies, a temporal analysis is needed. So far, only data provided by the National Oceanic and Atmospheric Administration (NOAA) satellites with Advanced Very High Resolution Radiometer (AVHRR) sensors offer the required temporal resolution, but their spatial resolution is coarse (1.1 km). But, in many situations, the vegetation cover is heterogeneous and the 1.1 km AVHRR pixel contains several types of land use radiometrically different and is, in fact, a mixed pixel. Thus, the reflectance and consequently deduced parameters (NDVI, LAI, etc.) measured by AVHRR is actually average value and does not represent a value for each vegetation class present in the pixel. The objective is to extract the reflectance of each vegetation class from the mixed pixel using NOAA-AVHRR data and SPOT-HRV data simultaneously which give the proportions of each type of vegetation inside the mixed pixel through a classification map. The paper presents a method for radiometrically unmixing coarse resolution signals through the inversion of linear mixture modelling on heterogeneous regions of natural vegetation (Bidi-Bahn) in Burkina-Faso and in Niger (Hapex site). In a first step, simulated coarse resolution data (NOAA-AVHRR) obtained from the degradation of SPOT images are used to assess the method. In a second step, real NOAA-AVHRR data are used and some elements of validation are given by comparing the results to airborne reflectance measurements.

Different ways to estimate the spectral reflectance for the component classes in a mixture problem have been proposed in the literature (pure pixels, spectral library, field measurements). One of the most common approaches consists in the use of pure pixels, i.e., pixels that are covered by a single component class. This approach presents the advantage of allowing the extraction of the components' reflectance directly from the image data. This approach, however, is generally not feasible in the case of low spatial resolution image data, due to the large ground area covered by a single pixel. In this paper, a methodology aiming to overcome this limitation is proposed. The proposed approach makes use of the spectral linear mixing model. In the proposed methodology, the components' proportions in image data are estimated using a medium spatial resolution image as auxiliary data. The linear mixing model is then solved for the unknown spectral reflectances. Experiments are presented, using Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat Enhanced Thematic Mapper Plus, as low and medium spatial resolution image data, respectively, acquired on the same date over the Tapajos study site, Brazilian Amazon. Three component classes or endmembers are present in the scene covered by the experiment, namely vegetation, exposed soil, and shade. The components' spectral reflectance for the Terra MODIS spectral bands were then estimated by applying the proposed methodology. The reliability of these estimates is appraised by analyzing scatter diagrams produced by the Terra MODIS spectral bands and also by comparing the fraction images produced using both image datasets. This methodology appears appropriate for up-scaling information for regional and global studies.

The integration of the useful features of high and low spatial and temporal resolution satellite data is a major issue in remote sensing studies. The current work presents the development and testing of a procedure based on classification and regression analysis techniques for generating an NDVI data set with the spatial resolution of Landsat TM images and the temporal resolution of NOAA AVHRR maximum-value composites. The procedure begins with a classification of the high resolution TM data which yields land use references. These are degraded to low spatial resolution in order to produce abundance images comparable with the AVHRR data. Linear regressions are then applied between the AVHRR NDVI data and the abundance images to estimate the profiles of the pure classes, which are then merged to the high spatial resolution classification outputs to generate an integrated data set. Experiments carried out in an area of Tuscany (Central Italy) intercomparing different strategies for each methodological step (hard and fuzzy classification, mean and Gaussian degradation, uni- and multivariate regression) identified an optimum methodology composed of fuzzy classification, mean degradation, and multivariate regression procedures.

Estimation of forest cover change is important for boreal forests, one of the most extensive forested biomes, due to its unique role in global timber stock, carbon sequestration and deposition, and high vulnerability to the effects of global climate change. We used time-series data from the MODerate Resolution Imaging Spectroradiometer (MODIS) to produce annual forest cover loss hotspot maps. These maps were used to assign all blocks (18.5 by 18.5 km) partitioning the boreal biome into strata of high, medium and low likelihood of forest cover loss. A stratified random sample of 118 blocks was interpreted for forest cover and forest cover loss using high spatial resolution Landsat imagery from 2000 and 2005. Area of forest cover gross loss from 2000 to 2005 within the boreal biome is estimated to be 1.63% (standard error 0.10%) of the total biome area, and represents a 4.02% reduction in year 2000 forest cover. The proportion of identified forest cover loss relative to regional forest area is much higher in North America than in Eurasia (5.63% to 3.00%). Of the total forest cover loss identified, 58.9% is attributable to wildfires. The MODIS pan-boreal change hotspot estimates reveal significant increases in forest cover loss due to wildfires in 2002 and 2003, with 2003 being the peak year of loss within the 5-year study period. Overall, the precision of the aggregate forest cover loss estimates derived from the Landsat data and the value of the MODIS-derived map displaying the spatial and temporal patterns of forest loss demonstrate the efficacy of this protocol for operational, cost-effective, and timely biome-wide monitoring of gross forest cover loss.

Owing to technical limitations the acquisition of fine spatial resolution images (e.g. Landsat data) with frequent (e.g. daily) coverage remains a challenge. One approach is to generate frequent Landsat surface reflectances through blending with coarse spatial resolution images (e.g. Moderate Resolution Imaging Spectroradiometer, MODIS). Existing implementations for data blending, such as the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and Enhanced STARFM (ESTARFM), have their shortcomings, particularly in predicting the surface reflectance characterized by land-cover-type changes. This article proposes a novel blending model, namely the Unmixing-based Spatio-Temporal Reflectance Fusion Model (U-STFM), to estimate the reflectance change trend without reference to the change type, i.e. phenological change (e.g. seasonal change in vegetation) or land-cover change (e.g. conversion of a vegetated area to a built-up area). It is based on homogeneous change regions (HCRs) that are delineated by segmenting the Landsat reflectance difference images. The proposed model was tested on both simulated and actual data sets featuring phenological and land-cover changes. It proved more capable of capturing both types of change compared to STARFM and ESTARFM. The improvement was particularly observed on those areas characterized by land-cover-type changes. This improved fusion algorithm will thereby open new avenues for the application of spatio-temporal reflectance fusion.

融合多源遥感数据生成高时空分辨率数据具有重要的应用价值.对目前常用的3种时空融合方法进行对比分析, 即基于时序数据(STIFM)、基于混合像元分解(STDFM)和增强型自适应遥感图像(ESTATFM)的时空融合方法.以盈科灌区为例, 由多时相的MODIS数据提取地物的时间变化信息, 结合初期的ASTER/TM影像的空间信息, 融合生成30m具有MODIS时间分辨率的数据.以真实的ASTER数据为基准, 从光谱特征和地物类别的角度定量评价结果, 表明STDFM和ESTATFM分别在红波段和近红外波段取得效果最优, 相关系数分别为0.91和0.71, 3种方法融合的NDVI效果基本相当, 相关系数均高于0.84.在地物类别空间信息的表达方面, ESTATFM方法在异质性较强的玉米和小麦区域具有较好的适用性.

Monitoring vegetation dynamics is fundamental for improving Earth system models and for increasing our understanding of the terrestrial carbon cycle and the interactions between biosphere and climate. Medium spatial resolution sensors, like MERIS, exhibit a significant potential to study these dynamics over large areas because of their spatial, spectral and temporal resolution. However, the spatial resolution provided by MERIS (300m in full resolution mode) is not appropriate to monitor heterogeneous landscapes, where typical length scales of these dynamics rarely reach 300m. We, therefore, motivate the use of data fusion techniques to downscale medium spatial resolution data (MERIS full resolution, FR) to a Landsat-like spatial resolution (25m). An unmixing-based data fusion approach was applied to a time series of MERIS FR images acquired over The Netherlands. The selected data fusion approach is based on the linear mixing model and uses a high spatial resolution land use database to produce images having the spectral and temporal resolution as provided by MERIS, but a Landsat-like spatial resolution. A quantitative assessment of the quality of the fused images was done in order to test the validity of the proposed method and to evaluate the radiometric characteristics of the MERIS fused images. The resulting series of fused images was subsequently used to compute two vegetation indices specifically designed for MERIS: the MERIS terrestrial chlorophyll index (MTCI) and the MERIS global vegetation index (MGVI). These indices represent continuous fields of canopy chlorophyll (MTCI) and of the fraction of photosynthetically active radiation absorbed by the canopy (MGVI). Results indicate that the selected data fusion approach can be successfully used to downscale MERIS data and, therefore, to monitor vegetation dynamics at Landsat-like spatial, and MERIS-like spectral and temporal resolution.

遥感数据时空融合技术是一种低空间分辨率影像与中空间分辨率影像在时间域和空间域的融合技术，利用遥感数据时空融合技术获得的融合影像既具备低空间分辨率影像的高时间分辨率特征，又具备中空间分辨率影像的高空间分辨率特征.提出了一种新的遥感数据时空融合方法(STDFA).该方法从时序MODIS数据中提取地物的时间变化信息，结合早期Landsat-TM影像的纹理信息，融合出具有MODIS时间分辨率和TM空间分辨率的影像.以江苏省南京市江宁区为研究区，以Landsat红波段和近红外波段为融合波段，对该方法进行了测试.结果显示，该方法能够产生高精度的中空间分辨率影像，融合影像与真实影像间的相关系数达到0.939.融合影像计算的NDVI与真实中空间分辨率影像计算的NDVI间的相关性达到0.938.

中国科学院机构知识库(中国科学院机构知识库网格（CAS IR GRID）)以发展机构知识能力和知识管理能力为目标，快速实现对本机构知识资产的收集、长期保存、合理传播利用，积极建设对知识内容进行捕获、转化、传播、利用和审计的能力，逐步建设包括知识内容分析、关系分析和能力审计在内的知识服务能力，开展综合知识管理。

蒸渗仪是用来研究营养元素在农田中的运移的系统，本文系统描述了一种大型蒸渗仪的设计，建造与安装。试验采用湖北地区典型土壤类型黄棕壤与潮土，在华中农业大学校园内每种土壤安装16个大型蒸渗仪。蒸渗仪采用减少土柱的扰动的方法建造，并填充凡士林减少土壤水分的边缘流动。蒸渗仪采用外径630 mm高700 mm厚10 mm的PVC管作材料。土柱建成后安装在预制PVC底座上，底座中间有一圆孔外接淋失液收集装置。淋失液收集后用来测定淋失液离子含量。试验结果表明，相同土壤土柱之间淋失量差异不显著，没有检测到水分的边缘流动；同时建设效率高，费用低。

本文利用Wu等人提出的遥感数据时空融合方法 STDFA(Spatial Temporal Data Fusion Approach)以Landsat 8和MODIS为数据源构建高时间、空间分辨率的遥感影像数据。以此为基础,构建15种30 m分辨率分类数据集,然后利用支持向量机SVM(Support Vector Machine)进行秋粮作物识别,验证不同维度分类数据集进行秋粮作物识别的适用性。实验结果显示,不同分类数据集的秋粮作物分类结果均达到了较高的识别精度。综合各项精度指标分析,Red+Phenology数据组合对秋粮识别效果最好,水稻识别的制图精度和用户精度分别达到91.76%和82.49%,玉米识别的制图精度和用户精度分别达到85.80%和74.97%,水稻和玉米识别的总体精度达到86.90%。

The focus of the current study is to compare data fusion methods applied to sensors with medium- and high-spatial resolutions. Two documented methods are applied, the spatial and temporal adaptive reflectance fusion model (STARFM) and an unmixing-based method which proposes a Bayesian formulation to incorporate prior spectral information. Furthermore, the strengths of both algorithms are combined in a novel data fusion method: the Spatial and Temporal Reflectance Unmixing Model (STRUM). The potential of each method is demonstrated using simulation imagery and Landsat and MODIS imagery. The theoretical basis of the algorithms causes STARFM and STRUM to produce Landsat-like reflectances while preserving the spatial patterns found in Landsat images, and the unmixing-based method to produce MODIS-like reflectances. The ability of fused images to capture phenological variations is also assessed using temporal NDVI profiles. Temporal profiles of STARFM NDVI closely resembled Landsat NDVI profiles. However, the unmixing-based method and STRUM produced a more accurate reconstruction of the NDVI trajectory in experiments simulating situations where few input high-resolution images are available. STRUM had the best performance as it produced surface reflectances which had the highest correlations to reference Landsat images. The results of this study indicate that STRUM is more suitable for data fusion applications requiring Landsat-like surface reflectances, such as gap-filling and cloud masking, especially in situations where few high-resolution images are available. Unmixing-based data fusion is recommended in situations which downscale the spectral characteristics of the medium-resolution input imagery and the STARFM method is recommended for constructing temporal profiles in applications containing many input high-resolution images.

中国科学院机构知识库(CAS IR GRID)以发展机构知识能力和知识管理能力为目标，快速实现对本机构知识资产的收集、长期保存、合理传播利用，积极建设对知识内容进行捕获、转化、传播、利用和审计的能力，逐步建设包括知识内容分析、关系分析和能力审计在内的知识服务能力，开展综合知识管理。

A semi-physical fusion approach that uses the MODIS BRDF/Albedo land surface characterization product and Landsat ETM+ data to predict ETM+ reflectance on the same, an antecedent, or subsequent date is presented. The method may be used for ETM+ cloud/cloud shadow and SLC-off gap filling and for relative radiometric normalization. It is demonstrated over three study sites, one in Africa and two in the U.S. (Oregon and Idaho) that were selected to encompass a range of land cover land use types and temporal variations in solar illumination, land cover, land use, and phenology. Specifically, the 30 m ETM+ spectral reflectance is predicted for a desired date as the product of observed ETM+ reflectance and the ratio of the 500 m surface reflectance modeled using the MODIS BRDF spectral model parameters and the sun-sensor geometry on the predicted and observed Landsat dates. The difference between the predicted and observed ETM+ reflectance (prediction residual) is compared with the difference between the ETM+ reflectance observed on the two dates (temporal residual) and with respect to the MODIS BRDF model parameter quality. For all three scenes, and all but the shortest wavelength band, the mean prediction residual is smaller than the mean temporal residual, by up to a factor of three. The accuracy is typically higher at ETM+ pixel locations where the MODIS BRDF model parameters are derived using the best quality inversions. The method is most accurate for the ETM+ near-infrared (NIR) band; mean NIR prediction residuals are 9%, 12% and 14% of the mean NIR scene reflectance of the African, Oregon and Idaho sites respectively. The developed fusion approach may be applied to any high spatial resolution satellite data, does not require any tuning parameters and so may be automated, is applied on a per-pixel basis and is unaffected by the presence of missing or contaminated neighboring Landsat pixels, accommodates for temporal variations due to surface changes (e.g., phenological, land cover/land use variations) observable at the 500 m MODIS BRDF/Albedo product resolution, and allows for future improvements through BRDF model refinement and error assessment.

An operational data fusion framework was built to generate dense time-series Landsat-like images by fusing MODIS data products and Landsat imagery. The spatial and temporal adaptive reflectance fusion model (STARFM) was integrated in the framework. Compared with earlier implementations of the STARFM, several improvements have been incorporated in the operational data fusion framework. These include viewing angular correction on the MODIS daily bidirectional reflectance, precise and automated coregistration on MODIS and Landsat paired images, and automatic selection of Landsat and MODIS paired dates. Three tests that use MODIS and Landsat data pairs from the same season of the same year, the same season of two different years, and different seasons from adjacent years were performed over a Landsat scene in northern India using the integrated STARFM operational framework. The results show that the accuracy of the predicted results depends on the data consistency between the MODIS nadir bidirectional-reflectance-distribution-function-adjusted reflectance and Landsat surface reflectance on both the paired dates and the prediction dates. When MODIS and Landsat reflectances were consistent, the maximum difference of the predicted results for all Landsat spectral bands, except the blue band, was about 0.007 (or 5.1% relatively). However, differences were larger (0.026 in absolute and 13.8% in relative, except the blue band) when two data sources were inconsistent. In an extreme case, the difference for blue-band reflectance was as large as 0.029 (or 39.1% relatively). Case studies focused on monitoring vegetation condition in central India and the Hindu Kush Himalayan region. In general, spatial and temporal landscape variation could be identified with a high level of detail from the fused data. Vegetation index trajectories derived from the fused products could be associated with specific land cover types that occur in the study regions. The operational data fusion framework provides a feasible and cost-effective way to build dense time-series images at Landsat spatial resolution for cloudy regions.

Landsat 5卫星较低的时间分辨率（16天）使得其很难获得大区域的、时相一致的清晰影像数据集。本文发展了一种基于半物理模型的时空融合算法-即乘性调制融合算法，并借助多时序的MODIS反射率数据来生成多时相的Landsat TM/ETM+反射率合成影像，经镶嵌后得到区域尺度的高时空分辨率地表反射率数据集（Landsat TM/ETM+）。本文利用吉林省2006年―2011年的Landsat 5 TM地表反射率数据以及500 m的MOD09A1反射率产品来生成3个时相的Landsat 5 TM反射率合成数据，从而获得研究区在上述时相下地表反射率数据的镶嵌图。初步分析表明，所生成的Landsat 5 TM反射率数据的光谱分布特征与MOD09A1反射率数据较为一致，且图像在整体上光谱特征的连续性较好。

Current satellite sensors provide data of insufficient spatial and temporal resolutions to fully characterize the patchy phenology patterns of dryland forests. The spatial and temporal adaptive reflectance fusion model (STARFM) is an algorithm that fuses Landsat 30 m data with MODIS 500 m data to produce synthetic imagery at Landsat spatial resolution and MODIS time steps. In this study, we evaluated the feasibility of using STARFM to produce synthetic imagery over a dryland vegetation study site for the purpose of tracking phenological changes. We assembled subsets of six Landsat-5 TM scenes and temporally-coincident MODIS datasets spanning the 2006 April ctober growing season in central-northern Arizona, which is characterized by large tracts of dryland forest. To investigate the effects of temporal compositing, BRDF-adjustment, and base pair timing on the accuracy of the resulting synthetic imagery, we employed a range of MODIS 500 m surface reflectance datasets (daily, 8-day composite, and 16-day Nadir BRDF-Adjusted Reflectance (NBAR)) as well as initial Landsat/MODIS imagery pairs from opposite ends of the growing season. The STARFM algorithm was applied to each MODIS data series to produce up to twelve synthetic images corresponding to each Landsat image. We evaluated the accuracy of the synthetic images by comparing the reflectance values of a random sample of the vegetation pixels with the corresponding pixel values of the reference Landsat image on a band-by-band basis. Our results indicate that the NBAR imagery is the optimal dataset for use with Landsat-5 TM data in this area. The NBAR dataset consistently returned the lowest absolute difference values and the highest correlations. A comparison of landscape-scale maps of the timing and value of the peak NDVI derived from STARFM, Landsat, and MODIS (NBAR) time series across the full 2006 growing season shows the effect of the heightened spatial and temporal resolution offered by a STARFM-based dataset. This work demonstrates the feasibility of using the STARFM algorithm to assemble an imagery time series at Landsat spatial resolution and MODIS temporal resolution in vegetated dryland ecosystems.

Although remotely sensed data have been used in public health studies, these studies are often limited by the critical choice that has to be made in data selection: either using data with high spatial but low temporal resolution, or data with high temporal but low spatial resolution. This choice creates significant limitations for time-dependent epidemiological studies, since it is often essential to have datasets with high spatial and temporal resolution. Effectively synthesizing high temporal resolution imagery with high spatial resolution imagery can potentially ease this limitation. To this end, we conducted an experiment by creating a series of simulated ASTER datasets by fusing ASTER and MODIS data with the STARFM image fusion model. These simulated datasets are then used to derive the following urban environmental variables: normalized difference vegetation index, normalized difference water index, and land surface temperature. These variables are used to quantitatively examine the effects of urban environmental characteristics on West Nile Virus dissemination in Los Angeles County, California, where the epidemic was most prevalent in the United States in 2007. Mosquito surveillance data were collected from the weekly summary reports published in the California West Nile Virus website. A spatial–temporal analysis of WNV dissemination was conducted by synthesizing remote sensing variables and mosquito surveillance records. We focused on assessment of WNV risk areas in July through September due to data sufficiency of tested-positive mosquito pools. Moderate- and high-risk areas of WNV infections in mosquitoes were identified for five selected time windows, i.e., epidemiological weeks 30–31, 32–33, 34–35, 36–37, and 38–39. The results show that elevation and urban built-up conditions were negatively associated with the WNV propagation, while LST positively correlated with viral transmission. NDVI was not significantly associated with WNV transmission during the studied time intervals. San Fernando Valley was found to be the most vulnerable to mosquito infections of WNV within the City of Los Angeles. With the complementation of high-spatial resolution ASTER and high-temporal resolution MODIS images, the fused image datasets allow for estimating environmental parameters at desired epidemiological weeks. This paper provides important insights into how high temporal resolution remote sensing imagery might be used to study time-dependant events in public health, especially in the operational surveillance and control of vector-borne or other epidemic diseases.

Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) has been used for the blending of Landsat and MODIS data. Specifically, the 3002m Landsat-7 ETM+ (Enhanced Thematic Mapper plus) surface reflectance was predicted for a period of 10 years (2000–2009) as the product of observed ETM+ and MODIS surface reflectance (MOD09A1) on the predicted and observed ETM+ dates. A pixel based analysis for six observed ETM+ dates covering winter and summer crops showed that the prediction method was more accurate for NIR band (mean r 2 02=020.71, p 02≤020.01) compared to green band (mean r 2 02=020.53; p 02≤020.01). A recently proposed chlorophyll index (CI), which involves NIR and green spectral bands, was used to retrieve gross primary productivity (GPP) as the product of CI and photosynthetic active radiation (PAR). The regression analysis of GPP derived from closet observed and synthetic ETM+ showed a good agreement ( r 2 02=020.85, p 02≤020.01 and r 2 02=020.86, p 02≤020.01) for wheat and sugarcane crops, respectively. The difference between the GPP derived from synthetic and observed ETM+ (prediction residual) was compared with the difference in GPP values from observed ETM+ on the two dates (temporal residual). The prediction residuals (mean value of 1.9702g02C/m 2 in 8 days) was found to be significantly lower than the temporal residuals (mean value of 4.4602g02C/m 2 in 8 days) that correspondence to 12% and 27%, respectively, of GPP values (mean value of 16.5302g02C/m 2 in 8 days) from observed ETM+ data, implying that the prediction method was better than temporal pixel substitution. Investigating the trend in synthetic ETM+ GPP values over a growing season revealed that phenological patterns were well captured for wheat and sugarcane crops. A direct comparison between the GPP values derived from MODIS and synthetic ETM+ data showed a good consistency of the temporal dynamics but a systematic error that can be read as bias (MODIS GPP over estimation). Further, the regression analysis between observed evapotranspiration and synthetic ETM+ GPP showed good agreement ( r 2 02=020.66, p 02≤020.01).

为实现草地生物量的实时高精度监测,将时空适应性反射率融合模型(spatial and temporal adaptive reflectance fusion model,STARFM)融合后的数据引入到草地生物量估算模型中,以提高该模型的精度。以内蒙古锡林浩特市为研究区,首先采用STARFM融合MODIS和Landsat TM数据,同时对比分析反射率和NDVI输入数据的融合效果,认为直接融合NDVI数据得到的高分辨率NDVI产品的精度更高; 然后,基于融合后高精度的NDVI数据与实测生物量建立多种生物量估算模型; 通过统计比较得到最优生物量估算模型——指数模型; 最后,基于融合后NDVI与原始MODIS NDVI数据分别作为自变量建立指数模型,以验证融合数据提高生物量估算模型精度的能力。研究表明,基于融合后NDVI的生物量估算模型决定系数<em>R</em>²由0.761提高到0.832, 均方根误差由32.521 g/m²降低到28.653 g/m²,证明融合NDVI数据提高了生物量估算模型的精度。

<p>由于受到16 d重访周期与云等对数据质量的影响,具有时间与空间连续性的Landsat 8 OLI观测数据难以直接获取。考虑地物分布的空间自相关性,提出一种基于STARFM模型改进的局部自相关时空数据融合模型(LASTARFM),以新疆维吾尔族自治区喀什地区叶城县为研究区,利用Landsat 8 OLI数据和MODIS数据的红光波段和近红外波段进行融合方法测试。结果表明:利用LASTARFM模型得到的融合影像,与真实影像NDVI相关系数达到0.92;在局部空间自相关性低的区域比STARFM模型影像反映出更多地物细节,具有更高的融合精度;在土地利用类型发生显著变化的区域与真实影像具有一定差异。</p>

This article proposes a spatialtemporal expansion method for remote-sensing reflectance by blending observations from sensors with different spatial and temporal characteristics. Compared with the methods used in the past, the main characteristic of the proposed method is consideration of sensor observation differences between different cover types when calculating the weight function of the fusion model. The necessity of the temporal difference factor commonly used in spatialtemporal fusion is also analysed in this article. The method was tested and quantitatively assessed under different landscape situations. The results indicate that the proposed fusion method improves the prediction accuracy of fine-resolution reflectance.

Land surface temperature (LST) is a crucial parameter in investigating environmental, ecological processes and climate change at various scales, and is also valuable in the studies of evapotranspiration, soil moisture conditions, surface energy balance, and urban heat islands. These studies require thermal infrared (TIR) images at both high temporal and spatial resolution to retrieve LST. However, currently, no single satellite sensors can deliver TIR data at both high temporal and spatial resolution. Thus, various algorithms/models have been developed to enhance the spatial or the temporal resolution of TIR data, but rare of those can enhance both spatial and temporal details. This paper presents a new data fusion algorithm for producing Landsat-like LST data by blending daily MODIS and periodic Landsat TM datasets. The original Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) was improved and modified for predicting thermal radiance and LST data by considering annual temperature cycle (ATC) and urban thermal landscape heterogeneity. The technique of linear spectral mixture analysis was employed to relate the Landsat radiance with the MODIS one, so that the temporal changes in radiance can be incorporated in the fusion model. This paper details the theoretical basis and the implementation procedures of the proposed data fusion algorithm, Spatio-temporal Adaptive Data Fusion Algorithm for Temperature mapping (SADFAT). A case study was conducted that predicted LSTs of five dates in 2005 from July to October in Los Angeles County, California. The results indicate that the prediction accuracy for the whole study area ranged from 1.3 K to 2 K. Like existing spatio-temporal data fusion models, the SADFAT method has a limitation in predicting LST changes that were not recorded in the MODIS and/or Landsat pixels due to the model assumption.

With the recent launch of new satellites and the developments of spatiotemporal data fusion methods, we are entering an era of high spatiotemporal resolution remote-sensing analysis. This study proposed a method to reconstruct daily 30 m remote-sensing data for monitoring crop types and phenology in two study areas located in Xinjiang Province, China. First, the Spatial and Temporal Data Fusion Approach (STDFA) was used to reconstruct the time series high spatiotemporal resolution data from the Huanjing satellite charge coupled device (HJ CCD), Gaofen satellite no. 1 wide field-of-view camera (GF-1 WFV), Landsat, and Moderate Resolution Imaging Spectroradiometer (MODIS) data. Then, the reconstructed time series were applied to extract crop phenology using a Hybrid Piecewise Logistic Model (HPLM). In addition, the onset date of greenness increase (OGI) and greenness decrease (OGD) were also calculated using the simulated phenology. Finally, crop types were mapped using the phenology information. The results show that the reconstructed high spatiotemporal data had a high quality with a proportion of good observations (PGQ) higher than 0.95 and the HPLM approach can simulate time series Normalized Different Vegetation Index (NDVI) very well with R ranging from 0.635 to 0.952 in Luntai and 0.719 to 0.991 in Bole, respectively. The reconstructed high spatiotemporal data were able to extract crop phenology in single crop fields, which provided a very detailed pattern relative to that from time series MODIS data. Moreover, the crop types can be classified using the reconstructed time series high spatiotemporal data with overall accuracy equal to 0.91 in Luntai and 0.95 in Bole, which is 0.028 and 0.046 higher than those obtained by using multi-temporal Landsat NDVI data.

To understand the mechanism of wetland cover change with both moderate spatial resolution and high temporal frequency, this research evaluates the applicability of a spatiotemporal reflectance blending model in the Poyang Lake area, China, using 9 time-series Landsat-5 Thematic Mapper images and 18 time-series Terra Moderate Resolution Imaging Spectroradiometer images acquired between July 2004 and November 2005. The customized blending model was developed based on the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). Reflectance of the moderate-resolution image pixels on the target dates can be predicted more accurately by the proposed customized model than the original ESTARFM. Water level on the input image acquisition dates strongly affected the accuracy of the blended reflectance. It was found that either of the image sets used as prior or posterior inputs are required when the difference of water level between the prior or posterior date and target date at Poyang Hydrological Station is <2.68 m to achieve blending accuracy with a mean average absolute difference of 4% between the observed and blended reflectance in all spectral bands.

Satellite images provide important data sources for monitoring flood disasters. However, the trade-off between spatial and temporal resolutions of current satellite sensors limits their uses in urban flooding studies. This study applied and compared two data fusion models, the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), in generating synthetic flooding images with improved temporal and spatial resolution for flood mapping. The synthetic images are produced in two scenarios: (1) for real-time prediction based on Landsat and MODIS images acquired before the investigated flooding; and (2) for post-disaster prediction based on images acquired after the flooding. The 2005 Hurricane Katrina in New Orleans was selected as a case study. The result shows that the Landsat-like images generated can be successfully applied in flood mapping. Particularly, ESTARFM surpasses STARFM in predicting surface reflectance in both real-time and post-flooding predictions. However, the flood mapping results from the Landsat-like images produced by both STARFM and ESTARFM are similar with overall accuracy around 0.9. Only for the flooding maps of real-time predictions does ESTARFM get a slightly higher overall accuracy than STARFM, indicating that the lower quality of the Landsat-like image generated by STARFM may not affect flood mapping accuracy, due to the marked contrast between land and water. This study suggests great potential of both STARFM and ESTARFM in urban flooding research. Blending multi-sources images could also support other disaster studies that require remotely sensed data with both high spatial and temporal resolution.

61Landsat and MODIS data fusion via STARFM can reveal dryland phenology complexity.61Land cover characteristics influence the accuracy of STARFM imagery.61Fused 30m imagery captures phenology variations obscured within 500m MODIS pixels.61Synchronicity of peak EVI and NDVI greenness dates varies by land cover class.

Land surface temperature (LST) and its diurnal variation are important when evaluating climate change, the land鈥揳tmosphere energy budget, and the global hydrological cycle. However, the available satellite LST products have either a coarse spatial resolution or a low temporal resolution, which constrains their potential applications. This paper proposes a spatio-temporal integrated temperature fusion model (STITFM) for the retrieval of high temporal and spatial resolution LST from multi-scale polar-orbiting and geostationary satellite observations. Compared with the traditional fusion methods for LST with two different sensors, the proposed method is able to fuse the LST from arbitrary sensors in a unified framework. The model was tested using LST with fine, moderate, and coarse-resolutions. Data from the Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM +) sensors, the Moderate Resolution Imaging Spectroradiometer (MODIS), the Geostationary Operational Environmental Satellite (GOES) Imager, and the Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) were used. The fused LST values were evaluated with in situ LST obtained from the Surface Radiation Budget Network (SURFRAD) and the Land Surface Analysis Satellite Application Facility (LSA SAF) project. The final validation results indicate that the STITFM is accurate to within about 2.5 K.

Accurate monitoring of grassland biomass at high spatial and temporal resolutions is important for the effective utilization of grasslands in ecological and agricultural applications. However, current remote sensing data cannot simultaneously provide accurate monitoring of vegetation changes with fine temporal and spatial resolutions. We used a data-fusion approach, namely the spatial and temporal adaptive reflectance fusion model (STARFM), to generate synthetic normalized difference vegetation index (NDVI) data from Moderate-Resolution Imaging Spectroradiometer (MODIS) and Landsat data sets. This provided observations at fine temporal (8-d) and medium spatial (30 m) resolutions. Based on field-sampled aboveground biomass (AGB), synthetic NDVI and support vector machine (SVM) techniques were integrated to develop an AGB estimation model (SVM-AGB) for Xilinhot in Inner Mongolia, China. Compared with model generated from MODIS-NDVI (R2 = 0.73, root-mean-square error (RMSE) = 30.61 g/m2), the SVM-AGB model we developed can not only ensure the accuracy of estimation (R2 = 0.77, RMSE = 17.22 g/m2), but also produce higher spatial (30 m) and temporal resolution (8-d) biomass maps. We then generated the time-series biomass to detect biomass anomalies for grassland regions. We found that the synthetic NDVI-derived estimations contained more details on the distribution and severity of vegetation anomalies compared with MODIS NDVI-derived AGB estimations. This is the first time that we have generated time series of grassland biomass with 30-m and 8-d intervals data through combined use of a data-fusion method and the SVM-AGB model. Our study will be useful for near real-time and accurate (improved resolutions) monitoring of grassland conditions, and the data have implications for arid and semi-arid grasslands management.

A sufficient number of satellite acquisitions in a growing season are essential for deriving agronomic indicators, such as green leaf area index (GLAI), to be assimilated into crop models for crop productivity estimation. However, for most high resolution orbital optical satellites, it is often difficult to obtain images frequently due to their long revisit cycles and unfavorable weather conditions. Data fusion algorithms, such as the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and the Enhanced STARFM (ESTARFM), have been developed to generate synthetic data with high spatial and temporal resolution to address this issue. In this study, we evaluated the approach of assimilating GLAI into the Simple Algorithm for Yield Estimation model (SAFY) for winter wheat biomass estimation. GLAI was estimated using the two-band Enhanced Vegetation Index (EVI2) derived from data acquired by the Operational Land Imager (OLI) onboard the Landsat-8 and a fusion dataset generated by blending the Moderate-Resolution Imaging Spectroradiometer (MODIS) data and the OLI data using the STARFM and ESTARFM models. The fusion dataset had the temporal resolution of the MODIS data and the spatial resolution of the OLI data. Key parameters of the SAFY model were optimised through assimilation of the estimated GLAI into the crop model using the Shuffled Complex Evolution-University of Arizona (SCE-UA) algorithm. A good agreement was achieved between the estimated and field measured biomass by assimilating the GLAI derived from the OLI data (GLAIL) alone (R2=0.77 and RMSE=231gm 2). Assimilation of GLAI derived from the fusion dataset (GLAIF) resulted in aR2of 0.71 and RMSE of 193gm 2while assimilating the combination of GLAILand GLAIFled to further improvements (R2=0.76 and RMSE=176gm鈭2). Our results demonstrated the potential of using the fusion algorithms to improve crop growth monitoring and crop productivity estimation when the number of high resolution remote sensing data acquisitions is limited.

A novel spatiotemporal reflectance fusion method integrating image inpainting and steering kernel regression fusion model (ISKRFM) is proposed to improve the fusion accuracy for remote-sensing images with different temporal and spatial characteristics in this article. This method first detects the land-cover changed regions and then fills them with unchanged similar pixels by an exemplar-based inpainting technique. Furthermore, a steering kernel regression (SKR) is used to adaptively determine the weightings of local neighbouring pixels to predict high spatial resolution image. Accordingly, the main contributions of this method are twofold. One is to address the land-cover change issues in the spatiotemporal fusion, and the other is to establish an adaptive weighting assignment according to the pixel locations and the radiometric properties of the local neighbours to account for the effect of neighbouring pixels. To validate the proposed method, two actual Enhanced Thematic Mapper Plus (ETM+) and Moderate Resolution Imaging Spectroradiometer (MODIS) acquisitions at southeast China were implemented and compared with the baseline spatial and temporal adaptive reflectance fusion model (STARFM). The experimental results demonstrate that addressing the land-cover changes in spatiotemporal fusion has positive effects on the fused image, and the proposed ISKRFM method significantly outperforms STARFM in terms of both visual and quantitative measurements.

61FSDAF is developed to fuse multi-sensor images, e.g., Landsat and MODIS.61FSDAF needs minimum input data and is suitable for heterogeneous landscapes.61FSDAF can predict both phenological change and land cover change.61FSDAF can get more accurate fused images than existing methods.61FSDAF can support studies of rapid land surface dynamics.

This paper presents a new approach to single-image superresolution, based upon sparse signal representation. Research on image statistics suggests that image patches can be well-represented as a sparse linear combination of elements from an appropriately chosen over-complete dictionary. Inspired by this observation, we seek a sparse representation for each patch of the low-resolution input, and then use the coefficients of this representation to generate the high-resolution output. Theoretical results from compressed sensing suggest that under mild conditions, the sparse representation can be correctly recovered from the downsampled signals. By jointly training two dictionaries for the low- and high-resolution image patches, we can enforce the similarity of sparse representations between the low-resolution and high-resolution image patch pair with respect to their own dictionaries. Therefore, the sparse representation of a low-resolution image patch can be applied with the high-resolution image patch dictionary to generate a high-resolution image patch. The learned dictionary pair is a more compact representation of the patch pairs, compared to previous approaches, which simply sample a large amount of image patch pairs , reducing the computational cost substantially. The effectiveness of such a sparsity prior is demonstrated for both general image super-resolution (SR) and the special case of face hallucination. In both cases, our algorithm generates high-resolution images that are competitive or even superior in quality to images produced by other similar SR methods. In addition, the local sparse modeling of our approach is naturally robust to noise, and therefore the proposed algorithm can handle SR with noisy inputs in a more unified framework.

This paper presents a novel model for blending remote sensing data of high spatial resolution (HSR), taken at infrequent intervals, with those available frequently but at low spatial resolution (LSR) in the context of monitoring and predicting changes in land usage and phenology. Named Parse-representation-based SpatioTemporal reflectance Fusion Model (SPSTFM), the model has been developed for predicting HSR surface reflectances through data blending with LSR scenes. Remarkably, this model forms a unified framework for fusing remote sensing images with temporal reflectance changes, phenology change (e.g., seasonal change of vegetation), or type change (e.g., conversion of farmland to built-up area), by establishing correspondences between structures within HSR images of given areas and their corresponding LSR images. Such corresponding relationship is achieved by means of the sparse representation, specifically by jointly training two dictionaries generated from HSR and LSR difference image patches and sparse coding at the reconstruction stage. SPSTFM was tested using both a simulated data set and an actual data set of Landsat Enhanced Thematic Mapper Plus-Moderate Resolution Imaging Spectroradiometer acquisitions. It was also compared with other related algorithms on two types of data: images primarily with phenology change and images primarily with land-cover type change. Experimental results demonstrate the superiority of SPSTFM in capturing surface reflectance changes on both categories of images.

This paper proposes a novel spatiotemporal fusion model for generating images with high-spatial and high-temporal resolution (HSHT) through learning with only one pair of prior images. For this purpose, this method establishes correspondence between low-spatial-resolution but high-temporal-resolution (LSHT) data and high-spatial-resolution but low-temporal-resolution (HSLT) data through the superresolution of LSHT data and further fusion by using high-pass modulation. Specifically, this method is implemented in two stages. In the first stage, the spatial resolution of LSHT data on prior and prediction dates is improved simultaneously by means of sparse representation; in the second stage, the known HSLT and the superresolved LSHTs are fused via high-pass modulation to generate the HSHT data on the prediction date. Remarkably, this method forms a unified framework for blending remote sensing images with temporal reflectance changes, whether phenology change (e.g., seasonal change of vegetation) or land-cover-type change (e.g., conversion of farmland to built-up area) based on a two-layer spatiotemporal fusion strategy due to the large spatial resolution difference between HSLT and LSHT data. This method was tested on both a simulated data set and two actual data sets of Landsat Enhanced Thematic Mapper Plus-Moderate Resolution Imaging Spectroradiometer acquisitions. It was also compared with other well-known spatiotemporal fusion algorithms on two types of data: images primarily with phenology changes and images primarily with land-cover-type changes. Experimental results demonstrated that our method performed better in capturing surface reflectance changes on both types of images.

In order for quantitative applications to make full use of the ever-increasing number of Earth observation satellite systems, data from the various imaging sensors involved must be on a consistent radiometric scale. This paper reports on an investigation of radiometric calibration errors due to differences in spectral response functions between satellite sensors when attempting cross-calibration based on near-simultaneous imaging of common ground targets in analogous spectral bands, a commonly used post-launch calibration methodology. Twenty Earth observation imaging sensors (including coarser and higher spatial resolution sensors) were considered, using the Landsat solar reflective spectral domain as a framework. Scene content was simulated using spectra for four ground target types (Railroad Valley Playa, snow, sand and rangeland), together with various combinations of atmospheric states and illumination geometries. Results were obtained as a function of ground target type, satellite sensor comparison, spectral region, and scene content. Overall, if spectral band difference effects (SBDEs) are not taken into account, the Railroad Valley Playa site is a “good” ground target for cross calibration between most but not all satellite sensors in most but not all spectral regions investigated. “Good” is defined as SBDEs within ± 3%. The other three ground target types considered (snow, sand and rangeland) proved to be more sensitive to uncorrected SBDEs than the RVPN site overall. The spectral characteristics of the scene content (solar irradiance, surface reflectance and atmosphere) are examined in detail to clarify why spectral difference effects arise and why they can be significant when comparing different imaging sensor systems. Atmospheric gas absorption features are identified as being the main source of spectral variability in most spectral regions. The paper concludes with recommendations on spectral data and tools that would facilitate cross-calibration between multiple satellite sensors.

多时相遥感影像的辐射归一化是进行变化检测或拼接不可缺少的步骤。本文从绝对辐射归一化和相对辐射归一化两个方面对现有的多时相遥感影像辐射归一化方法进行了总结。对各种辐射归一化方法进行了剖析,并指出了这些方法的优缺点和适应性。最后对多时相遥感影像辐射归一化方法未来的发展进行了展望。

Remotely sensed multitemporal, multisensor data are often required in Earth observation applications. A common problem associated with the use of multisource image data is the grey value differences caused by non鈥恠urface factors such as different illumination, atmospheric, or sensor conditions. Such differences make it difficult to compare images using the same colour metric system. Image normalization is required to reduce the radiometric influences caused by non鈥恠urface factors and to ensure that the grey value differences between temporal images reflect actual changes on the surface of the Earth.A variety of image normalization methods, such as pseudoinvariant features (PIF), dark and bright set (DB), simple regression (SR), no hange set determined from scattergrams (NC), and histogram matching (HM), have been published in scientific journals. These methods have been tested with either Landsat TM data, MSS data or both, and test results differ from author to author. However, whether or not existing methods could be adopted for normalizing high resolution multispectral satellite images, such as IKONOS and QuickBird, is still open for discussion because of the dramatic change in spatial resolution and the difference of available multispectral bands. In this research, the existing methods are introduced and employed to normalize the radiometric difference between IKONOS and QuickBird multispectral images taken in different years. Some improvements are introduced to the existing methods to overcome problems caused by band difference and to achieve more stable and better results. The normalized results are compared in terms of visual inspection and statistical analysis. This paper examined whether or not existing methods can be directly adopted for image normalization with high resolution satellite images, and showed how these methods can be modified for use with such images.

<p>长时间序列遥感数据集被广泛应用于全球或区域环境变化、植被动态变化、土地覆盖变化和植物生物物理参数反演等研究,受云、气溶胶、太阳高度角和地物二向反射等因素的影响,这些数据集本身存在很多噪声,限制了数据的深入分析和应用,因此,数据重建是应用前的重要工作。在总结归纳现有时间序列重建方法的基础上,综述了重建方法比较研究的成果,深入比较了常用重建方法对参数的设置要求,分析了目前对重建效果的评价方法以及存在的问题,同时对时间序列数据重建通用软件及其发展作了详细介绍。最后提出了时间序列重建以后的研究重点和发展方向,以期为长时间数据重建研究和应用提供帮助。</p>

The relationship between AVHRR-derived normalized difference vegetation index (NDVI) values and those of future sensors is critical to continued long-term monitoring of land surface properties. The follow-on operational sensor to the AVHRR, the Visible/Infrared Imager/Radiometer Suite (VIIRS), will be very similar to the NASA Earth Observing System's Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. NDVI data derived from visible and near-infrared data acquired by the MODIS (Terra and Aqua platforms) and AVHRR (NOAA-16 and NOAA-17) sensors were compared over the same time periods and a variety of land cover classes within the conterminous United States. The results indicate that the 16-day composite NDVI values are quite similar over the composite intervals of 2002 and 2003, and linear relationships exist between the NDVI values from the various sensors. The composite AVHRR NDVI data included water and cloud masks and adjustments for water vapor as did the MODIS NDVI data. When analyzed over a variety of land cover types and composite intervals, the AVHRR derived NDVI data were associated with 89% or more of the variation in the MODIS NDVI values. The results suggest that it may be possible to successfully reprocess historical AVHRR data sets to provide continuity of NDVI products through future sensor systems.

Consistent NDVI time series are paramount in monitoring ecological resources that are being altered by climate and human impacts. An increasing number of natural resource managers use web-based geospatial decision support tools that integrate time series of both historical and current NDVI data derived from multiple sensors to make better informed planning and management decisions. Representative canopy reflectance and NDVI data were simulated for historical, current and future AVHRR, MODIS and VIIRS land surface monitoring satellites to quantify the differences due to sensor-specific characteristics. Cross-sensor NDVI translation equations were developed for surface conditions. The effect of a range of atmospheric conditions (Rayleigh scattering, ozone, aerosol optical thickness, and water vapor content) on the sensor-specific reflectance and NDVI values were evaluated to quantify the uncertainty in the apparent NDVI for each sensor. MODIS and VIIRS NDVI data are minimally affected by the atmospheric water vapor, while AVHRR NDVI data are substantially reduced by water vapor. Although multi-sensor NDVI continuity can be obtained by using the developed cross-sensor translation equations, the interactions between the spectral characteristics of surface vegetation and soil components, sensor-specific spectral band characteristics and atmospheric scattering and absorption windows will introduce uncertainty due to insufficient knowledge about the atmospheric conditions that affect the signal of the Earth's pixels at the time of data acquisitions. Processing strategies and algorithm preferences among data streams are also hindering cross-sensor NDVI continuity.

In this paper, we developed a new geospatial database of paddy rice agriculture for 13 countries in South and Southeast Asia. These countries have 30% of the world population and 2/3 of the total rice land area in the world. We used 8-day composite images (500-m spatial resolution) in 2002 from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the NASA EOS Terra satellite. Paddy rice fields are characterized by an initial period of flooding and transplanting, during which period a mixture of surface water and rice seedlings exists. We applied a paddy rice mapping algorithm that uses a time series of MODIS-derived vegetation indices to identify the initial period of flooding and transplanting in paddy rice fields, based on the increased surface moisture. The resultant MODIS-derived paddy rice map was compared to national agricultural statistical data at national and subnational levels. Area estimates of paddy rice were highly correlated at the national level and positively correlated at the subnational levels, although the agreement at the national level was much stronger. Discrepancies in rice area between the MODIS-derived and statistical datasets in some countries can be largely attributed to: (1) the statistical dataset is a sown area estimate (includes multiple cropping practices); (2) failure of the 500-m resolution MODIS-based algorithm in identifying small patches of paddy rice fields, primarily in areas where topography restricts field sizes; and (3) contamination by cloud. While further testing is needed, these results demonstrate the potential of the MODIS-based algorithm to generate updated datasets of paddy rice agriculture on a timely basis. The resultant geospatial database on the area and spatial distribution of paddy rice is useful for irrigation, food security, and trace gas emission estimates in those countries.

MODIS 250-m NDVI and EVI datasets are now regularly used to classify regional-scale agricultural land-use practices in many different regions of the globe, especially in the state of Mato Grosso, Brazil, where rapid land-use change due to agricultural development has attracted considerable interest from researchers and policy makers. Variation exists in which MODIS datasets are used, how they are processed for analysis, and what ground reference data are used. Moreover, various land-use/land-cover classes are ultimately resolved, and as yet, crop-specific classifications (e.g. soy-corn vs. soy-cotton double crop) have not been reported in the literature, favoring instead generalized classes such as single vs. double crop. The objective of this study is to present a rigorous multiyear evaluation of the applicability of time-series MODIS 250-m VI data for crop classification in Mato Grosso, Brazil. This study shows progress toward more refined crop-specific classification, but some grouping of crop classes remains necessary. It employs a farm field polygon-based ground reference dataset that is unprecedented in spatial and temporal coverage for the state, consisting of 2003 annual field site samples representing 415 unique field sites and five crop years (2005-2009). This allows for creation of a dataset containing "best-case" or "pure" pixels, which we used to test class separability in a multiyear cross validation framework applied to boosted decision tree classifiers trained on MODIS data subjected to different pre-processing treatments. Reflecting the agricultural landscape of Mato Grosso as a whole, cropping practices represented in the ground reference dataset largely involved soybeans, and soy-based classes (primarily double crop 'soy-commercial' and single crop 'soy-cover') dominated the analysis along with cotton and pasture. With respect to the MODIS data treatments, the best results were obtained using date-of-acquisition interpolation of the 16-day composite VI time series and outlier point screening, for which five-year out-of-sample accuracies were consistently near or above 80% and Kappa values were above 0.60. It is evident that while much additional research is required to fully and reliably differentiate more specific crop classes, particular groupings of cropping strategies are separable and useful for a number of applications, including studies of agricultural intensification and extensification in this region of the world. (C) 2012 Elsevier Inc. All rights reserved.

; ; ; ; USDA, water conservation lab., Phoenix, ETATS-UNISCEMAGREF-ENGREF, remote sensing lab., Montpellier, FRANCE02 0202 02 0202RSEEA702 CongrèsRemote sensing of soils and vegetation. Symposium, Tempe AZ , ETATS-UNIS (1993) 1994,02vol.0249,02n02302(1 p.1/2),02pp.02246-263AnglaisElsevier, New York, NY, ETATS-UNIS02 (1969) (Revue); ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; INIST-CNRS, Cote INIST : 14287,0235400004199403.0090

农业是遥感技术应用最重要和广泛的领域之一,本文回顾了遥感技术在国内外农业研究与应用中的进展,概括和总结了农田辐射传输机理及作物参量遥感反演、作物遥感分类与识别、农田养分遥感与变量施肥决策、作物产量与品质预测、农情遥感监测与预报、农业遥感监测空间决策支持系统6个主要研究与应用方面。在此基础上,针对农业遥感技术面临的问题与发展趋势,指出了农业遥感技术今后的重点发展方向。

Recent studies have demonstrated the usefulness of optical indices from hyperspectral remote sensing in the assessment of vegetation biophysical variables both in forestry and agriculture. Those indices are, however, the combined response to variations of several vegetation and environmental properties, such as Leaf Area Index (LAI), leaf chlorophyll content, canopy shadows, and background soil reflectance. Of particular significance to precision agriculture is chlorophyll content, an indicator of photosynthesis activity, which is related to the nitrogen concentration in green vegetation and serves as a measure of the crop response to nitrogen application. This paper presents a combined modeling and indices-based approach to predicting the crop chlorophyll content from remote sensing data while minimizing LAI (vegetation parameter) influence and underlying soil (background) effects. This combined method has been developed first using simulated data and followed by evaluation in terms of quantitative predictive capability using real hyperspectral airborne data. Simulations consisted of leaf and canopy reflectance modeling with PROSPECT and SAILH radiative transfer models. In this modeling study, we developed an index that integrates advantages of indices minimizing soil background effects and indices that are sensitive to chlorophyll concentration. Simulated data have shown that the proposed index Transformed Chlorophyll Absorption in Reflectance Index/Optimized Soil-Adjusted Vegetation Index (TCARI/OSAVI) is both very sensitive to chlorophyll content variations and very resistant to the variations of LAI and solar zenith angle. It was therefore possible to generate a predictive equation to estimate leaf chlorophyll content from the combined optical index derived from above-canopy reflectance. This relationship was evaluated by application to hyperspectral CASI imagery collected over corn crops in three experimental farms from Ontario and Quebec, Canada. The results presented here are from the L'Acadie, Quebec, Agriculture and Agri-Food Canada research site. Images of predicted leaf chlorophyll content were generated. Evaluation showed chlorophyll variability over crop plots with various levels of nitrogen, and revealed an excellent agreement with ground truth, with a correlation of r 2=.81 between estimated and field measured chlorophyll content data.

Since optical and microwave sensors respond to very different target characteristics, their role in crop monitoring can be viewed as complementary. In particular, the all‐weather capability of Synthetic Aperture Radar (SAR) sensors can ensure that data gaps that often exist during monitoring with optical sensors are filled. There were three Landsat Thematic Mapper (TM) satellite images and three Envisat Advanced Synthetic Aperture Radar (ASAR) satellite images acquired from reviving stage to milking stage of winter wheat. These data were successfully used to monitor crop condition and forecast grain yield and protein content. Results from this study indicated that both multi‐temporal Envisat ASAR and Landsat TM imagery could provide accurate information about crop conditions. First, bivariate correlation results based on the linear regression of crop variables against backscatter suggested that the sensitivity of ASAR C‐HH backscatter image to crop or soil condition variation depends on growth stage and time of image acquisition. At the reviving stage, crop variables, such as biomass, Leaf Area Index (LAI) and plant water content (PWC), were significantly positively correlated with C‐HH backscatter (r02=020.65, 0.67 and 0.70, respectively), and soil water content at 502cm, 1002cm and 2002cm depths were correlated significantly with C‐VV backscatter (r02=020.44, 0.49 and 0.46, respectively). At booting stage, only a significant and negative correlation was observed between biomass and C‐HH backscatter (r02=02610.44), and a saturation of the SAR signal to canopy LAI could explain the poor correlation between crop variables and C‐HH backscatter. Furthermore, C‐HH backscatter was correlated significantly with soil water content at booting and milking stage. Compared with ASAR backscatter data, the multi‐spectral Landsat TM images were more sensitive to crop variables. Secondly, a significant and negative correlation between grain yield and ASAR C‐HH & C‐VV backscatter at winter wheat booting stage was observed (r02=02610.73 and 610.55, respectively) and a yield prediction model with a correlation coefficient of 0.91 was built based on the Normalized Difference Water Index (NDWI) data from Landsat TM on 17 April and ASAR C‐HH backscatter on 27 April. Finally, grain protein content was found to be correlated significantly with ASAR C‐HH backscatter at milking stage (r02=02610.61) and with Structure Insensitive Pigment Index (SIPI) data from Landsat TM at grain‐filling stage (r02=020.53), and a grain protein content prediction model with a correlation coefficient of 0.75 was built based on the C‐HH backscatter and SIPI data.

The classification method was tested in mapping corn and soybean, which are two dominant summer crop types in the central United States, and the experiment was carried out for Doniphan County, Kansas during years 2006鈥2010. Over 100 Landsat TM and ETM + images in this period were utilized and phenological metrics were calculated from Enhanced Vegetation Index time series using techniques including image segmentation and curve-fitting. Several sets of input variables, ranging from multi-spectral features of selected dates, which are widely used in traditional mapping efforts, to phenological metrics and derived measurements such as accumulated temperature, were tested using a random forest classifier. When the classifier was trained by reference data collected in the same year as that of remotely sensed data, most sets of input variables yielded accuracies higher than 88%. However, when the training data used by the classifier were obtained in a year different from the mapping years, only input sets containing phenological metrics were able to achieve acceptable accuracies greater than 80%. The use of phenological metrics as classification inputs avoids the restrictive requirements of a large ground reference dataset, enabling frequent and routine crop mapping without repeated collection of reference data.