Information on the changing land surface is required at high spatial resolutions as many processes cannot be resolved using coarse resolution data. Deriving such information over large areas for Landsat data, however, still faces numerous challenges. Image compositing offers great potential to circumvent such shortcomings. We here present a compositing algorithm that facilitates creating cloud free, seasonally and radiometrically consistent datasets from the Landsat archive. A parametric weighting scheme allows for flexibly utilizing different pixel characteristics for optimized compositing. We describe in detail the development of three parameter decision functions: acquisition year, day of year and distance to clouds. Our test site covers 42 Landsat footprints in Eastern Europe and we produced three annual composites. We evaluated seasonal and annual consistency and compared our composites to BRDF normalized MODIS reflectance products. Finally, we also evaluated how well the composites work for land cover mapping. Results prove that our algorithm allows for creating seasonally consistent large area composites. Radiometric correspondence to MODIS was high (up to R-2 > 0.8), but varied with land cover configuration and selected image acquisition dates. Land cover mapping yielded promising results (overall accuracy 72%). Class delineations were regionally consistent with minimal effort for training data. Class specific accuracies increased considerably (similar to 10%) when spectral metrics were incorporated. Our study highlights the value of compositing in general and for Landsat data in particular, allowing for regional to global LULCC mapping at high spatial resolutions.

The objective of this study was to evaluate alternative methods for producing both Normalized Difference Vegetation Index (NDVI) and single-channel composite images of land surfaces from the Advanced Very High Resolution Radiometer (AVHRR). As a goal, it was specified that the composite image should approximate as much as possible a single-date image with a constant, near-nadir geometry. The comparative performance of three single-step [maximum NDVI (MaN), maximum apparent temperature (MaT), maximum difference of channels 2 minus 1 (MaD)] and two two-step [maximum NDVI followed by maximum temperature (MaNMaT) or by minimum scan angle (MaNMiSc)] criteria in creating composite images was evaluated for three land cover types, namely, cropland, coniferous forest, and deciduous forest within a boreal ecosystem. The assessment was carried out using 18 images obtained with various acquisition geometries in mid-summer over a 1000脳1000 km area in Manitoba, Canada. In addition, MaT performance was compared with that of MaN for all Canada during two compositing periods. It was found that among the five criteria tested, MaT and MaNMiSc were the most effective one- and two-step criteria, respectively. MaN preferentially selected off-nadir pixels from the forescatter region, the degree varying with land cover type; the overall NDVI values were higher than for MaT. MaD showed a very strong preference for high backscatter region pixels, regardless of land cover type. Depending on cover type, the NDVI values resulting from Mao were higher or lower than those from a nadir image. Based on ranking of the five techniques using statistics of the differences between composite and reference images, it was found that MaT and MaNMiSc performed similarly, and better than the remaining criteria. Results of the tests show that although statistically reasonable approximations of the reference image could be produced by one or more methods, none of the criteria could consistently yield composites closely resembling the nadir image on a pixel basis, even for a reasonably long compositing period. Therefore, pixel-specific applications of the composites relying on individual channels will likely have to be based on data corrected for bidirectional effects. The results suggest that such corrections are also required for NDVI and, by analogy, for other AVHRR channel combinations

Abstract The U.S. Geological Survey currently generates composites of A VHRR imagery based on a single objective - maximizing the Normalized Difference Vegetation Index (NDVI) - as a means of reducing cloud contamination. Our research supports the findings of others that, in some cases, NDVI is maximized at the expense of optimal viewing geometry; that is, satellite zenith angles are often further off-nadir than necessary to ensure cloud-free viewing. We explore various compositing methods by systematically varying weights on NDVI, satellite zenith angle, and maximum apparent temperature. A test composite of California from September 1990 appears to be superior to the maximum NDVI and maximum apparent temperature composites in several respects. First, the satellite zenith angle distribution is more closely clustered about nadir, which minimizes atmospheric path length, spatial distortion) and bidirectional reflectance effects. Second, neighboring pixels are more frequently selected with similar viewing geometry and atmospheric conditions.

Free and open access to the more than 40 years of data captured in the Landsat archive, combined with improvements in standardized image products and increasing computer processing and storage capabilities, have enabled the production of large-area, cloud-free, surface reflectance pixel-based image composites. Best-available-pixel (BAP) composites represent a new paradigm in remote sensing that is no longer reliant on scene-based analysis. A time series of these BAP image composites affords novel opportunities to generate information products characterizing land cover, land cover change, and forest structural attributes in a manner that is dynamic, transparent, systematic, repeatable, and spatially exhaustive. Herein, we articulate the information needs associated with forest ecosystem science and monitoring in a Canadian context, and indicate how these new image compositing approaches and subsequent derived products can enable us to address these needs. We highlight some of the issues and opportunities associated with an image compositing approach and demonstrate annual composite products at a national-scale for a single year, with more detailed analyses for two prototype areas using 15 years of Landsat data. Recommendations concerning how to best link compositing decisions to the desired use of the composite (and the information need) are presented, along with future research directions.

<p>多源遥感数据时空融合模型是解决目前遥感数据获取能力不足问题的重要方法之一，当前主要融合方法的研究，集中于平原区域，缺乏复杂条件下的多源遥感数据融合技术的应用研究。针对我国南方复杂条件，本文对比研究了多源遥感数据时空融合模型在我国南方复杂条件下的应用能力。针对LORENZO模型、LIU模型、统计回归模型、STARFM和ESTARFM 5种主流多源遥感数据时空融合模型，采用Landsat-ETM+和MODIS数据，以江苏省南京市的小块区域为实验区，利用5种模型生产融合影像，以真实Landsat-ETM+数据为模板，定性和定量评价融合效果的好坏。结果表明：除LORENZO模型外，其余4种模型获得的融合影像与真实影像之间都具有较高的相关性，相关系数均高于0.6，其中，ESTARFM模型的融合影像与真实影像间的相关性最高，融合效果最好，其次为STARFM模型，再次为LIU模型和统计模型法。在融合过程中采用距离、时间和光谱等信息越多，融合效果越好，在复杂地区的适用能力越强，融合影像更能反映地物的细节特征。</p>

61A new algorithm for Continuous Change Detection and Classification of land cover61All available (a total of 519) Landsat images from Path 12 Row 31 were used.61It can detect many kinds of land cover change continuously.61It can provide land cover maps for any given time.61The results were accurate both in change detection and classification.

78 We propose a new time series based near real-time change monitoring approach. 78 The method is robust, fast and does not require gap filling or smoothing. 78 A wide range of in-situ and remote sensing data series can be analysed. 78 Drought related disturbances are detected in southern Somalia as an example. 78 Functionality is available via the 'bfast' package for R.