Information on soil moisture is important for crop and water management. This study investigated soil moisture variability in relation to rice cropping systems in the Mekong Delta (MD), Vietnam, using MODIS data acquired from January to April, 2002 to 2007. The soil moisture was estimated using the temperature vegetation dryness index (TVDI). This index was empirically calculated by parameterizing the relationship between the MODIS land surface temperature (LST) and the normalized difference vegetation index (NDVI) data. The rice cropping systems were classified using time-series MODIS NDVI data for 2002 and 2006. The data were processed using empirical mode decomposition (EMD), principal component analysis (PCA) and maximum likelihood classification (MLC) methods. Various spatial and non-spatial data were also collected for accuracy validation of the TVDI and classification results. Comparisons reveal good agreement between the TVDI and daily rainfall data. The areas with low soil moisture were mainly distributed in coastal areas from 2002 to 2005, but expanded into the middle of the MD in 2006 and 2007. The largest area of low soil moisture was observed in 2006. Classification of rice cropping systems reveals an overall accuracy and Kappa coefficient for 2002 of 76.3% and 0.68, respectively while the values for 2006 were 76.9% and 0.69, respectively. To relate soil moisture with rice cropping systems, we aggregated the composite soil moisture maps (considering dry and very dry classes only) with the rice crop maps for 2002 and 2006. The results indicate a remarkable increase in the area of double and triple irrigated ice cropping systems in areas of low soil moisture (i.e., dry and very dry conditions) during this period. This study demonstrated the merits of using MODIS data for studying soil variability in relation to rice crops which is important for crop and water management.

针对基于时域反射（TDR）和频域反射（FDR）原理的土壤水分传感器，以潮土、红壤和黑土3种典型土样为实验对象进行室内标定，用不同标定方式对传感器与烘干法的测量值进行拟合及回归分析。结果表明：1）对于TDR和FDR，二次多项式拟合效果均优于线性及指数形式；2）在潮土中，TDR的测量精度高于FDR，而在红壤和黑土中，FDR的测量精度高于TDR；3）在3种测试土壤内，TDR相对于FDR普适性强。选择的3种土壤是中国北部、中部及南部地区的典型土壤，具有一定代表性，可为全国大部分地区土壤类型普适性研究提供实验基础。

Many physical, chemical and biological processes taking place at the land surface are strongly influenced by the amount of water stored within the upper soil layers. Therefore, many scientific disciplines require soil moisture observations for developing, evaluating and improving their models. One of these disciplines is meteorology where soil moisture is important due to its control on the exchange of heat and water between the soil and the lower atmosphere. Soil moisture observations may thus help to improve the forecasts of air temperature, air humidity and precipitation. However, until recently, soil moisture observations had only been available over a limited number of regional soil moisture networks. This has hampered scientific progress as regards the characterisation of land surface processes not just in meteorology but many other scientific disciplines as well. Fortunately, in recent years, satellite soil moisture data have increasingly become available. One of the freely available global soil moisture data sets is derived from the backscatter measurements acquired by the Advanced Scatterometer (ASCAT) that is a C-band active microwave remote sensing instrument flown on board of the Meteorological Operational (METOP) satellite series. ASCAT was designed to observe wind speed and direction over the oceans and was initially not foreseen for monitoring soil moisture over land. Yet, as argued in this review paper, the characteristics of the ASCAT instrument, most importantly its wavelength (5.7 cm), its high radiometric accuracy, and its multiple-viewing capabilities make it an attractive sensor for measuring soil moisture. Moreover, given the operational status of ASCAT, and its promising long-term prospects, many geoscientific applications might benefit from using ASCAT soil moisture data. Nonetheless, the ASCAT soil moisture product is relatively complex, requiring a good understanding of its properties before it can be successfully used in applications. To provide a comprehensive overview of the major characteristics and caveats of the ASCAT soil moisture product, this paper describes the ASCAT instrument and the soil moisture processor and near-real-time distribution service implemented by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). A review of the most recent validation studies shows that the quality of ASCAT soil moisture product is - with the exception of arid environments - comparable to, and over some regions (e.g. Europe) even better than currently available soil moisture data derived from passive microwave sensors. Further, a review of applications studies shows that the use of the ASCAT soil moisture product is particularly advanced in the fields of numerical weather prediction and hydrologic modelling. But also in other application areas such as yield monitoring, epidemiologic modelling, or societal risks assessment some first progress can be noted. Considering the generally positive evaluation results, it is expected that the ASCAT soil moisture product will increasingly be used by a growing number of rather diverse land applications.

Leaf-area index of a forest can be measured by determining the ratio of light at 800 mμ to that at 675 mμ on the forest floor. It is based on the principle that leaves absorb relatively more red than infrared light, and therefore, the more leaves that are present in the canopy, the greater will be the ratio.

Thermal inertia of the earth's surface can be used in geologic mapping as a complement to surface reflectance data as provided by Landsat. Thermal inertia cannot be determined directly but must be inferred from radiation temperature measurements (by thermal IR sensors) made at various times in the diurnal cycle, combined with a model of the surface heating processes. We have developed a model which differs from models created previously for this purpose, because it includes sensible and latent heating. Tests of this model using field data indicate that it accurately determines the surface heating. When the model is used with field measurements of meteorological variables and is combined with remotely sensed temperature data, a thermal inertia image can be produced.

The thermal response of a substance to a time-varying surface power input is determined by its thermal inertia. Remote sensing (e.g., from satellites) can be utilized to measure this property, which is related to surface composition or to near-surface soil moisture. An algorithm is developed which relates thermal inertia to remote measurements of surface temperature and reflectance. Application to geosynchronous satellite data illustrates the contrast between irrigated and desert areas in the region north of the Gulf of California. The effect of local weather conditions (latent and sensible heat transfer to the atmosphere) must be estimated before precise values for thermal inertia can be specified.

A spectral window in the thermal infrared permits observations of surface temperature by satellite radiometry. The Heat Capacity Mapping Mission (HCMM) acquired 10–12 μm data at times of day favorable for estimation of surface thermal properties and the surface energy budget. Two variables, surface wetness, which controls evaporation and hence mean surface temperature, and thermal inertia, which relates the diurnal excursion of surface temperature to ground heat flux, are responsible for most observed temperature variability. These variables may be estimated from the mid night (2:30 a.m.) and early afternoon (1:30 p.m.) data from the HCMM or from the afternoon NOAA satellites. However, the HCMM data product, “apparent thermal inertia,” is potentially misleading in agricultural areas because surface evaporation reduces the amplitude of the soil heat flux compared to the amplitude in dry areas. Thus apparent thermal inertia should not be used in regions having variability in surface moisture.

ABSTRACT A unique relationship between the surface soil moisture availability and the radiant temperature does not exist in the presence of variable vegetation cover. To overcome this ambiguity, the authors present a method which couples a Soil‐Vegetation‐Atmosphere‐Transfer (SVAT) model to satellite derived measurements of surface radiant temperature and Normalized Vegetation Difference Index (NDVI) to ascertain surface soil moisture availability and fractional vegetation cover.Application of the technique is demonstrated for an agricultural area in a Pennsylvania watershed. Results of surface soil moisture availability and fractional vegetation cover are qualitatively realistic but the distribution of soil moisture availability is questionable at high fractional vegetation amounts.

A simplified land surface dryness index (Temperature egetation Dryness Index, TVDI) based on an empirical parameterisation of the relationship between surface temperature ( T s) and vegetation index (NDVI) is suggested. The index is related to soil moisture and, in comparison to existing interpretations of the T s/NDVI space, the index is conceptually and computationally straightforward. It is based on satellite derived information only, and the potential for operational application of the index is therefore large. The spatial pattern and temporal evolution in TVDI has been analysed using 37 NOAA-AVHRR images from 1990 covering part of the Ferlo region of northern, semiarid Senegal in West Africa. The spatial pattern in TVDI has been compared with simulations of soil moisture from a distributed hydrological model based on the MIKE SHE code. The spatial variation in TVDI reflects the variation in moisture on a finer scale than can be derived from the hydrological model in this case.

<p>基于2010年淮河流域35个地级市城市化和生态环境指标数据，运用模糊物元分析模型建立城市化生态环境系统指标体系，采用熵值法确定指标权重，构造改进的城市化与生态环境耦合协调度函数，测度淮河流域城市化与生态环境系统交互耦合的协调程度.结果表明： 2010年，淮河流域城市化子系统的发展水平低于生态环境子系统，两者综合协调指数值为0.186，两类响应指标之间存在一定差距.城市化与生态环境耦合度的平均值为0.475，流域城市化生态环境系统处于颉颃阶段.不同城市的城市化发展子系统之间存在较大差异，而生态环境子系统现状发展水平之间差异相对较小.城市化与生态环境协调度的平均值为0.706，表明淮河流域正处于高度协调耦合阶段，城市化与生态环境建设的整体&ldquo;功效&rdquo;与&ldquo;协同&rdquo;效应较高.</p>

时间、大气、太阳辐射的改变,导致获取的相邻遥感影像的对比度及光亮度产生差异,为了更好地进行分析、评价,需对遥感数据进行预处理。以新疆天山公路及其周边为研究区域,基于MODIS数据,利用ENVI软件进行数据的预处理,包括数据的几何校正、图像拼接、图像裁剪等,为其后进行的积雪研究作数据准备。

研究黄淮海平原旱灾监测中作物缺水指数模型实现的方法 ,特别是以与遥感图象相同的分辨率来计算作物缺水指数采取的对气象数据的处理 ,以及在 GIS的支持下 ,对矢量格式的地形图及遥感图象的不同处理 ,以实现图象、图形、数据的一体化。从而得到所需的研究区旱情分布图等不同形式的结果。实时监测表明 :本方法精度高 ,可靠性强 ,基本上达到了准确、实时监测干旱的目的。

应用NOAA-AVHRR数据,在用条件植被指数、条件温度指数和距平植被指数进行年度间相 对干旱程度监测的基础上,提出了条件植被温度指数的概念,它适用于监测某一特定年内某一时期(如旬)区域级的相对干旱程度.条件植被温度指数的定义既考虑 了区域内归一化植被指数的变化,又考虑了在归一化植被指数值相同条件下土地表面温度的变化.陕西省关中平原地区2000年3月下旬干旱的监测结果表明,条 件植被温度指数能较好地监测该区域的相对干旱程度,并可用于研究干旱程度的空间变化特征,对干旱的监测结果与用土壤热惯量模型反演的土壤表层含水量的结果 基本吻合.

Recent attempts to integrate remote sensing-based drought indices with precipitation data seem promising, and can compensate for potential uncertainties from image-based parameters alone, which may be unrelated to meteorological drought. However most remote sensing-based studies have been at regional or global scale and have not considered differences between different land cover types. This study examines a drought-prone region in Central Yunnan Province of China over a four-year period including a notable severe drought event in 2010. The study investigates the phase relationships between meteorological drought from image-based rainfall estimates from the Tropical Rainfall Measurement Mission (TRMM), and imaged drought from a remote sensing drought index, the Normalised Vegetation Supply Water Index (NVSWI) for different land cover types at local scale. The land cover types derived from MODIS and Landsat images were resampled to 250 m to match all datasets used. Significant differences between cover types are observed, with cropland and shrubland most highly correlated with 64 days' earlier rainfall and evergreen forest most responsive to rainfall 90 days earlier, indicating a need to consider detailed land cover information for accurate integrated drought indices. The finding that concurrent rainfall is only weakly correlated with observed drought, suggests that existing drought indices, which compute lowest weightings for the most distant lag period would be unrepresentative.

<p>提出一种改进的表观热惯量计算模型,以中科院栾城农业生态 系统试验站为基地,通过实测的模型参数,利用提出的表观热惯量模型计算不同植被覆盖下、不同实验区土壤含水量的热惯量值,并与土壤含水量进行相关性分析,以找到热惯量方法可以用来反演土壤含水量的适用条件(归一化植被指数NDVI的阈值)。实验结果表明,该模型监测土壤含水量是可行的,在植被覆盖度较低的情况下(<em>NDVI</em>&le;0.35)具有较高的精度,在植被覆盖度较高(<em>NDVI</em>&gt;0.35)时,热惯量模型失效,因此用热惯量方法反演土壤含水量植被覆盖时将<em>NDVI</em>阈值的最大值设为0.35。将该方法应用到MODIS数据中,以河北省栾城县、赵县、藁城市3 市县为研究区,分别反演该区土壤含水量,反演结果与实际情况相符合。实地取点人工监测土壤含水量为25.1%,栾城站模型计算结果为22.4%,匹配性较好,该方法在遥感数据中得到了很好的应用。</p>