Surface soil moisture quantification and validation based on hyperspectral data and field measurements
Surface soil moisture quantification and validation based on hyperspectral data and field measurements
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DOI：
10.1117/1.3059191
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发表时间：
2008
影响因子：
1.7
通讯作者：
S. Haubrock;S. Chabrillat;M. Kuhnert;P. Hostert;H. Kaufmann
中科院分区：
文献类型：
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作者：
S. Haubrock;S. Chabrillat;M. Kuhnert;P. Hostert;H. Kaufmann
地表土壤水分信息是监测和模拟不同空间尺度的地表过程所必需的。虽然已经开发了许多基于反射率的土壤水分量化模型并在实验室中进行了验证，但从遥感平台上应用并在田间进行彻底验证的很少。本文讨论了a)在沙质基质和低植被覆盖的景观中利用遥感高分辨率光谱测量来量化表层土壤水分的问题，以及b)在野外全面验证这些结果的问题。为此，对最近发展起来的归一化土壤水分指数(NSMI)在航空高光谱遥感数据中的适用性进行了分析。2004年和2005年的三个HyMap场景是从德国勃兰登堡南部的褐煤矿区收集的。对NSMI模型进行了校正(R2=0.92)，并基于该模型计算了表层土壤水分图。基于频域反射仪(FDR)和重力数据相结合的现场表层土壤水分图可用于验证每个图像像素(R2=0.82)。此外，对2004年以来两个连续的NSMI数据集进行了定性的多时相比较，并进行了验证，结果表明，与野外测量和降水数据相对应的表层土壤湿度估计值有所增加。研究表明，NSMI适合于高光谱分辨率遥感数据的表层土壤水分建模。在沙质基质和低植被覆盖地区(NDVI<；0.3)，该指数可以有效地估计田间持水量以下的土壤含水率。进一步的研究将分析NSMI在不同景观中利用环境测绘和分析计划(EnMap)等星载高光谱传感器估计表层土壤水分的有效性。
Surface soil moisture information is needed for monitoring and modeling surface processes at various spatial scales. While many reflectance based soil moisture quantification models have been developed and validated in laboratories, only few were applied from remote sensing platforms and thoroughly validated in the field. This paper addresses the issues of a) quantifying surface soil moisture with very high resolution spectral measurements from remote sensors in a landscape with sandy substrates and low vegetation cover as well as b) comprehensively validating these results in the field. For this purpose, the recently developed Normalized Soil Moisture Index (NSMI) has been analyzed for its applicability to airborne hyperspectral remote sensing data. Three HyMap scenes from 2004 and 2005 were collected from a lignite mining area in southern Brandenburg, Germany. An NSMI model was calibrated (R 2=0.92) and surface soil moisture maps were calculated based on this model. An in-situ surface soil moisture map based on a combination of Frequency Domain Reflectometry (FDR) and gravimetric data allowed for validating each image pixel (R 2=0.82). In addition, a qualitative multitemporal comparison between two consecutive NSMI datasets from 2004 was performed and validated, showing an increase in estimated surface soil moisture corresponding with field measurements and precipitation data. The study shows that the NSMI is appropriate for modeling surface soil moisture from high spectral-resolution remote sensing data. The index leads to valid estimations of soil moisture values below field capacity in an area with sandy substrates and low vegetation cover (NDVI < 0.3). Further studies will analyze the validity of the NSMI for surface soil moisture estimation from spaceborne hyperspectral sensors like the Environmental Mapping and Analysis Program (EnMap) in different landscapes.