Progress in soil moisture estimation from remote sensing data for agricultural drought monitoring

Feng Yan; Zhihao Qin; Maosong Li; Wenjuan Li

doi:10.1117/12.689309

3 October 2006 Progress in soil moisture estimation from remote sensing data for for agricultural drought monitoring

Feng Yan, Zhihao Qin, Maosong Li, Wenjuan Li

Proceedings Volume 6366, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology VI; 636601 (2006) https://doi.org/10.1117/12.689309
Event: SPIE Remote Sensing, 2006, Stockholm, Sweden

Abstract

Soil moisture is one of the most important indicators for agricultural drought monitoring. In this paper we present a comprehensive review to the progress in remote sensing of soil moisture, with focus on discussion of the method details and problems existing in soil moisture estimation from remote sensing data. Thermal inertia and crop water stress index (CWSI) can be used for soil moisture estimation under bare soil and vegetable environments respectively. Anomaly vegetation index (AVI) and vegetation condition index (VCI) are another alternative methods for soil moisture estimation with Normalized difference vegetation index (NDVI). Both NDVI and land surface temperature (LST) are considered in temperature vegetation index (TVI), vegetation supply water index (VSWI) and vegetation temperature condition index (VTCI). Microwave remote sensing is the most effective technique for soil moisture estimation. Active microwave can provide high spatial resolution but is sensitive to soil rough and vegetation. Passive microwave has a low resolution and revisit frequency but it has more potential for large scale agricultural drought monitoring. Integration of optical/ IR and microwave remote sensing may be the practical method for drought monitoring in both accuracy and in efficiency.

Citation Download Citation

Feng Yan, Zhihao Qin, Maosong Li, and Wenjuan Li "Progress in soil moisture estimation from remote sensing data for for agricultural drought monitoring", Proc. SPIE 6366, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology VI, 636601 (3 October 2006); https://doi.org/10.1117/12.689309

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