Estimation of vegetation water content from the radar vegetation index at L-band

Yuancheng Huang, Jeffrey Walker, Ying Gao, Xiaoling Wu, Alessandra Monerris-Belda

Research output: Contribution to journalArticleResearchpeer-review

23 Citations (Scopus)

Abstract

Information on vegetation water content (VWC) is importϒant in retrieving soil moisture using microwave remote sensing. It can be also used for other applications, including drought detection, bushfire prediction, and agricultural productivity assessment. Through the Soil Moisture Active Passive (SMAP) mission of the National Aeronautics and Space Administration, radar data may potentially provide the VWC information needed for soil moisture retrieval from the radiometer data acquired by the same satellite. In this paper, VWC estimation is tested using radar vegetation index (RVI) data from the third SMAP airborne Experiment. Comparing with coincident ground measurements, prediction equations for wheat and pasture were developed. While a good relationship was found for wheat, with r = 0.49, 0.62, and 0.65 and root-mean-square error (RMSE) = 0.42, 0.37, and 0.36 kg/m2, the relationship for pasture was poor, with r = -0.06, -0.14, and - 0.002 and RMSE = 0.15, 0.15, and 0.15, kg/m2, for 10-, 30-, and 90-m resolutions, respectively. These results suggested that RVI is better correlated with VWC for vegetation types having a greater dynamic range. However, the results were not as good as those from a previous tower-based study (r = 0.98 and RMSE = 0.12 kg/m2) over wheat. This is possibly due to spatial variation in vegetation structure and surface roughness not present in tower studies. Consequently, results from this study are expected to more closely represent those from satellite observations such as SMAP, where large variation in vegetation and environment conditions will be experienced.
Original languageEnglish
Pages (from-to)981 - 989
Number of pages9
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume54
Issue number2
DOIs
Publication statusPublished - Feb 2016

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