4.7 Article

Optical Remote Sensing Indexes of Soil Moisture: Evaluation and Improvement Based on Aircraft Experiment Observations

期刊

REMOTE SENSING
卷 13, 期 22, 页码 -

出版社

MDPI
DOI: 10.3390/rs13224638

关键词

optical remote sensing; soil; vegetation; water content; moisture; drought

资金

  1. National Natural Science Foundation of China [41871338]
  2. Yue Qi Young Scholar Project [CUMTB2018]
  3. Fundamental Research Funds for the Central Universities [2020YJSDC08]

向作者/读者索取更多资源

This study evaluated thirteen typical optical soil moisture indexes and found that the Visible and Shortwave Infrared Drought Index (VSDI) and Optical TRApezoid Model (OPTRAM) outperformed the other indexes in comparison with observed soil moisture. Both VSDI and OPTRAM utilize two sensitive bands, which may contribute to their superior performance. The study also made improvements to VSDI and OPTRAM to enhance their performance in soil moisture monitoring.
Optical remote sensing (about 0.4~2.0 mu m) indexes of soil moisture (SM) are valuable for some specific applications such as monitoring agricultural drought and downscaling microwave SM, due to their abundant data sources, higher spatial resolution, and easy-to-use features, etc. In this study, we evaluated thirteen typical optical SM indexes with aircraft and in situ observed SM from two field campaigns, the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12) and 2016 (SMAPVEX16) conducted in Manitoba, Canada. MODIS surface reflectance products (MOD09A1) and Sentinel-2 multispectral imager Level-1C data were utilized to calculate the optical SM indexes. The evaluation results demonstrated that (1) the Visible and Shortwave Infrared Drought Index (VSDI) and Optical TRApezoid Model (OPTRAM) outperform the other eleven optical SM indexes as compared with aircraft and in situ observed SM. They also presented well consistence in temporal variation with the in situ observed SM. (2) The VSDI achieved comparable performance with the OPTRAM while the former has very simple calculation expression and the latter requires complex process to determine the dry and wet boundaries. (3) Both the VSDI and OPTRAM utilize two sensitive bands of soil and vegetation moisture, i.e., Red and SWIR bands, whereas the other eleven SM indexes only employ one sensitive band. This may be the main reason of the evaluation results. (4) Based on this recognition, improvements of the VSDI and OPTRAM were created and validated in this study through adding more sensitive band to VSDI and combining NDVI and modified VSDI into a new feature space for calculating the optical SM index as with OPTRAM. The results are conducive to selecting and utilizing the current numerous optical SM indexes for SM and drought monitoring.

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