Abstract
Soil moisture in root zone soil layers is one of the most important indicators of agricultural drought. Thus, monitoring agricultural drought requires not only knowledge of rainfall anomaly but also quantification of soil moisture. In this study, the effects of various methods of quantifying the green vegetation fraction green vegetation fraction (GVF) on the land-surface-model (LSM)-based soil moisture drought index (SMDI) were assessed using the harvest area data of the World Meteorological Organization together with the widely used vegetation health index and drought severity index. GVF data used in this study include monthly climatological GVF, weekly advanced very high-resolution radiometer (AVHRR)-normalized difference vegetation index-based and 8-daily moderate-resolution imaging spectroradiometer (MODIS) leaf area index (LAI)-based GVF. The results show that SMDI is optimized when using the near-real-time GVF and that LAI-based GVF increases the accuracy of SMDI when monitoring early agricultural drought. The study shows that we can be confident in the accuracy of signals of emerging drought, particularly during the rapid onset of drought.
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The data that support the findings of this study are available from the corresponding author upon reasonable request.
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This work was supported by a grant from National Natural Science Foundation of China (No. 41575110). We are also grateful to anonymous reviewers for helping to significantly improve the quality of the manuscript.
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Wu, R., Li, Q. Assessing the soil moisture drought index for agricultural drought monitoring based on green vegetation fraction retrieval methods. Nat Hazards 108, 499–518 (2021). https://doi.org/10.1007/s11069-021-04693-x
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DOI: https://doi.org/10.1007/s11069-021-04693-x