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.

根区土壤层的土壤水分是农业干旱的最重要指标之一。因此，监测农业干旱不仅需要了解降雨异常情况，还需要量化土壤湿度。在这项研究中，使用世界收获面积数据评估了各种量化绿色植被分数的方法对基于土地表面模型（LSM）的土壤水分干旱指数（SMDI）的影响。气象组织以及广泛使用的植被健康指数和干旱严重程度指数。本研究中使用的GVF数据包括每月气候GVF，每周先进的超高分辨率辐射计（AVHRR）归一化差异植被指数和基于8每日中等分辨率成像光谱仪（MODIS）的叶面积指数（LAI）的GVF。结果表明，使用近实时GVF可以优化SMDI，而基于LAI的GVF可以在监测早期农业干旱时提高SMDI的准确性。研究表明，我们对新出现的干旱信号的准确性充满信心，特别是在干旱迅速发作期间。