Reliable drought monitoring is critical for evaluating drought risk and reducing potential agricultural losses. However, many existing drought indices developed by a single indicator may not properly describe the complex features of agricultural drought. Here, we propose a new drought index—the integrated agricultural drought index (IDI), which describes the relationship between multiple variables and agricultural drought conditions. The derivation of IDI is based on the remote sensing data and the back-propagation (BP) neural network, capable of identifying the non-stationary relationship of drought conditions. Development of IDI involves the following meteo-hydrological variables: precipitation, land surface temperature (LST), normalized difference vegetation index (NDVI), soil water capacity, and elevation. The lagging effect of NDVI with respect to precipitation and LST changes can also be captured by the proposed IDI. Our results indicate that the IDI based on a machine learning method can relax the assumption used in many existing indices that the input and output data are linearly correlated. Results also demonstrate that the IDI is close to SPI-3 and SPEI-3 in a case study of the North China Plain (NCP). Moreover, we found the drought condition in the NCP area is highly correlated with 10 cm depth soil moisture at 8 agrometeorological stations and the newly developed IDI can effectively monitor the drought in terms of onset, duration, extent, and intensity of a drought episode. Additionally, the IDI provides spatial information about root zone soil moisture that can facilitate agricultural drought monitoring. The proposed framework of IDI can also be applied in other regions of the world for agriculture management.

可靠的干旱监测对于评估干旱风险和减少潜在的农业损失至关重要。但是，由单个指标制定的许多现有干旱指数可能无法正确描述农业干旱的复杂特征。在这里，我们提出了一个新的干旱指数-综合农业干旱指数（IDI），该指数描述了多个变量与农业干旱条件之间的关系。IDI的推导基于遥感数据和反向传播（BP）神经网络，能够识别干旱条件的非平稳关系。IDI的发展涉及以下气象水文变量：降水，地表温度（LST），归一化植被指数（NDVI），土壤水容量和海拔。提出的IDI也可以捕获NDVI对降水和LST变化的滞后效应。我们的结果表明，基于机器学习方法的IDI可以放宽在许多现有索引中使用的假设，即输入和输出数据是线性相关的。结果还表明，在华北平原（NCP）的案例研究中，IDI接近SPI-3和SPEI-3。此外，我们发现NCP地区的干旱状况与8个农业气象站的10 cm深度土壤湿度高度相关，并且新开​​发的IDI可以有效地监测干旱发作的发生，持续时间，程度和强度。此外，IDI还提供了有关根区土壤水分的空间信息，可以促进农业干旱监测。