A remote sensing and artificial neural network-based integrated agricultural drought index: Index development and applications
A remote sensing and artificial neural network-based integrated agricultural drought index: Index development and applications
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基于遥感和人工神经网络的综合农业干旱指数：指数开发与应用
DOI：
10.1016/j.catena.2019.104394
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发表时间：
2020-03
期刊：
影响因子：
6.2
通讯作者：
Peng Sun
中科院分区：
文献类型：
--
作者：
Xianfeng Liu;Xiufang Zhu;Qiang Zhang;Tiantian Yang;Yaozhong Pan;Peng Sun
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。然而，许多现有的干旱指数由一个单一的指标可能无法正确地描述农业干旱的复杂特征。本文提出了一种新的干旱指数--综合农业干旱指数（IDI），它描述了多个变量与农业干旱状况之间的关系。IDI的推导是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：降水量，地表温度（LST），归一化植被指数（NDVI），土壤水分容量和海拔。建议的IDI也可以捕获相对于降水量和LST变化的滞后效应的NDVI。我们的研究结果表明，基于机器学习方法的IDI可以放松许多现有指标中使用的假设，即输入和输出数据是线性相关的。以华北平原为例，研究结果还表明，IDI与SPI-3和SPEI-3接近。此外，我们发现，在NCP地区的干旱状况是高度相关的10厘米深的土壤水分在8个农业气象站和新开发的IDI可以有效地监测干旱的发生，持续时间，范围和强度的干旱事件。此外，IDI提供了根区土壤水分的空间信息，可以促进农业干旱监测。拟议的国际农业发展倡议框架也可适用于世界其他地区的农业管理。
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.
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影响因子：
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作者：
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DOI：
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发表时间：
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期刊：
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影响因子：
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