Comparison of hydrological and vegetation remote sensing datasets as proxies for rainfed maize yield in Malawi
Comparison of hydrological and vegetation remote sensing datasets as proxies for rainfed maize yield in Malawi
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DOI：
10.1016/j.agwat.2021.107375
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发表时间：
2022-03-31
影响因子：
6.7
通讯作者：
Sheffield, Justin
中科院分区：
文献类型：
--
作者：
Anghileri, Daniela;Bozzini, Veronica;Sheffield, Justin
基于天气指数的Incentives（WIIs）已成为一种有前途的风险应对机制，以补偿天气引起的农业损失。遥感可以提供具有成本效益的信息，能够区分天气的空间变异性，从而降低空间基础风险，即，触发保险赔付的基于天气的指数与农民遭受的实际损失之间的不匹配，这往往是阻碍WII广泛实施的原因之一。在这项工作中，我们评估哪些指数的基础上遥感数据集是最好的代理指标，在马拉维的玉米产量。我们分析了空间历史玉米产量数据和几个遥感数据集（包括气候灾害组红外降水与台站（CHIRPS）数据集、欧空局CCI土壤湿度组合数据集）的（地区尺度）和时间（月）相关性（版本4.2），来自大气-土地交换反演模型（ALEXI）的蒸发应力指数（ESI），MOD 13 Q1归一化植被指数（NDVI）和增强型植被指数（EVI）。关于以前的文献，这项工作利用了次国家一级的历史作物产量数据集，这使我们能够以比通常所做的更高的空间分辨率分析水文气象和植被变量的相关性（即，在国家一级使用粮农组织国家产量统计数据），并最终探讨与WII空间基础风险有关的问题。结果表明，农作物产量和卫星数据集之间的相关性显示出很高的空间和时间变异性，因此很难确定一个独特的WII指数，同时对整个国家既简单又有效。降水，特别是标准化的3月降水异常，与玉米产量的相关性最高（皮尔逊相关值高于0.55），在中部和南部马拉维。土壤湿度和植被指数并没有增加太多的价值，降水在预测历史玉米产量在地区尺度。从方法论的角度来看，我们的工作表明，WII指数最好通过以下方式确定：i）尽可能考虑具有良好空间分辨率的数据集; ii）考虑不同作物生长阶段对水分胁迫的脆弱性; iii）区分缺水和丰水事件。
Weather Index-based Insurances (WIIs) have emerged as a promising risk coping mechanism to compensate for weather-induced damage to rainfed agriculture. Remote sensing may provide cost-effective information capable of discriminating the weather spatial variability thus reducing the spatial basis risk, i.e., the mismatch between the weather-based index triggering the insurance payout and the actual damage experienced by the farmers, which is often one of the causes hindering the wide implementation of WIIs. In this work we assess which indices based on remote sensing datasets are the best proxy indicators for rainfed maize yield in Malawi. We analyse the spatial (district scale) and temporal (monthly) correlations of historical maize yield data and several remote sensing datasets including the Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset, the ESA CCI Soil Moisture combined dataset (version 4.2), the Evaporative Stress Index (ESI) from the AtmosphereLand Exchange Inversion model (ALEXI), the MOD13Q1 Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). With respect to the previous literature, this work exploits a historical crop yield dataset at the sub-national level which allows us to analyse the correlation of the hydro-meteorological and vegetation variables at a higher spatial resolution than what is commonly done (i.e., at the national level using FAO national yield statistics) and ultimately explore the issues related to WII spatial basis risk. Results show that the correlations between crop yield and satellite datasets show high spatial and temporal variability, making it difficult to identify a unique WII index that is at the same time simple and effective for the entire country. Precipitation, particularly the standardized March precipitation anomaly, has the highest correlations with maize yield (with Pearson correlation values higher than 0.55), in Central and South Malawi. Soil moisture and NDVI do not add much value to precipitation in anticipating historical maize yield at the district scale. From a methodological perspective, our work shows that WII indexes are best identified by: i) considering datasets with fine spatial resolution, whenever possible; ii) accounting for the vulnerability of the different crop growing stages to water-stress; iii) distinguishing between water scarce and water abundant events.