Abstract
Rapid changing climate has increased the risk of natural hazards and threatened global and regional food security. Near real-time monitoring of crop response to agrometeorological hazards is fundamental to ensuring national and global food security. However, quantifying crop responses to a specific hazard in the natural environment is still quite challenging, especially over large areas, due to the lack of tools to separate the independent impact of the hazard on crops from other confounding factors. In this study, we present a general difference-in-differences (DID) framework to monitor crop response to agrometeorological hazards at near real-time using widely accessible remotely sensed vegetation indices (VIs). To demonstrate the effectiveness of the DID framework, we applied it in quantifying the dry-hot wind impact on winter wheat in northern China as a case study using the VIs calculated from the MODIS data. The monitoring results for three years with varying severity levels of dry-hot events (i.e., 2007, 2013, and 2014) demonstrated that the framework can effectively detect winter wheat growing areas affected by dry-hot wind hazards. The estimated damage shows a notable relationship (R2 = 0.903, p < 0.001) with the dry-hot wind intensity calculated from meteorological data, suggesting the effectiveness of the method when field data on a large scale is not available for direct validation. The main advantage of this method is that it can effectively isolate the impact of a specific hazard (i.e., dry-hot wind in the case study) from the mixed signals caused by other confounding factors. This general DID framework is very flexible and can be easily extended to other natural hazards and crop types with proper adjustment. Not only can this framework improve the crop yield forecast but also it can provide near real-time assessment for farmers to adapt their farming practice to mitigate impacts of agricultural hazards.
Highlights
The rapidly changing climate has affected global and regional food security due to increased temperatures, changing precipitation patterns, and more frequent extreme events [1]
If there is no significant difference in the trend of the observed outcomes (Y) between the treatment group and the control group before the external policy intervention, the change of the observed outcome (Y) after and before the policy intervention in the control group (∆Yc, Equation (2)) can be considered as the case where the treatment group is not affected by the policy
We presented a general DID framework for monitoring crop response to agrometeorological hazards with satellite data and applied it to quantify the dry-hot wind damage to winter wheat in northern China
Summary
The rapidly changing climate has affected global and regional food security due to increased temperatures, changing precipitation patterns, and more frequent extreme events [1]. Near real-time monitoring of crop conditions is fundamental to ensure national and global food security, especially under the increasing risk of natural hazards caused by climate change. Earth Observation Systems (EOS) play a significant role in monitoring crop response to natural hazards, such as drought, flooding, frost, and heatwaves, at near real-time [2,3,4,5]. Previous studies have demonstrated that vegetation indices (VIs) derived from remotely sensed data show strong responses to the physiological and structural change of crops when they are under stresses [6,7,8]. VIs have been widely used for crop stress monitoring (see the review article in [9]).
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