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Normalized NDVI valley area index (NNVAI)-based framework for quantitative and timely monitoring of winter wheat frost damage on the Huang-Huai-Hai Plain, China

Zhao, Longcai, Li, Qiangzi, Zhang, Yuan, Wang, Hongyan, Du, Xin
Agriculture, ecosystems & environment 2020 v.292 pp. 106793
Triticum aestivum, crop production, frost, frost injury, models, monitoring, normalized difference vegetation index, prediction, risk, winter wheat, China
Frost events pose a threat to normal development of winter wheat and increase risk of production loss, even given the context of global warming. On the Huang-Huai-Hai Plain (HHHP), which is the largest region of winter wheat cultivation in China, frost damage has become one of the major meteorological catastrophes. In recent years, both frequency of occurrence and severity of frost events in this region have exhibited an upward trend. For timely and accurate monitoring of single-event frost damage of winter wheat, this study developed a frost-monitoring framework that considers the processes of the evolution of a frost event and the response of winter wheat, as well as the lag between them. The framework adopts a NDVI valley area index (NVAI) to represent the gradual response process of winter wheat to frost. In this framework, the normalized NVAI (NNVAI) is used to indicate the degree of damage, and a two-phased damage assessment (i.e., T1 and T2) is adopted to resolve the contradiction between the urgency of obtaining information on frost severity, the spatial distribution of frost damage, and the delayed response process of winter wheat to frost. Using prediction models based on historical statistics, assessment at the T1 stage estimates the possible damage after the end of the frost, and assessment at the T2 stage evaluates the actual damage caused by frost when the winter wheat has recovered. Application of the proposed framework to a frost event that occurred on the HHHP during March 4–9, 2018 revealed that the average absolute percentage error between actual and predicted damage across the entire region was 33 %. Moreover, the prediction of damage was made 13 days ahead of the occurrence of actual damage. Thus, the framework could provide guidance to enable action to be taken to reduce the impact of frost. The proposed monitoring framework fully considers the processes of the evolution of a frost event and the response of winter wheat to frost, and it learns potential patterns from historical frost events to provide early prediction of potential frost damage. Consequently, the framework is considered suitable for wide practical application.