Using Satellite Data to Optimize Wheat Yield and Quality under Climate Change

• Main Authors: Shilo Shiff, Itamar M. Lensky, David J. Bonfil
• Format: Article
• Language: English
• Published: MDPI AG 2021-05-01
• Series: Remote Sensing
• Subjects: wheat; yield; climate change; optimize; MODIS; LST
• Online Access: https://www.mdpi.com/2072-4292/13/11/2049

Climatic conditions during the grain-filling period are a major factor affecting wheat grain yield and quality. Wheat in many semi-arid and arid areas faces high-temperature stress during this period. Remote sensing can be used to monitor both crops and environmental temperature. The objective of this study was to develop a tool to optimize field management (cultivar and sowing time). Analysis of 155 cultivar experiments (from 10 growth seasons) representing different environmental conditions revealed the required degree-days for each Israeli spring wheat cultivar to reach heading (from emergence). We developed a Google Earth Engine (GEE) app to analyze time series of gap-filled 1 km MODIS land surface temperature (<i>LST_cont</i>). By changing the cultivar and/or emergence date in the GEE app, the farmer can “expose” each wheat field to different climatic conditions during the grain-filling period, thereafter enabling him to choose the best cultivar to be sown in the field with the right timing. This approach is expected to reduce the number of fields that suffer from heat stress during the grain-filling period. The app can be also used to assess the effects of different global warming scenarios and to plan adaptation strategies in other regions too.