Improving Wheat Yield Prediction with Multi-Source Remote Sensing Data and Machine Learning in Arid Regions

• Published in: Remote sensing (Basel, Switzerland) Vol. 17; no. 5; p. 774
• Main Authors: Raza, Aamir, Shahid, Muhammad Adnan, Zaman, Muhammad, Miao, Yuxin, Huang, Yanbo, Safdar, Muhammad, Maqbool, Sheraz, Muhammad, Nalain E.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2025
• Subjects: Accuracy; Agricultural production; arid region; Arid regions; Arid zones; Climate; Climate change; Climatic data; Crop yield; Crop yields; Crops; Data analysis; Decision trees; Developmental biology; Food security; Food supply; Geospatial data; google earth engine (GEE); Grain; Growth models; Hydrology; Irrigation; Learning algorithms; Machine learning; Normalized difference vegetative index; Performance assessment; Precipitation; Predictions; Productivity; Remote sensing; Security management; Vegetation; Water shortages; Wheat; wheat yield prediction; Windows (intervals); Pakistan

Wheat (Triticum aestivum L.) is one of the world’s primary food crops, and timely and accurate yield prediction is essential for ensuring food security. There has been a growing use of remote sensing, climate data, and their combination to estimate yields, but the optimal indices and time window for...