CLIMATE CHANGE IMPACTS ON CROP YIELD AND PHENOLOGICAL SHIFTS USING REMOTE SENSING AND AGRO-CLIMATIC MODELING

Abstract

Climate change exerts profound, multifaceted impacts on global agriculture, manifesting as yield reductions, phenological disruptions, and altered agro-ecological suitability, with staple crops like maize, wheat, rice, and soybean projected to experience 24% caloric losses by 2100 under high-emission scenarios. This review synthesizes remote sensing techniques Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Land Surface Temperature (LST), and Solar-Induced Fluorescence (SIF) for monitoring these shifts at high spatiotemporal resolutions, complemented by agro-climatic models (DSSAT, APSIM, EPIC, CERES) that simulate genotype-environment-management (G×E×M) interactions under elevated CO₂, temperature extremes, and altered precipitation. Phenological alterations, including accelerated anthesis (5–15 days earlier) and shortened grain-filling periods, are linked to reduced yields (e.g., 5–20% per 1°C warming), with C₃ crops showing mixed responses to CO₂ fertilization (positive under drought but negative with heat). Regional disparities highlight severe vulnerabilities in tropical/subtropical breadbaskets, while adaptive strategies climate-smart varieties, precision irrigation, and diversified cropping offer mitigation potential. Integrating multi-sensor satellite data (MODIS, Sentinel, Landsat) with machine learning enhances predictive accuracy, supporting policy for resilient food systems amid accelerating climate variability.