From pixels to plots: Synthesizing remote sensing into crop production estimation

In the overarching realm of national accounts, the precision of crop production data is crucial, considering its inter-dynamics with macroeconomic factors. Timely evaluation of the supply-side situation can provide early signals for undertaking proactive policy measures. Publication lags and subsequent revisions in official statistics on foodgrain production pose limitations for their operational utility. Complexities of agricultural ecosystems and evolving climatic conditions underscore the need for a sophisticated approach. The paper makes an attempt to design a generic framework for synthesizing remote sensing data for in-season monitoring of crop conditions and production nowcasting, aiming at enhancing accuracy and timeliness. The framework is designed to be customizable and scalable, and a bottom-up aggregation approach is followed for arriving at country-level estimates, considering the geo-spatial variation in data. Vegetation, soil moisture conditions, and temperature effects are analyzed along with area coverage. An empirical application carried out for pulses demonstrates the value gain of incorporating such new-age data and provides encouragement for further refinements for the operational use of remote sensing, weather, and climatic data for data-driven policy making.