Uniting Advances in Remote Sensing, Crop Modeling, & Economics for Understanding and Managing Weather Risk in Agriculture

The expanding availability of satellite data at higher spatial, temporal, and spectral resolutions has enabled novel applications in agriculture and economic development, including agricultural insurance. Yet, effectively using satellite data in this context requires blending technical knowledge about their capabilities and limitations with an understanding of how they influence the value of different risk reduction programs. In this presentation, we will discuss results from a collaborative review paper characterizing how approaches to estimate agricultural losses for index insurance have evolved, starting with costly field-sampling based campaigns towards lower cost techniques from weather and now satellite data. We identify advances in remote sensing and crop modeling for assessing crop conditions but flag how reliably and cheaply assessing yield losses remains challenging in complex landscapes. A case study and diagnostic diagrams illustrate an economic framework to gauge and enhance the value of insurance based on earth observation data, emphasizing that even as average yield estimation techniques improve, much of a index's value for the insured depends on how well it captures low yield situations when people suffer most. We conclude with an outline of key opportunities for researchers and practitioners to enhance the value of index insurance to the insured.