Synergistic Integration of Optical and Microwave Satellite Data for Crop Yield Estimation

TL;DR

This paper introduces two novel methods combining optical and microwave satellite data for crop yield estimation, utilizing new joint metrics and machine learning to improve accuracy at the county level.

Contribution

It proposes a new joint metric combining EVI and VOD data and employs machine learning on full time series for crop yield prediction, advancing remote sensing techniques.

Findings

Effective crop yield estimation at county level

Improved accuracy using combined satellite data

Novel joint metric enhances data integration

Abstract

Developing accurate models of crop stress, phenology and productivity is of paramount importance, given the increasing need of food. Earth observation remote sensing data provides a unique source of information to monitor crops in a temporally resolved and spatially explicit way. In this study, we propose the combination of multisensor (optical and microwave) remote sensing data for crop yield estimation and forecasting using two novel approaches. We first propose the lag between Enhanced Vegetation Index derived from MODIS and Vegetation Optical Depth derived from SMAP as a new joint metric combining the information from the two satellite sensors in a unique feature or descriptor. Our second approach avoids summarizing statistics and uses machine learning to combine full time series of EVI and VOD. This study considers two statistical methods, a regularized linear regression and its nonlinear extension called kernel ridge regression to directly estimate the county-level surveyed total production, as well as individual yields of the major crops grown in the region: corn, soybean and wheat. The study area includes the US Corn Belt, and we use agricultural survey data from the National Agricultural Statistics Service (USDA-NASS) for year 2015 for quantitative assessment.