  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.
