The Ability of Sun-Induced Chlorophyll Fluorescence From OCO-2 and MODIS-EVI to Monitor Spatial Variations of Soybean and Maize Yields in the Midwestern USA

Aleksandra Wolanin,Yongguang Zhang,Songhan Wang,Weimin Ju,Liangzhi You,Yun Gao,Kaiyu Guan

doi:10.3390/rs12071111

Abstract

Satellite sun-induced chlorophyll fluorescence (SIF) has emerged as a promising tool for monitoring growing conditions and productivity of vegetation. However, it still remains unclear the ability of satellite SIF data to predict crop yields at the regional scale, comparing to widely used satellite vegetation index (VI), such as the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS). Additionally, few attempts have been made to verify if SIF products from the new Orbiting Carbon Observatory-2 (OCO-2) satellite could be applied for regional corn and soybean yield estimates. With the deep neural networks (DNN) approach, this study investigated the ability of OCO-2 SIF, MODIS EVI, and climate data to estimate county-level corn and soybean yields in the U.S. Corn Belt. Monthly mean and maximum SIF and MODIS EVI during the peak growing season showed similar correlations with corn and soybean yields. The DNNs with SIF as predictors were able to estimate corn and soybean yields well but performed poorer than MODIS EVI and climate variables-based DNNs. The performance of SIF and MODIS EVI-based DNNs varied with the areal dominance of crops while that of climate-based DNNs exhibited less spatial variability. SIF data could provide useful supplementary information to MODIS EVI and climatic variables for improving estimates of crop yields. MODIS EVI and climate predictors (e.g., VPD and temperature) during the peak growing season (from June to August) played important roles in predicting yields of corn and soybean in the Midwestern 12 states in the U.S. The results highlighted the benefit of combining data from both satellite and climate sources in crop yield estimation. Additionally, this study showed the potential of adding SIF in crop yield prediction despite the small improvement of model performances, which might result from the limitation of current available SIF products. The framework of this study could be applied to different regions and other types of crops to employ deep learning for crop yield forecasting by combining different types of remote sensing data (such as OCO-2 SIF and MODIS EVI) and climate data.

Highlights

Crop yield prediction is essential in a variety of socioeconomic aspects, such as agricultural management [1,2], economic planning and commodities forecasting [3,4], as well as food security monitoring [5,6]
The specific objectives of this study are: (1) to assess the ability of Orbiting Carbon Observatory-2 (OCO-2) sun-induced chlorophyll fluorescence (SIF) to monitor the spatial variation of maize and soybean yields relative to Enhanced Vegetation Index (EVI); (2) to compare the ability of remote sensing and climate data to monitor the spatial variations of corn and soybean yields in the Midwestern US; (3) to identify key periods when remote sensing and climate data are important for predicting crop yields
Correlations of monthly means and maxima of SIF and EVI with corn and soybean yields sharply increased from May to June and decreased from August to October

Summary

Introduction

Crop yield prediction is essential in a variety of socioeconomic aspects, such as agricultural management [1,2], economic planning and commodities forecasting [3,4], as well as food security monitoring [5,6]. Namely process and statistical-based modeling, have been extensively used to predict crop yield. Process-based models [10,11,12,13] simulate crop growth mechanically. They could be used to predict crop yield and to quantify the roles of individual factors in determining crop yield. Those models require various inputs such as cultivar and soil parameters, which are not always available or with significant uncertainties for many places around the world [14,15]. The widely-employed statistical methods usually predict crop yield by establishing simple linear or nonlinear relationships of yield with predictors, such as temperature, precipitation, and vapor pressure deficit (VPD) [18,19,20,21]

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