Abstract
The utility of remote sensing data in crop yield modeling has typically been evaluated at the regional or state level using coarse resolution (>250 m) data. The use of medium resolution data (10–100 m) for yield estimation at field scales has been limited due to the low temporal sampling frequency characteristics of these sensors. Temporal sampling at a medium resolution can be significantly improved, however, when multiple remote sensing data sources are used in combination. Furthermore, data fusion approaches have been developed to blend data from different spatial and temporal resolutions. This paper investigates the impacts of improved temporal sampling afforded by multi-source datasets on our ability to explain spatial and temporal variability in crop yields in central Iowa (part of the U.S. Corn Belt). Several metrics derived from vegetation index (VI) time-series were evaluated using Landsat-MODIS fused data from 2001 to 2015 and Landsat-Sentinel2-MODIS fused data from 2016 and 2017. The fused data explained the yield variability better, with a higher coefficient of determination (R2) and a smaller relative mean absolute error than using a single data source alone. In this study area, the best period for the yield prediction for corn and soybean was during the middle of the growing season from day 192 to 236 (early July to late August, 1–3 months before harvest). These findings emphasize the importance of high temporal and spatial resolution remote sensing data in agricultural applications.
Highlights
Accurate estimation of crop yields before harvest is critical for sustaining agricultural markets and ensuring food security
In the early 1980s, Tucker et al [2] reported that the normalized difference vegetation index (NDVI) for a five-week period from stem elongation to anthesis explained about 64 percent of the grain yield variation of wheat
Since clear Landsat dates and pixels were different for each year, the annual maximum EVI2 value may come from different dates in different years
Summary
Accurate estimation of crop yields before harvest is critical for sustaining agricultural markets and ensuring food security. The fraction of incoming PAR that is absorbed by the plant canopies can be estimated by remotely sensed multispectral data, and phytomass production can be estimated as a function of cumulative APAR. These pioneer studies were typically focused on specific crops and were conducted over small fields, they generally covered the major crop development phases. Those early field experiments demonstrate the importance of frequent remote sensing observations for estimating crop yields
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