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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #338110

Research Project: Leveraging Remote Sensing, Land Surface Modeling and Ground-based Observations ... Variables within Heterogeneous Agricultural Landscapes

Location: Hydrology and Remote Sensing Laboratory

Title: The shared and unique values of optical, fluorescence, thermal and microwave satellite data for estimating large-scale crop yields

Author
item GUAN, K. - UNIVERSITY OF ILLINOIS
item WU, J. - BROOKHAVEN NATIONAL LABORATORY
item KIMBALL, J. - UNIVERSITY OF MONTANA
item Anderson, Martha
item FROLKING, S. - UNIVERSITY OF NEW HAMPSHIRE
item LI, B. - UNIVERSITY OF ILLINOIS
item HAIN, C. - COLLABORATOR
item LOBELL, D. - STANFORD UNIVERSITY

Submitted to: Remote Sensing of Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/29/2017
Publication Date: 8/10/2017
Citation: Guan, K., Wu, J., Kimball, J., Anderson, M.C., Frolking, S., Li, B., Hain, C., Lobell, D. 2017. The shared and unique values of optical, fluorescence, thermal and microwave satellite data for estimating large-scale crop yields. Remote Sensing. 199:333-349.

Interpretive Summary: Efforts to integrate new sources of satellite information into crop yield monitoring and forecasting activities have intensified over the past decade. Remote sensing data collected in different spectral wavebands provide a valuable source of spatially distributed, timely information on crop condition and potential stress impacts on at-harvest yield. As we move toward enhanced monitoring techniques, it is important to understand the unique value that each type of remote sensing information conveys regarding crop condition, and how multiple satellite indicators might be used synergistically to improve yield estimates. This paper compares indicators developed from solar-induced fluorescence (SIF), thermal infrared (TIR) base evapotranspiration (ET), and passive and active microwave retrievals of vegetation optical depth (VOD). SIF radiance in the process of photosynthesis and is therefore an indicator of carbon uptake, ET reflects the water use by plants through transpiration, while VOD describes the water content of the plants themselves. Spatial distributions of these indicators, retrieved mid-season, are compared with corn and soybean yields reported by the National Agricultural Statistics Service over the US Corn Belt. The analyses demonstrate that SIF, being directly related to carbon uptake and biomass production, is a good indicator of yield. ET and VOD provide additional useful information over periods where moisture stress is known to have occurred. Further studies will investigate development of composite indices, optimally combining information from these various indicators.

Technical Abstract: Large-scale crop monitoring and yield estimation are important for both scientific research and practical applications. Satellite remote sensing provides an effective means for regional and global cropland monitoring, particularly in data-sparse regions that lack reliable ground observations and reporting. The conventional approach of using visible and near-infrared based vegetation index (VI) observations has prevailed for decades since the onset of the global satellite era. However, other satellite data encompass diverse spectral ranges that may contain complementary information on crop growth and yield, but have been largely understudied and underused. Here we conduct one of the first attempts at synergizing multiple satellite data spanning a diverse spectral range, including visible, near-infrared, thermal and microwave, into one framework to estimate crop yield for the U.S. Corn Belt, one of the world’s most important food baskets. Specifically, we include MODIS Enhanced VI (EVI), estimated Gross Primary Production based on GOME-2 Solar-induced fluorescence (SIF-GPP), thermal-based ALEXI Evapotranspiration (ET), QuikSCAT Ku-band radar backscatter, and AMSR-E/2 X-band passive microwave Vegetation Optical Depth (VOD) in this study, benchmarked on USDA county-level crop yield statistics. We use Partial Least Square Regression (PLSR) to distinguish commonly shared and unique individual information from the various satellite data and other ancillary climate information for crop yield estimation. We find that most of the satellite derived metrics (e.g. SIF-GPP, radar backscatter, EVI, VOD, ALEXI-ET) share common information related to aboveground crop biomass. For this shared information, the SIF-GPP and backscatter data contain almost the same amount of information as EVI at the county scale. When removing the above shared component from all of the satellite data, we find that EVI and SIF-GPP does not provide much extra information; instead, Ku-band backscatter, thermal-based ALEXI-ET, and X-band VOD provide information that improves overall crop yield predictive skill. In particular, Ku-band backscatter and associated differences between morning and afternoon overpasses contribute unique information on crop growth and environmental stress. Overall, using satellite data from various spectral bands significantly improves regional crop yield predictions. The additional use of ancillary climate data (e.g. precipitation and temperature) further improves model skill, in part because the crop reproductive stage related to harvest index is highly sensitive to environmental stresses that are not fully captured by the satellite data used in our study. We conclude that using satellite data across various spectral ranges can improve monitoring of large-scale crop growth and yield beyond what can be achieved with current individual sensors. These results also inform the synergistic use and development of current and next generation satellite missions, including NASA RapidSCAT, ECOSTRESS, SMAP, and OCO-2 for agricultural applications.