Fine resolution remote sensing spectra improves estimates of gross primary production of croplands

Shirkey, Gabriela; John, Ranjeet; Chen, Jiquan; Dahlin, Kyla; Abraha, Michael; Sciusco, Pietro; Lei, Cheyenne; Reed, David E.

doi:10.1016/j.agrformet.2022.109175

Title: Fine resolution remote sensing spectra improves estimates of gross primary production of croplands

Journal Article · Sun Sep 25 00:00:00 EDT 2022 · Agricultural and Forest Meteorology

DOI:https://doi.org/10.1016/j.agrformet.2022.109175· OSTI ID:1922706

^[1]; John, Ranjeet ^[2]; Chen, Jiquan ^[1]; Dahlin, Kyla ^[1]; Abraha, Michael ^[1]; Sciusco, Pietro ^[1]; Lei, Cheyenne ^[1]; Reed, David E. ^[3]

Michigan State Univ., East Lansing, MI (United States)
Univ. of South Dakota, Vermillion, SD (United States)
University of Science and Arts of Oklahoma, Chickasha, OK (United States)

Gross primary production (GPP) is a fundamental measure of the terrestrial carbon cycle critical to our understanding of ecosystem function under the changing climate and land use. Remote sensing enables access to continuous spatial coverage, but remains challenged in heterogeneous croplands. Coarse resolution products, like MOD17A (500 m), may aggregate fragmented land cover types commonly found in heavily managed landscapes and misrepresent their respective contribution to carbon production. Consequently, this study demonstrates the capability of fine-resolution imagery (20-30 m) and available red-edge vegetation indices to characterize GPP across seven Midwest cropping systems. Four sites were established on a 22-year-old USDA Conservation Reserve Program (CRP); and the other three on land conventionally farmed with corn-soybean-wheat rotation (AGR). We compare in situ GPP estimates from eddy-covariance towers with ten satellite models: eight variants of the vegetation photosynthesis models (VPM), of which five include a red-edge vegetation index, as well as conventional products Landsat CONUS GPP (30 m) and MOD17A2H V6 (500 m). Daily and cumulative fine-resolution imagery integrated within VPM generally agreed with tower-based GPP in heterogeneous landscapes more than those from MODIS 500 m VPM or conventional GPP products from MOD17AH V6 or Landsat 8 CONUS. Here, replacing EVI2 with red-edge indices NDRE2, NDRE1, and MTCI in Sentinel 2 VPMs notably improved explanation of variance and estimation of cumulative GPP. While existing methods using MODIS- and Landsat-derived GPP are important baselines for regional and global studies, future research may benefit from the higher spatial, temporal, and radiometric resolution.

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Research Organization:: Michigan State University, East Lansing, MI (United States). Great Lakes Bioenergy Research Center

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER); National Science Foundation Graduate Research Fellowship

Grant/Contract Number:: SC0018409; DGE-1848739

OSTI ID:: 1922706

Journal Information:: Agricultural and Forest Meteorology, Vol. 326; ISSN 0168-1923

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Title: Fine resolution remote sensing spectra improves estimates of gross primary production of croplands

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