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Harnessing Information from Shortwave Infrared Reflectance Bands to Enhance Satellite-Based Estimates of Gross Primary Productivity
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  • Sadegh Ranjbar,
  • Danielle Losos,
  • Benjamin dechant,
  • Sophie Hoffman,
  • Eyyup Ensar Başakın,
  • Paul Christopher Stoy
Sadegh Ranjbar
University of Wisconsin Madison

Corresponding Author:[email protected]

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Danielle Losos
University of Wisconsin Madison
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Benjamin dechant
Seoul National University
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Sophie Hoffman
University of Wisconsin Madison
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Eyyup Ensar Başakın
Istanbul Technical University
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Paul Christopher Stoy
University of Wisconsin - Madison
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Monitoring Gross Primary Productivity (GPP), the rate at which ecosystems fix atmospheric carbon dioxide, is crucial for understanding global carbon cycling. Remote sensing offers a powerful tool for monitoring GPP using vegetation indices (VIs) derived from visible and near-infrared reflectance (NIRv). While promising, these VIs often suffer from sensitivity to soil background, moisture, and variations in solar and view zenith angle (SZA and VZA). This study investigates the potential of incorporating shortwave infrared (SWIR) reflectance from MODIS and GOES-R advanced baseline imager (ABI) sensors to improve GPP estimation. We evaluated various formulations for creating SWIR-enhanced Near-InfraRed reflectance of Vegetation (sNIRv) by integrating SWIR information into established VIs across 96 Ameriflux research sites. Our findings reveal that sNIRv improves correlation with GPP for ABI data by up to 0.19 on a half-hourly basis for normalized difference vegetation index (NDVI) values below 0.25, with diminishing gains as NDVI values rise. Using MODIS data, sNIRv matches r values of NIRv for NDVI above 0.25, with a slight 0.05 increase for NDVI below 0.25. Analyses using SCOPE model simulations further support the ability of sNIRv to capture fPAR (fractional photosynthetically active radiation), a proxy for GPP, especially for ecosystems with low LAI. Results highlight that sNIRv-based VIs are less sensitive to soil background, SZA, and VZA compared to NIRv. Shapely Additive Explanations (SHAP) value analysis also identifies sNIRv as the best feature for GPP estimation using machine learning modeling across all different land covers, NDVI ranges, and soil water content (SWC) levels.
11 May 2024Submitted to ESS Open Archive
13 May 2024Published in ESS Open Archive