Harnessing Information From Shortwave Infrared Reflectance Bands to Enhance Satellite‐Based Estimates of Gross Primary Productivity

作者：Sadegh Ranjbar, Danielle Losos, Benjamin Dechant, Sophie Hoffman, Eyyup Ensar Başakın, Paul C. Stoy · 发表于：Journal of Geophysical Research Biogeosciences · 年份：2024 · DOI：10.1029/2024jg008240 · 被引用次数：7 · 研究领域：Remote Sensing in Agriculture、Calibration and Measurement Techniques、Spectroscopy and Chemometric Analyses

Abstract Monitoring gross primary productivity (GPP), the rate at which terrestrial 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 S WIR‐enhanced N ear‐ I nfra R ed reflectance of V egetation (sNIRv) by integrating SWIR information into established VIs across 96 Ameriflux and NEON 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 fractional photosynthetically active radiation, a proxy for GPP, especially for ecosystems with low leaf area index. Results highlight that sNIRv‐based VIs are less sensitive to soil background, SZA, a...