TY - JOUR
T1 - Harnessing Information From Shortwave Infrared Reflectance Bands to Enhance Satellite-Based Estimates of Gross Primary Productivity
AU - Ranjbar, Sadegh
AU - Losos, Danielle
AU - Dechant, Benjamin
AU - Hoffman, Sophie
AU - Başakın, Eyyup Ensar
AU - Stoy, Paul C.
N1 - Publisher Copyright:
© 2024 The Author(s).
PY - 2024/11
Y1 - 2024/11
N2 - 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 SWIR-enhanced Near-InfraRed reflectance of Vegetation (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, and VZA compared to NIRv. SHapley Additive exPlanations (SHAP) value analysis also identifies sNIRv as the best feature for GPP estimation using machine learning modeling across different land covers, NDVI ranges, and soil water content levels.
AB - 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 SWIR-enhanced Near-InfraRed reflectance of Vegetation (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, and VZA compared to NIRv. SHapley Additive exPlanations (SHAP) value analysis also identifies sNIRv as the best feature for GPP estimation using machine learning modeling across different land covers, NDVI ranges, and soil water content levels.
KW - SWIR-enhanced Near-InfraRed reflectance of Vegetation (sNIRv)
KW - gross primary productivity (GPP)
KW - remote sensing
KW - vegetation index
UR - http://www.scopus.com/inward/record.url?scp=85208746996&partnerID=8YFLogxK
U2 - 10.1029/2024JG008240
DO - 10.1029/2024JG008240
M3 - Article
AN - SCOPUS:85208746996
SN - 2169-8953
VL - 129
JO - Journal of Geophysical Research: Biogeosciences
JF - Journal of Geophysical Research: Biogeosciences
IS - 11
M1 - e2024JG008240
ER -