Evaluation of the monitoring capability of various vegetation indices and mainstream satellite band settings for grassland drought

Zhu, Xiufang; Li, Qingfen; Guo, Chunhua

doi:10.1016/j.ecoinf.2024.102717

Ecological Informatics

2024

DOI: 10.1016/j.ecoinf.2024.102717

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Evaluation of the monitoring capability of various vegetation indices and mainstream satellite band settings for grassland drought

Xiufang Zhu,

Qingfen Li,

Chunhua Guo

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Cited by 6 publications

References 78 publications

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Spatio-temporal evaluation of MODIS temperature vegetation dryness index in the Middle East

Khosravi,

Homayouni,

Ouarda

2024

Ecological Informatics

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Spatio-temporal evaluation of MODIS temperature vegetation dryness index in the Middle East

Khosravi,

Homayouni,

Ouarda

2024

Ecological Informatics

View full text Add to dashboard Cite

CataEx: A multi-task export tool for the Google Earth Engine data catalog

Domej,

Pluta,

Ewertowski

2025

Environmental Modelling & Software

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

Ranjbar,

Losos,

Dechant

et al. 2024

JGR Biogeosciences

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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.

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Evaluation of the monitoring capability of various vegetation indices and mainstream satellite band settings for grassland drought

Cited by 6 publications

References 78 publications

Spatio-temporal evaluation of MODIS temperature vegetation dryness index in the Middle East

Spatio-temporal evaluation of MODIS temperature vegetation dryness index in the Middle East

CataEx: A multi-task export tool for the Google Earth Engine data catalog

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

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