Solar-induced fluorescence as a robust proxy for vegetation productivity across climate zones and vegetation types in the United States

Solar-induced fluorescence (SIF) has become a promising remote sensing proxy for photosynthetic activity and thus plant health, but its broad application across vegetation types and climate regimes remains underexplored. Here, we present the first continental-scale assessment of seasonal SIF signatures for 33 vegetation types across 24 climate zones in the contiguous United States, enabled by a new open-access visualization tool. The analysis uses TROPOMI satellite SIF data (2019-2021), along with MODIS-derived gross primary productivity (GPP), normalized difference vegetation index (NDVI), and vapor pressure deficit (VPD). Our results show that SIF has consistently stronger and more reliable correlations with GPP than NDVI across vegetation types and environmental conditions. This relationship remains robust even under high VPD conditions (except for several perennial crops), confirming the ability of SIF to track productivity even in dry environments. While NDVI retains structural sensitivity, it often decouples from GPP under stress, particularly in arid climates and perennial crops. We also identify clear differences in SIF-NDVI and GPP-NDVI relationships by vegetation type and climate, with NDVI showing limited responsiveness to dynamic changes in canopy physiology. Despite the coarse spatial resolution of TROPOMI, these results demonstrate the feasibility of constructing climate-specific (i.e., the SIF signature for a given climate type) SIF signatures for agricultural and ecological monitoring. By identifying these climate-specific signatures at the continental scale, this work highlights the value of SIF for climate-smart crop management, productivity assessment, and satellite-based ecosystem modeling. • SIF signatures vary across 33 vegetation types and 24 climate zones • SIF, GPP and NDVI relationships vary with climate, photosynthetic path and vegetation • SIF-GPP relationship remains robust even under high VPD • SIF outperforms NDVI as a proxy for GPP across climate zones and environmental stress • We present an interactive tool for climate-specific vegetation monitoring using SIF

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Publication Details

Journal
Remote Sensing Applications Society and Environment
Published
2025-10-17
DOI
https://doi.org/10.1016/j.rsase.2025.101760
Citations
2
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
3.43
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article

Solar-induced fluorescence as a robust proxy for vegetation productivity across climate zones and vegetation types in the United States

Luka Mamić, Delphine K. Farmer, Mj Riches, Francesco Pirotti
2 citations
Remote Sensing Applications Society and Environment
Remote Sensing in Agriculture
3.43
article

Solar-induced fluorescence as a robust proxy for vegetation productivity across climate zones and vegetation types in the United States

Luka Mamić, Delphine K. Farmer, Mj Riches, Francesco Pirotti
article en
2 citations

Abstract

Solar-induced fluorescence (SIF) has become a promising remote sensing proxy for photosynthetic activity and thus plant health, but its broad application across vegetation types and climate regimes remains underexplored. Here, we present the first continental-scale assessment of seasonal SIF signatures for 33 vegetation types across 24 climate zones in the contiguous United States, enabled by a new open-access visualization tool. The analysis uses TROPOMI satellite SIF data (2019-2021), along with MODIS-derived gross primary productivity (GPP), normalized difference vegetation index (NDVI), and vapor pressure deficit (VPD). Our results show that SIF has consistently stronger and more reliable correlations with GPP than NDVI across vegetation types and environmental conditions. This relationship remains robust even under high VPD conditions (except for several perennial crops), confirming the ability of SIF to track productivity even in dry environments. While NDVI retains structural sensitivity, it often decouples from GPP under stress, particularly in arid climates and perennial crops. We also identify clear differences in SIF-NDVI and GPP-NDVI relationships by vegetation type and climate, with NDVI showing limited responsiveness to dynamic changes in canopy physiology. Despite the coarse spatial resolution of TROPOMI, these results demonstrate the feasibility of constructing climate-specific (i.e., the SIF signature for a given climate type) SIF signatures for agricultural and ecological monitoring. By identifying these climate-specific signatures at the continental scale, this work highlights the value of SIF for climate-smart crop management, productivity assessment, and satellite-based ecosystem modeling. • SIF signatures vary across 33 vegetation types and 24 climate zones • SIF, GPP and NDVI relationships vary with climate, photosynthetic path and vegetation • SIF-GPP relationship remains robust even under high VPD • SIF outperforms NDVI as a proxy for GPP across climate zones and environmental stress • We present an interactive tool for climate-specific vegetation monitoring using SIF

Remote Sensing Applications Society and EnvironmentVol. 40
University of Padua (IT), Sapienza University of Rome (IT), Colorado State University (US)
Openalex Percentile: Top 7%
Remote Sensing in Agriculture
3.43
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