Detection of fast-changing intra-seasonal vegetation dynamics of drylands using solar-induced chlorophyll fluorescence (SIF) DOI Creative Commons
Jiaming Wen, Giulia Tagliabue, Micol Rossini

et al.

Biogeosciences, Journal Year: 2025, Volume and Issue: 22(8), P. 2049 - 2067

Published: April 25, 2025

Abstract. Dryland ecosystems are the habitat supporting 2 billion people on Earth, and they strongly impact global terrestrial carbon sink. Vegetation growth in drylands is mainly controlled by water availability with strong intra-seasonal variability. Timely of information at such scales (e.g., from days to weeks) essential for early warning potential catastrophic impacts emerging climate extremes crops natural vegetation. However, large-scale monitoring vegetation dynamics has been very challenging drylands. Satellite solar-induced chlorophyll fluorescence (SIF) emerged as a promising tool characterize spatiotemporal photosynthetic uptake detect dynamics. few studies have evaluated its capability detecting fast-changing advantages over traditional approaches based indices (VIs). To fill this knowledge gap, study utilized vast dryland Horn Africa (HoA) testbed their inferred satellite SIF. The HoA an ideal because highly dynamic responses short-term environmental changes. satellite-data-based analysis was corroborated unique situ SIF dataset collected Kenya – so far, only ground time series continent Africa. We found that TROPOspheric Monitoring Instrument (TROPOMI) daily revisit frequency identified week-to-week variations both shrublands grasslands; rapidly changing corresponded up- downregulation fluctuations variables air temperature, vapor pressure deficit, soil moisture). neither reconstructed products nor near-infrared reflectance (NIRv) Moderate Resolution Imaging Spectroradiometer (MODIS), which widely used literature, able capture variations. same findings hold site scale, where we TROPOMI revealed two separate within-season cycles response extreme moisture rainfall amount duration, consistent measurements. This generates novel insights evaluation sensitivities, enabling development predictive scalable understanding how may respond future change informing design effective systems

Language: Английский

The growing threat of multiyear droughts DOI
David L. Hoover, William K. Smith

Science, Journal Year: 2025, Volume and Issue: 387(6731), P. 246 - 247

Published: Jan. 16, 2025

Understanding and monitoring ecological responses is important as droughts last longer

Language: Английский

Citations

0
A new method for evaluating the coordinated relationship between vegetation greenness and urbanization DOI Creative Commons
Huimeng Wang,

Chuanwen Yang,

Yong Sun

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 19, 2025

Language: Английский

Citations

0
Revealing the spectral bands that make generic remote estimates of leaf area index in wheat crop over various interference factors and planting conditions DOI
Heli Li,

Pingheng Li,

Xingang Xu

et al.

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 235, P. 110381 - 110381

Published: April 14, 2025

Language: Английский

Citations

0
Spatial Heterogeneity of Driving Factors in Multi-Vegetation Indices RSEI Based on the XGBoost-SHAP Model: A Case Study of the Jinsha River Basin, Yunnan DOI Creative Commons
Jisheng Xia, Guoyou Zhang,

Shiping Ma

et al.

Land, Journal Year: 2025, Volume and Issue: 14(5), P. 925 - 925

Published: April 24, 2025

The Jinsha River Basin in Yunnan serves as a crucial ecological barrier southwestern China. Objective assessment and identification of key driving factors are essential for the region’s sustainable development. Remote Sensing Ecological Index (RSEI) has been widely applied assessments. In recent years, interpretable machine learning (IML) introduced novel approaches understanding complex mechanisms. This study employed Google Earth Engine (GEE) to calculate three vegetation indices—NDVI, SAVI, kNDVI—for area from 2000 2022, along with their corresponding RSEI models (NDVI-RSEI, SAVI-RSEI, kNDVI-RSEI). Additionally, it analyzed spatiotemporal variations these relationship indices. Furthermore, an IML model (XGBoost-SHAP) was interpret RSEI. results indicate that (1) levels 2022 were primarily moderate; (2) compared NDVI-RSEI, SAVI-RSEI is more susceptible soil factors, while kNDVI-RSEI exhibits lower saturation tendency; (3) potential evapotranspiration, land cover, elevation drivers variations, affecting environment western, southeastern, northeastern parts area. XGBoost-SHAP approach provides valuable insights promoting regional

Language: Английский

Citations

0
Detection of fast-changing intra-seasonal vegetation dynamics of drylands using solar-induced chlorophyll fluorescence (SIF) DOI Creative Commons
Jiaming Wen, Giulia Tagliabue, Micol Rossini

et al.

Biogeosciences, Journal Year: 2025, Volume and Issue: 22(8), P. 2049 - 2067

Published: April 25, 2025

Abstract. Dryland ecosystems are the habitat supporting 2 billion people on Earth, and they strongly impact global terrestrial carbon sink. Vegetation growth in drylands is mainly controlled by water availability with strong intra-seasonal variability. Timely of information at such scales (e.g., from days to weeks) essential for early warning potential catastrophic impacts emerging climate extremes crops natural vegetation. However, large-scale monitoring vegetation dynamics has been very challenging drylands. Satellite solar-induced chlorophyll fluorescence (SIF) emerged as a promising tool characterize spatiotemporal photosynthetic uptake detect dynamics. few studies have evaluated its capability detecting fast-changing advantages over traditional approaches based indices (VIs). To fill this knowledge gap, study utilized vast dryland Horn Africa (HoA) testbed their inferred satellite SIF. The HoA an ideal because highly dynamic responses short-term environmental changes. satellite-data-based analysis was corroborated unique situ SIF dataset collected Kenya – so far, only ground time series continent Africa. We found that TROPOspheric Monitoring Instrument (TROPOMI) daily revisit frequency identified week-to-week variations both shrublands grasslands; rapidly changing corresponded up- downregulation fluctuations variables air temperature, vapor pressure deficit, soil moisture). neither reconstructed products nor near-infrared reflectance (NIRv) Moderate Resolution Imaging Spectroradiometer (MODIS), which widely used literature, able capture variations. same findings hold site scale, where we TROPOMI revealed two separate within-season cycles response extreme moisture rainfall amount duration, consistent measurements. This generates novel insights evaluation sensitivities, enabling development predictive scalable understanding how may respond future change informing design effective systems

Language: Английский

Citations

0