Articles | Volume 22, issue 11
https://doi.org/10.5194/bg-22-2637-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-22-2637-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Duration of vegetation green-up response to snowmelt on the Tibetan Plateau
Jingwen Ni
Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200241, China
School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities, Ministry of Natural Resources, Shanghai 200241, China
Jin Chen
State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
Yao Tang
Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200241, China
School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities, Ministry of Natural Resources, Shanghai 200241, China
Jingyi Xu
Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200241, China
School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities, Ministry of Natural Resources, Shanghai 200241, China
Zhejiang Tiantong Forest Ecosystem National Observation and Research Station, School of Ecological and Environmental Sciences, East China Normal University, Shanghai 200241, China
Jiahui Xu
Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200241, China
School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities, Ministry of Natural Resources, Shanghai 200241, China
Linxin Dong
Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200241, China
School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities, Ministry of Natural Resources, Shanghai 200241, China
Qingyu Gu
Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200241, China
School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities, Ministry of Natural Resources, Shanghai 200241, China
Bailang Yu
Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200241, China
School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities, Ministry of Natural Resources, Shanghai 200241, China
Jianping Wu
Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200241, China
School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities, Ministry of Natural Resources, Shanghai 200241, China
Yan Huang
CORRESPONDING AUTHOR
Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200241, China
School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities, Ministry of Natural Resources, Shanghai 200241, China
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Zixuan Pei, Xiaolin Zhu, Yang Hu, Jin Chen, and Xiaoyue Tan
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-142, https://doi.org/10.5194/essd-2025-142, 2025
Preprint under review for ESSD
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Nighttime light (NTL) data aids urban planning and disaster monitoring. Our study introduces HDNTL, a daily NTL dataset for 653 major cities (population >1 M by 2025) from 2012–2024, enhancing NASA's VNP46A2 accuracy by correcting spatial mismatches, angular effects, and filling small missing holes via spatiotemporal interpolation. Validated against high-resolution NTL datasets and ground-truth records, HDNTL enables precise monitoring of nighttime human activities.
Anyao Jiang, Xin Meng, Yan Huang, and Guitao Shi
The Cryosphere, 18, 5347–5364, https://doi.org/10.5194/tc-18-5347-2024, https://doi.org/10.5194/tc-18-5347-2024, 2024
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Landlocked lakes are crucial to the Antarctic ecosystem and sensitive to climate change. Limited research on their distribution prompted us to develop an automated detection process using deep learning and multi-source satellite imagery. This allowed us to accurately determine the landlocked lake open water (LLOW) area in Antarctica, generating high-resolution time series data. We find that the changes in positive and negative degree days predominantly drive variations in the LLOW area.
Jiahui Xu, Yao Tang, Linxin Dong, Shujie Wang, Bailang Yu, Jianping Wu, Zhaojun Zheng, and Yan Huang
The Cryosphere, 18, 1817–1834, https://doi.org/10.5194/tc-18-1817-2024, https://doi.org/10.5194/tc-18-1817-2024, 2024
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Understanding snow phenology (SP) and its possible feedback are important. We reveal spatiotemporal heterogeneous SP on the Tibetan Plateau (TP) and the mediating effects from meteorological, topographic, and environmental factors on it. The direct effects of meteorology on SP are much greater than the indirect effects. Topography indirectly effects SP, while vegetation directly effects SP. This study contributes to understanding past global warming and predicting future trends on the TP.
Zhengyi Hu, Wei Jiang, Yuzhen Yan, Yan Huang, Xueyuan Tang, Lin Li, Florian Ritterbusch, Guo-Min Yang, Zheng-Tian Lu, and Guitao Shi
The Cryosphere, 18, 1647–1652, https://doi.org/10.5194/tc-18-1647-2024, https://doi.org/10.5194/tc-18-1647-2024, 2024
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The age of the surface blue ice in the Grove Mountains area is dated to be about 140 000 years, and one meteorite found here is 260 000 years old. It is inferred that the Grove Mountains blue-ice area holds considerable potential for paleoclimate studies.
Yan Huang, Jiahui Xu, Jingyi Xu, Yelei Zhao, Bailang Yu, Hongxing Liu, Shujie Wang, Wanjia Xu, Jianping Wu, and Zhaojun Zheng
Earth Syst. Sci. Data, 14, 4445–4462, https://doi.org/10.5194/essd-14-4445-2022, https://doi.org/10.5194/essd-14-4445-2022, 2022
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Reliable snow cover information is important for understating climate change and hydrological cycling. We generate long-term daily gap-free snow products over the Tibetan Plateau (TP) at 500 m resolution from 2002 to 2021 based on the hidden Markov random field model. The accuracy is 91.36 %, and is especially improved during snow transitional period and over complex terrains. This dataset has great potential to study climate change and to facilitate water resource management in the TP.
Song Shu, Hongxing Liu, Richard A. Beck, Frédéric Frappart, Johanna Korhonen, Minxuan Lan, Min Xu, Bo Yang, and Yan Huang
Hydrol. Earth Syst. Sci., 25, 1643–1670, https://doi.org/10.5194/hess-25-1643-2021, https://doi.org/10.5194/hess-25-1643-2021, 2021
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This study comprehensively evaluated 11 satellite radar altimetry missions (including their official retrackers) for lake water level retrieval and developed a strategy for constructing consistent long-term water level records for inland lakes. It is a two-step bias correction and normalization procedure. First, we use Jason-2 as the initial reference to form a consistent TOPEX/Poseidon–Jason series. Then, we use this as the reference to remove the biases with other radar altimetry missions.
Zuoqi Chen, Bailang Yu, Chengshu Yang, Yuyu Zhou, Shenjun Yao, Xingjian Qian, Congxiao Wang, Bin Wu, and Jianping Wu
Earth Syst. Sci. Data, 13, 889–906, https://doi.org/10.5194/essd-13-889-2021, https://doi.org/10.5194/essd-13-889-2021, 2021
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An extended time series (2000–2018) of NPP-VIIRS-like nighttime light (NTL) data was proposed through a cross-sensor calibration from DMSP-OLS NTL data (2000–2012) and NPP-VIIRS NTL data (2013–2018). Compared with the annual composited NPP-VIIRS NTL data, our extended NPP-VIIRS-like NTL data have a high accuracy and also show a good spatial pattern and temporal consistency. It could be a useful proxy to monitor the dynamics of urbanization for a longer time period compared to existing NTL data.
W. Wang, X. Chen, X. Cao, and J. Chen
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 221–225, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-221-2020, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-221-2020, 2020
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Remote Sensing: Terrestrial
Detection of fast-changing intra-seasonal vegetation dynamics of drylands using solar-induced chlorophyll fluorescence (SIF)
Geographic patterns of upward shifts in treeline vegetation across western North America, 1984–2017
Field heterogeneity of soil texture controls leaf water potential spatial distribution predicted from UAS-based vegetation indices in non-irrigated vineyards
Grassland yield estimations – potentials and limitations of remote sensing, process-based modelling and field measurements
Assessing evapotranspiration dynamics across central Europe in the context of land-atmosphere drivers
Remote sensing reveals fire-driven enhancement of a C4 invasive alien grass on a small Mediterranean volcanic island
Divergent biophysical responses of western United States forests to wildfire driven by eco-climatic gradients
Synergistic use of Sentinel-2 and UAV-derived data for plant fractional cover distribution mapping of coastal meadows with digital elevation models
Data-based investigation of the effects of canopy structure and shadows on chlorophyll fluorescence in a deciduous oak forest
Evaluation of five models for constructing forest NPP–age relationships in China based on 3121 field survey samples
Reviews and syntheses: Remotely sensed optical time series for monitoring vegetation productivity
Geographically divergent trends in snow disappearance timing and fire ignitions across boreal North America
Dune belt restoration effectiveness assessed by UAV topographic surveys (northern Adriatic coast, Italy)
High-resolution data reveal a surge of biomass loss from temperate and Atlantic pine forests, contextualizing the 2022 fire season distinctiveness in France
Local environmental context drives heterogeneity of early succession dynamics in alpine glacier forefields
Jiaming Wen, Giulia Tagliabue, Micol Rossini, Francesco Pietro Fava, Cinzia Panigada, Lutz Merbold, Sonja Leitner, and Ying Sun
Biogeosciences, 22, 2049–2067, https://doi.org/10.5194/bg-22-2049-2025, https://doi.org/10.5194/bg-22-2049-2025, 2025
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Solar-induced chlorophyll fluorescence (SIF), a tiny optical signal emitted from the core photosynthetic machinery, has emerged as a promising tool to evaluate vegetation growth from satellites. We find satellite SIF can capture intra-seasonal (i.e., from days to weeks) vegetation dynamics of dryland ecosystems, while greenness-based vegetation indices cannot. This study generates novel insights for developing effective real-time vegetation monitoring systems to inform climate risk management.
Joanna L. Corimanya, Daniel Jiménez-García, Xingong Li, and A. Townsend Peterson
EGUsphere, https://doi.org/10.5194/egusphere-2025-1203, https://doi.org/10.5194/egusphere-2025-1203, 2025
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Treelines—the highest elevations where trees can grow—are shifting upward as the climate warms. Using nearly 40 years of satellite imagery, we analyzed treeline movement across 115 high mountain peaks from Canada to Central America. We found that treeline shifts are not uniform and are most pronounced in tropical regions, where few studies have been conducted. These results highlight the need for more research in these areas to better understand how climate change reshapes mountain ecosystems.
Louis Delval, Jordan Bates, François Jonard, and Mathieu Javaux
Biogeosciences, 22, 513–534, https://doi.org/10.5194/bg-22-513-2025, https://doi.org/10.5194/bg-22-513-2025, 2025
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The accurate quantification of grapevine water status is crucial for winemakers as it significantly impacts wine quality. It is acknowledged that within a single vineyard, the variability of grapevine water status can be significant. The within-field spatial distribution of soil hydraulic conductance and weather conditions are the primary factors governing the leaf water potential spatial heterogeneity and extent observed in non-irrigated vineyards, and their effects are concomitant.
Sophie Reinermann, Carolin Boos, Andrea Kaim, Anne Schucknecht, Sarah Asam, Ursula Gessner, Sylvia H. Annuth, Thomas M. Schmitt, Thomas Koellner, and Ralf Kiese
EGUsphere, https://doi.org/10.5194/egusphere-2024-4087, https://doi.org/10.5194/egusphere-2024-4087, 2025
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Grasslands shape the landscape in many parts of the world and serve as the main source of fodder for livestock. There is a lack of comprehensive data on grassland yield, though highly valuable for authorities and research. By applying three approaches to estimate grassland yields, namely a satellite data model, a biogeochemical model and a field measurements approach, we provide annual grassland yield maps for the Ammer region in 2019 and highlight potentials and limitations of the approaches.
Anke Fluhrer, Martin Baur, María Piles, Bagher Bayat, Mehdi Rahmati, David Chaparro, Clémence Dubois, Florian Hellwig, Carsten Montzka, Angelika Kübert, Marlin Mueller, Isabel Augscheller, Francois Jonard, Konstantin Schellenberg, and Thomas Jagdhuber
EGUsphere, https://doi.org/10.5194/egusphere-2024-3386, https://doi.org/10.5194/egusphere-2024-3386, 2024
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This study compares established evapotranspiration products in Central Europe and evaluates their multi-seasonal performance during wet & drought phases between 2017–2020 together with important soil-plant-atmosphere drivers. Results show that SEVIRI, ERA5-land & GLEAM perform best compared to ICOS measurements. During moisture limited drought years, ET is decreasing due to decreasing soil moisture and increasing vapor pressure deficit, while in other years ET is mainly controlled by VPD.
Riccardo Guarino, Daniele Cerra, Renzo Zaia, Alessandro Chiarucci, Pietro Lo Cascio, Duccio Rocchini, Piero Zannini, and Salvatore Pasta
Biogeosciences, 21, 2717–2730, https://doi.org/10.5194/bg-21-2717-2024, https://doi.org/10.5194/bg-21-2717-2024, 2024
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The severity and the extent of a large fire event that occurred on the small volcanic island of Stromboli (Aeolian archipelago, Italy) on 25–26 May 2022 were evaluated through remotely sensed data to assess the short-term effect of fire on local plant communities. For the first time, we documented the outstanding after-fire resilience of an invasive alien species, Saccharum biflorum, which is a rhizomatous C4 perennial grass introduced on the island in the nineteenth century.
Surendra Shrestha, Christopher A. Williams, Brendan M. Rogers, John Rogan, and Dominik Kulakowski
Biogeosciences, 21, 2207–2226, https://doi.org/10.5194/bg-21-2207-2024, https://doi.org/10.5194/bg-21-2207-2024, 2024
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Here, we generated chronosequences of leaf area index (LAI) and surface albedo as a function of time since fire to demonstrate the differences in the characteristic trajectories of post-fire biophysical changes among seven forest types and 21 level III ecoregions of the western United States (US) using satellite data from different sources. We also demonstrated how climate played the dominant role in the recovery of LAI and albedo 10 and 20 years after wildfire events in the western US.
Ricardo Martínez Prentice, Miguel Villoslada, Raymond D. Ward, Thaisa F. Bergamo, Chris B. Joyce, and Kalev Sepp
Biogeosciences, 21, 1411–1431, https://doi.org/10.5194/bg-21-1411-2024, https://doi.org/10.5194/bg-21-1411-2024, 2024
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Despite hosting a wide range of ecosystem services, coastal wetlands face threats from global changes. This study models the plant fractional cover of plant communities in Estonian coastal meadows with a synergistic use of drone, satellite imagery and digital elevation models. This approach highlights the significant contribution of digital elevation models to multispectral data, enabling the modelling of heterogeneous plant community distributions in such wetlands.
Hamadou Balde, Gabriel Hmimina, Yves Goulas, Gwendal Latouche, Abderrahmane Ounis, and Kamel Soudani
Biogeosciences, 21, 1259–1276, https://doi.org/10.5194/bg-21-1259-2024, https://doi.org/10.5194/bg-21-1259-2024, 2024
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We show that FyieldLIF was not correlated with SIFy at the diurnal timescale, and the diurnal patterns in SIF and PAR did not match under clear-sky conditions due to canopy structure. Φk was sensitive to canopy structure. RF models show that Φk can be predicted using reflectance in different bands. RF models also show that FyieldLIF was more sensitive to reflectance and radiation than SIF and SIFy, indicating that the combined effect of reflectance bands could hide the SIF physiological trait.
Peng Li, Rong Shang, Jing M. Chen, Mingzhu Xu, Xudong Lin, Guirui Yu, Nianpeng He, and Li Xu
Biogeosciences, 21, 625–639, https://doi.org/10.5194/bg-21-625-2024, https://doi.org/10.5194/bg-21-625-2024, 2024
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The amount of carbon that forests gain from the atmosphere, called net primary productivity (NPP), changes a lot with age. These forest NPP–age relationships could be modeled from field survey data, but we are not sure which model works best. Here we tested five different models using 3121 field survey samples in China, and the semi-empirical mathematical (SEM) function was determined as the optimal. The relationships built by SEM can improve China's forest carbon modeling and prediction.
Lammert Kooistra, Katja Berger, Benjamin Brede, Lukas Valentin Graf, Helge Aasen, Jean-Louis Roujean, Miriam Machwitz, Martin Schlerf, Clement Atzberger, Egor Prikaziuk, Dessislava Ganeva, Enrico Tomelleri, Holly Croft, Pablo Reyes Muñoz, Virginia Garcia Millan, Roshanak Darvishzadeh, Gerbrand Koren, Ittai Herrmann, Offer Rozenstein, Santiago Belda, Miina Rautiainen, Stein Rune Karlsen, Cláudio Figueira Silva, Sofia Cerasoli, Jon Pierre, Emine Tanır Kayıkçı, Andrej Halabuk, Esra Tunc Gormus, Frank Fluit, Zhanzhang Cai, Marlena Kycko, Thomas Udelhoven, and Jochem Verrelst
Biogeosciences, 21, 473–511, https://doi.org/10.5194/bg-21-473-2024, https://doi.org/10.5194/bg-21-473-2024, 2024
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We reviewed optical remote sensing time series (TS) studies for monitoring vegetation productivity across ecosystems. Methods were categorized into trend analysis, land surface phenology, and assimilation into statistical or dynamic vegetation models. Due to progress in machine learning, TS processing methods will diversify, while modelling strategies will advance towards holistic processing. We propose integrating methods into a digital twin to improve the understanding of vegetation dynamics.
Thomas D. Hessilt, Brendan M. Rogers, Rebecca C. Scholten, Stefano Potter, Thomas A. J. Janssen, and Sander Veraverbeke
Biogeosciences, 21, 109–129, https://doi.org/10.5194/bg-21-109-2024, https://doi.org/10.5194/bg-21-109-2024, 2024
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In boreal North America, snow and frozen ground prevail in winter, while fires occur in summer. Over the last 20 years, the northwestern parts have experienced earlier snow disappearance and more ignitions. This is opposite to the southeastern parts. However, earlier ignitions following earlier snow disappearance timing led to larger fires across the region. Snow disappearance timing may be a good proxy for ignition timing and may also influence important atmospheric conditions related to fires.
Regine Anne Faelga, Luigi Cantelli, Sonia Silvestri, and Beatrice Maria Sole Giambastiani
Biogeosciences, 20, 4841–4855, https://doi.org/10.5194/bg-20-4841-2023, https://doi.org/10.5194/bg-20-4841-2023, 2023
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A dune restoration project on the northern Adriatic coast (Ravenna, Italy) was assessed using UAV monitoring. Structure-from-motion photogrammetry, elevation differencing, and statistical analysis were used to quantify dune development in terms of sand volume and vegetation cover change. Results show that the installed fence has been effective as there was significant sand accumulation, embryo dune development, and a decrease in blowout features due to increased vegetation colonization.
Lilian Vallet, Martin Schwartz, Philippe Ciais, Dave van Wees, Aurelien de Truchis, and Florent Mouillot
Biogeosciences, 20, 3803–3825, https://doi.org/10.5194/bg-20-3803-2023, https://doi.org/10.5194/bg-20-3803-2023, 2023
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This study analyzes the ecological impact of the 2022 summer fire season in France by using high-resolution satellite data. The total biomass loss was 2.553 Mt, equivalent to a 17 % increase of the average natural mortality of all French forests. While Mediterranean forests had a lower biomass loss, there was a drastic increase in burned area and biomass loss over the Atlantic pine forests and temperate forests. This result revisits the distinctiveness of the 2022 fire season.
Arthur Bayle, Bradley Z. Carlson, Anaïs Zimmer, Sophie Vallée, Antoine Rabatel, Edoardo Cremonese, Gianluca Filippa, Cédric Dentant, Christophe Randin, Andrea Mainetti, Erwan Roussel, Simon Gascoin, Dov Corenblit, and Philippe Choler
Biogeosciences, 20, 1649–1669, https://doi.org/10.5194/bg-20-1649-2023, https://doi.org/10.5194/bg-20-1649-2023, 2023
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Glacier forefields have long provided ecologists with a model to study patterns of plant succession following glacier retreat. We used remote sensing approaches to study early succession dynamics as it allows to analyze the deglaciation, colonization, and vegetation growth within a single framework. We found that the heterogeneity of early succession dynamics is deterministic and can be explained well by local environmental context. This work has been done by an international consortium.
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Short summary
The effect of snowmelt on vegetation is not immediate but has a mean response lag of 38.5 d. As precipitation and snowmelt increase, the response time shortens. More complex than these factors, temperature shortens the response time in colder regions while lengthening it in warmer areas. Furthermore, vegetation in arid regions is more dependent on water than heat, and low-vegetation areas rely more on sub-snow habitats than external climatic factors.
The effect of snowmelt on vegetation is not immediate but has a mean response lag of 38.5 d. As...
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