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Remote Sensing and Modelling of Terrestrial Ecosystems Functioning

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: 30 December 2025 | Viewed by 2834

Special Issue Editors


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Guest Editor
Institute of Bio- and Geosciences: Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
Interests: vegetation properties and functioning; radiative transfer modelling; surafce energy balance; biogeochemical modelling; earth observation; ecohydrology
Institute of Bio- and Geosciences: Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany
Interests: satellite remote sensing (SAR and optical) of vegetation; process-based modeling of vegetation productions; radiative transfer modeling
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Guest Editor
School of Geography, Nanjing Normal University, Nanjing 210023, China
Interests: quantitative remote sensing; radiative transfer modelling; plant-climate interaction via photosynthetic and hydrologic processes
Special Issues, Collections and Topics in MDPI journals
Luxembourg Institute of Science and Technology, 4362 Esch-sur-Alzette, Luxembourg
Interests: thermal infrared remote sensing; ecohydroloy; ecosystem processes
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Plants are vital components of nearly all terrestrial ecosystems (i.e., forests, grasslands, and croplands). Water and carbon exchanges between plants and the atmosphere are two fundamental traits of vegetation functioning [i.e., evapotranspiration (ET) and photosynthesis or gross primary productivity (GPP)].

ET comprises plant transpiration, soil evaporation and evaporation of intercepted precipitation and provides the primary linkage between energy and hydrologic flux in the ecosystem. It quantifies the water loss from the Earth surface to the atmosphere. ET, as the atmosphere’s water source, controls basin surface water and affects regional rainfall patterns. GPP controls some of the crucial functions in the ecosystem, such as respiration and growth. It demonstrates the efficiency of the exchange of carbon dioxide in the surface-atmosphere continuum and sustains the food web by providing the total carbohydrate matter and, therefore, plays an essential role in human life.

Remote sensing provides a synoptic view of the plants from space. It contains rich information on the canopy spectra (reflectance/radiance) at a large spatio-temporal scale. The observed spectra carry valuable information about the biophysical and biochemical properties of the leaf composition and the canopy structure that can be employed for remote sensing of ET and GPP across ecosystems by means of statistical and physical models. The availability of a wide range of active and passive sensors, covering various portions of the electromagnetic spectrum  (from optical to thermal to microwave) at different resolutions, has accelerated the local to global monitoring of vegetation. Moreover, several algorithms have been developed to extract sun-induced fluorescence (SIF) from remote sensing, which is the radiation in the far-red wavelength range between 650 and 800 nm emitted by plants. SIF is closely connected to the carbon assimilation of vegetation.

This special issue aims at studies covering various vegetation functioning estimations in forests, grasslands, and croplands using remote sensing observations. Topics may cover a broad range of approaches (from simple statistical approach to more comprehensive physical modelling), scales (from laboratory experiments, point estimates, watershed and ecosystem levels), and time-span (from single data and image to longer time-series analysis). Articles may address, but are not limited to, the following topics:

  • Vegetation biophysical and biochemical properties (e.g., LAI, Cab) estimations
  • Satellite ET monitoring
  • Satellite GPP estimation
  • Combined use of optical, thermal, and SIF data for ET and GPP estimation
  • ET and GPP products evaluation and accuracy assessment
  • Heatwave and drought analysis based on ET and GPP estimates
  • Radiative transfer modelling
  • Vegetation index analysis
  • Land surface temperature estimation
  • Surface energy balance approach
  • Use of drone and airborne data for ET and GPP estimation
  • Bias correction of ET and GPP estimates in dry episodes

Research articles, review articles as well as short communications are invited. Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere.

Dr. Bagher Bayat
Dr. Rahul Raj
Prof. Dr. Peiqi Yang
Dr. Tian Hu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • terrestrial ecosystems
  • remote sensing
  • vegetation functioning
  • ET and GPP
  • estimation and modelling
  • accuracy assessment

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Published Papers (3 papers)

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Research

30 pages, 24355 KiB  
Article
Bioclimatic Characterization of Jalisco (Mexico) Based on a High-Resolution Climate Database and Its Relationship with Potential Vegetation
by Norma-Yolanda Ochoa-Ramos, Miguel Ángel Macías-Rodríguez, Joaquín Giménez de Azcárate, Ramón Álvarez-Esteban, Ángel Penas and Sara del Río
Remote Sens. 2025, 17(7), 1232; https://doi.org/10.3390/rs17071232 - 30 Mar 2025
Viewed by 327
Abstract
Bioclimatic classifications provide critical insights into the relationships between climatic variables and the geographic distribution of organisms. Advances in high-resolution climate data, geobotanical integration, and spatial analysis techniques have improved the delineation of bioclimatic units, enabling more precise characterization of terrestrial ecosystems. This [...] Read more.
Bioclimatic classifications provide critical insights into the relationships between climatic variables and the geographic distribution of organisms. Advances in high-resolution climate data, geobotanical integration, and spatial analysis techniques have improved the delineation of bioclimatic units, enabling more precise characterization of terrestrial ecosystems. This study characterizes the bioclimatic conditions of Jalisco, Mexico, through the identification of bioclimatic units and variants using bioclimatic indices and parameters. High-resolution climate data (1980–2018) from the CHELSA database and GIS-based spatial analysis were employed to delineate bioclimatic patterns and their correlation with climatophyllous potential vegetation. The results identified one macrobioclimate and two bioclimates—Tropical pluviseasonal (56.62%) and Tropical xeric (43.38%)—as well as two bioclimatic variants, six thermotypes, and seven ombrotypes. Notably, 49.84% of the territory exhibits bioclimatic variants, and a total of 42 isobioclimates were associated with 14 types of climatophyllous potential vegetation. These findings provide a foundation for understanding vegetation dynamics and support territorial planning and land management. The integration of remote sensing and bioclimatic analysis enhances the identification of spatial heterogeneity in climate–vegetation relationships, facilitating applications in ecological modeling, drought assessment, and conservation planning. This study contributes to ongoing research on terrestrial ecosystem functioning, aligning with current advancements in remote sensing-based environmental analysis. Full article
(This article belongs to the Special Issue Remote Sensing and Modelling of Terrestrial Ecosystems Functioning)
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26 pages, 19628 KiB  
Article
Analysis of the Spatiotemporal Characteristics of Gross Primary Production and Its Influencing Factors in Arid Regions Based on Improved SIF and MLR Models
by Wei Liu, Ali Mamtimin, Yu Wang, Yongqiang Liu, Hajigul Sayit, Chunrong Ji, Jiacheng Gao, Meiqi Song, Ailiyaer Aihaiti, Cong Wen, Fan Yang, Chenglong Zhou and Wen Huo
Remote Sens. 2025, 17(5), 811; https://doi.org/10.3390/rs17050811 - 25 Feb 2025
Viewed by 448
Abstract
In this study of constructing gross primary production (GPP) based on solar-induced chlorophyll fluorescence (SIF) and analyzing its spatial–temporal characteristics and influencing factors, numerous challenges are encountered, especially in arid regions with fragile ecologies. Coupling SIF with other factors to construct the GPP [...] Read more.
In this study of constructing gross primary production (GPP) based on solar-induced chlorophyll fluorescence (SIF) and analyzing its spatial–temporal characteristics and influencing factors, numerous challenges are encountered, especially in arid regions with fragile ecologies. Coupling SIF with other factors to construct the GPP and elucidating the influencing mechanisms of environmental factors could offer a novel theoretical method for the comprehensive analysis of GPP in arid regions. Therefore, we used the GPP station data from three different ecosystems (grasslands, farmlands, and desert vegetation) as well as the station and satellite data of environmental factors (including photosynthetically active radiation (PAR), a vapor pressure deficit (VPD), the air temperature (Tair), soil temperature (Tsoil), and soil moisture content (SWC)), and combined these with the TROPOMI SIF (RTSIF, generated through the reconstruction of SIF from the Sentinel-5P sensor), whose spatiotemporal precision was improved, the mechanistic light reaction model (MLR model), and different weather conditions. Then, we explored the spatiotemporal characteristics of GPP and its driving factors in local areas of Xinjiang. The results indicated that the intra-annual variation of GPP showed an inverted “U” shape, with the peak from June to July. The spatial attributes were positively correlated with vegetation coverage and sun radiation. Moreover, inverting GPP referred to the process of estimating the GPP of an ecosystem through models and remote sensing data. Based on the MLR model and RTSIF, the inverted GPP could capture more than 80% of the GPP changes in the three ecosystems. Furthermore, in farmland areas, PAR, VPD, Tair, and Tsoil jointly dominate GPP under sunny, cloudy, and overcast conditions. In grassland areas, PAR was the main influencing factor of GPP under all weather conditions. In desert vegetation areas, the dominant influencing factor of GPP was PAR on sunny days, VPD and Tair on cloudy days, and Tair on overcast days. Regarding the spatial correlation, the high spatial correlation between PAR, VPD, Tair, Tsoil, and GPP was observed in regions with dense vegetation coverage and low radiation. Similarly, the strong spatial correlation between SWC and GPP was found in irrigated farmland areas. The characteristics of a low spatial correlation between GPP and environmental factors were the opposite. In addition, it was worth noting that the impact of various environmental factors on GPP in farmland areas was comprehensively expressed based on a linear pattern. However, in grassland and desert vegetation areas, the impact of VPD on GPP was expressed based on a linear pattern, while the impact of other factors was more accurately represented through a non-linear pattern. This study demonstrated that SIF data combined with the MLR model effectively estimated GPP and revealed its spatial patterns and driving factors. These findings may serve as a foundation for developing targeted carbon reduction strategies in arid regions, contributing to improved regional carbon management. Full article
(This article belongs to the Special Issue Remote Sensing and Modelling of Terrestrial Ecosystems Functioning)
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24 pages, 7033 KiB  
Article
geeSSEBI: Evaluating Actual Evapotranspiration Estimated with a Google Earth Engine Implementation of S-SEBI
by Jerzy Piotr Kabala, Jose Antonio Sobrino, Virginia Crisafulli, Dražen Skoković and Giovanna Battipaglia
Remote Sens. 2025, 17(3), 395; https://doi.org/10.3390/rs17030395 - 24 Jan 2025
Viewed by 886
Abstract
Quantifying and mapping evapotranspiration (ET) from land surfaces is crucial in the context of climate change. For decades, remote sensing data have been utilized to estimate ET, leading to the development of numerous algorithms. However, their application is still non-trivial, mainly due to [...] Read more.
Quantifying and mapping evapotranspiration (ET) from land surfaces is crucial in the context of climate change. For decades, remote sensing data have been utilized to estimate ET, leading to the development of numerous algorithms. However, their application is still non-trivial, mainly due to practical constraints. This paper introduces geeSSEBI, a Google Earth Engine implementation of the S-SEBI (Simplified Surface Energy Balance Index) model, for deriving ET from Landsat data and ERA5-land radiation. The source code and a graphical user interface implemented as a Google Earth Engine application are provided. The model ran on 871 images, and the estimates were evaluated against multiyear data of four eddy covariance towers belonging to the ICOS network, representative of both forests and agricultural landscapes. The model showed an RMSE of approximately 1 mm/day, and a significant correlation with the observed values, at all the sites. A procedure to upscale the data to monthly is proposed and tested as well, and its accuracy evaluated. Overall, the model showed acceptable accuracy, while performing better on forest ecosystems than on agricultural ones, especially at daily and monthly timescales. This implementation is particularly valuable for mapping evapotranspiration in data-scarce environments by utilizing Landsat archives and ERA5-land radiation estimates. Full article
(This article belongs to the Special Issue Remote Sensing and Modelling of Terrestrial Ecosystems Functioning)
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