Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Menu

Journal Browser

Remote Sensing and Modelling of Terrestrial Ecosystems Functioning

Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

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: closed (31 May 2026) | Viewed by 15360

Share This Special Issue

Editors

Dr. Bagher Bayat

E-Mail Website
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

Dr. Rahul Raj

E-Mail Website
Guest Editor

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
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Peiqi Yang

E-Mail Website
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

Dr. Tian Hu

E-Mail Website
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (7 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Review

32 pages, 8318 KB

Open AccessArticle

The Role of Solar-Induced Chlorophyll Fluorescence (SIF) in the Mechanistic Simulation of Eco-Hydrological Processes

by Aofan Cui, Yunfei Wang, Qiting Zuo, Xinyu Mao, Linlin Li, Jingjing Yang, Xiongbiao Peng, Zhunqiao Liu, Xiaoliang Lu, Qiang Yu, Huanjie Cai, Yijian Zeng and Zhongbo Su

Remote Sens. 2026, 18(9), 1364; https://doi.org/10.3390/rs18091364 - 28 Apr 2026

Viewed by 718

Abstract

Accurate quantification of ecohydrological processes is essential for effective water and carbon management in terrestrial ecosystems. Traditional simulations mainly rely on mechanistic models, yet their accuracy is often limited by inconsistencies in representing physical processes and uncertainties in parameterization. Integrating remote sensing signals offers a promising way to reduce these uncertainties and enhance model applicability. In this study, in-situ observations from a wheat cropland in the Guanzhong Plain were used to simulate gross primary productivity (GPP) and latent heat flux (LE) by comparing a forward model (STEMMUS-SCOPE) with a remote sensing-driven inverse model (STEMMUS-MLR). We further examined the role of solar-induced chlorophyll fluorescence (SIF), an emerging proxy for photosynthesis, as an input to improve mechanistic modeling of GPP and LE. Results show that STEMMUS-MLR outperformed STEMMUS-SCOPE in estimating water and carbon fluxes, demonstrating that incorporating SIF effectively reduces bias associated with uncertainties in parameters and forcing data. The contribution of SIF was quantified using Random Forest regression and Shapley additive explanations (SHAP), revealing that SIF markedly reduced the dependence of GPP and LE simulations on shortwave radiation (SW), air temperature (Ta), and leaf area index (LAI). These findings highlight the critical role of SIF in ecohydrological modeling of semi-arid cropland ecosystems and provide a scientific basis for advancing process understanding and improving the precision management of water and carbon budgets in terrestrial ecosystems. Full article

(This article belongs to the Special Issue Remote Sensing and Modelling of Terrestrial Ecosystems Functioning)

► Show Figures

Figure 1

27 pages, 5853 KB

Open AccessArticle

Evaluation of a LUE Model and Various Water Scalars Based on Eddy Covariance Data from 13 Forest Sites Across Europe

by Theofilos Vanikiotis, Stavros Stagakis and Aris Kyparissis

Remote Sens. 2026, 18(4), 548; https://doi.org/10.3390/rs18040548 - 9 Feb 2026

Viewed by 563

Abstract

Light use efficiency (LUE) models are widely used to estimate gross primary productivity (GPP) because they provide strong accuracy while maintaining low complexity. The aim of this study is (a) to evaluate the performance of a LUE model (sCASE) and (b) to compare the performance of several alternative water scalars. The analyses are done using GPP measurements from thirteen eddy covariance sites across Europe, corresponding to different forest types. Daily GPP estimates produced by sCASE were highly accurate for most sites (average R² = 0.750 and average RMSE = 2.317 g C m⁻² d⁻¹), matching the performance of other widely used LUE models in the literature. All three scalars were essential for maintaining model accuracy, although their relative importance varied among sites. The developmental scalar, which is not incorporated in most productivity models, was particularly important for accurately estimating GPP in deciduous species. Among the ten water scalars tested, those based on simple water balance calculations performed best in water-limited sites, whereas the VPD-based scalar performed better in sites without water limitation. The EF (evaporative fraction) scalar showed high accuracy at some sites across both water status categories but very low accuracy at others. For large-scale applications, water scalars based on MODIS indices offer the advantage of global coverage, which can outweigh their lower accuracy relative to other scalars. Full article

(This article belongs to the Special Issue Remote Sensing and Modelling of Terrestrial Ecosystems Functioning)

► Show Figures

Figure 1

30 pages, 24355 KB

Open AccessArticle

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

Cited by 2 | Viewed by 3746

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 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)

► Show Figures

Figure 1

26 pages, 19628 KB

Open AccessArticle

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

Cited by 1 | Viewed by 1456

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 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)

► Show Figures

Figure 1

24 pages, 7033 KB

Open AccessArticle

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

Cited by 2 | Viewed by 3545

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 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)

► Show Figures

Graphical abstract

Review

Jump to: Research

36 pages, 4683 KB

Open AccessReview

Machine Learning for Satellite Solar-Induced Fluorescence: Retrieval, Reconstruction, Downscaling, and Applications

by Jochem Verrelst, Yuxin Zhang, Miguel Morata, Emma De Clerck and Leizhen Liu

Remote Sens. 2026, 18(4), 553; https://doi.org/10.3390/rs18040553 - 9 Feb 2026

Cited by 3 | Viewed by 1074

Abstract

Satellite-observed solar-induced chlorophyll fluorescence (SIF) provides a direct radiative link between solar radiation, photosystem de-excitation and vegetation photosynthetic activity. As multiple satellite missions now deliver global SIF products, machine learning (ML) has become a key tool for: (i) flexible nonlinear SIF retrieval, (ii) spatial reconstruction and downscaling of SIF fields, (iii) full-spectrum SIF reconstruction beyond narrow absorption windows, and (iv) data-driven analysis of the SIF–gross primary production (GPP) relationship. In addition, ML methods are increasingly used for: (v) uncertainty quantification (UQ) along the SIF information chain, and (vi) emulation (i.e., surrogate modelling) of radiative transfer models (RTMs) to accelerate computationally demanding SIF workflows. This review provides a conceptual and methodological survey of recent ML applications across the satellite SIF processing chain, summarises emerging products and methods, and highlights open challenges in uncertainty treatment, spectral reconstruction, and hybrid RTM–ML approaches. Particular emphasis is placed on the upcoming ESA FLEX mission, planned for launch in 2026, which will deliver multi-band SIF observations optimised for photosynthesis monitoring. While FLEX Level-2 (L2) operational processing will be based on physically grounded retrieval algorithms developed within ESA projects, ML is expected to play an important role in scientific exploitation and in the development of higher-level products (L3/L4), supporting high-resolution, uncertainty-aware SIF and GPP products and helping to bridge scales from leaf to ecosystem. Full article

(This article belongs to the Special Issue Remote Sensing and Modelling of Terrestrial Ecosystems Functioning)

► Show Figures

Figure 1

26 pages, 2860 KB

Open AccessReview

A Systematic Review on Remote Sensing of Dryland Ecological Integrity: Improvement in the Spatiotemporal Monitoring of Vegetation Is Required

by Andres Sutton, Adrian Fisher and Graciela Metternicht

Remote Sens. 2026, 18(1), 184; https://doi.org/10.3390/rs18010184 - 5 Jan 2026

Cited by 2 | Viewed by 2188

Abstract

Remote sensing approaches to monitoring dryland ecosystem states and trends have been dominated by the binary distinction between degraded/non-degraded areas, leading to inconsistent results. We propose a different conceptual framework that better reflects the states and pressures of these ecosystems—ecological integrity—that is, the maintenance of ecosystem composition and its capacity to contribute to human needs and adapt to change. We systematically reviewed earth observation techniques for characterizing ecological integrity in trusted databases together with studies identified through expert-guided search. A total of 137 papers were included, and their metadata (i.e., location, year) and data (i.e., aspect of ecological integrity assessed, techniques employed) were analyzed. The results show that remote sensing ecological integrity is becoming an increasingly researched topic, especially in countries with extensive drylands. Vegetation was the most frequently monitored attribute and was often employed as an indicator of other attributes (i.e., soil and water quality) and as a key feature in approaches that aimed for a comprehensive ecosystem assessment. However, most of the literature employed the normalized difference vegetation index (NDVI) as a descriptor of vegetation characteristics (i.e., health, structure, cover), which has been shown not to be a good indicator of the litter/senescent vegetation components that tend to frequently dominate drylands. Methods to overcome this weakness have been identified, although more research is needed to demonstrate their application in ecological integrity monitoring. Specifically, knowledge gaps in the relationship between vegetation cover fractions (i.e., green, non-green, and bare soil), descriptors of ecosystem quality (e.g., soil condition or vegetation structure complexity), and management (i.e., how human intervention affects ecosystem quality) should be addressed. Notable potential has been identified in time series analysis as a means of operationalising remotely sensed vegetation fractional cover. Nevertheless, limitations in benchmarking must also be tackled for effective ecological integrity monitoring. Full article

(This article belongs to the Special Issue Remote Sensing and Modelling of Terrestrial Ecosystems Functioning)

► Show Figures

Journal Menu

Journal Browser

Remote Sensing and Modelling of Terrestrial Ecosystems Functioning

Share This Special Issue

Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (7 papers)

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI