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

Journal Menu

Journal Browser

Remote Sensing of Land–Atmosphere Interactions

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Biogeosciences Remote Sensing".

Deadline for manuscript submissions: closed (20 October 2022) | Viewed by 15194

Share This Special Issue

Special Issue Editor

Prof. Dr. Trent W. Biggs

E-Mail Website
Guest Editor

Department of Geography, San Diego State University, San Diego, CA 92182, USA
Interests: effects of land cover change on hydrology, sediment, and stream biogeochemistry; remote sensing of land cover, terrain, and evapotranspiration; isotopes in the hydrologic cycle; watershed analysis for multi-scale characterization of land cover change; 3D photo reconstruction of stream channels and hillslopes

Special Issue Information

Dear Colleagues,

Land surface fluxes interact with the lower atmosphere in ways that impact regional water, energy, and carbon balances. Land cover and land use, including deforestation, agriculture, irrigation, and urbanization, impact surface characteristics including aerodynamic roughness, albedo, soil moisture, evapotranspiration, and nutrient cycles. Alterations in surface characteristics and fluxes can then impact atmospheric concentrations of vapor and trace gases, while changes in energy balances can impact atmospheric circulation in ways that alter precipitation and extreme event frequency and magnitude.

This Special issue highlights the use of remote sensing to quantify and understand land–atmosphere interactions. We invite articles that integrate remotely sensed data with other techniques, including modeling and fieldwork to document interactions and feedbacks in the land–atmosphere system.

Potential topics could include but are not limited to applications in remote sensing related to:

Measurement of land–atmosphere fluxes;
Quantification of moisture and trace gases in the atmosphere: ground- and satellite-based methods;
Active and passive methods for measuring atmospheric properties and processes;
Impacts of land use on atmospheric circulation, trace gases, and/or precipitation;
Integration of remote sensing and modeling;
Use of remote sensing to map trace gas isotopic composition;
Interactions between climate and land–surface fluxes;
Feedbacks between vegetation, soil moisture, and atmospheric processes;
Use of big data platforms (e.g., Google Earth Engine) and machine learning algorithms for quantification of land-atmosphere interactions.

Prof. Dr. Trent W. Biggs
Guest Editor

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

Land atmosphere
Turbulent heat flux
Carbon dynamics
Ecosystem exchange
Water vapor
Hydrologic cycle

Published Papers (4 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: Other

22 pages, 7549 KiB

Open AccessArticle

Global Evapotranspiration Datasets Assessment Using Water Balance in South America

by Anderson Ruhoff, Bruno Comini de Andrade, Leonardo Laipelt, Ayan Santos Fleischmann, Vinícius Alencar Siqueira, Adriana Aparecida Moreira, Rafael Barbedo, Gabriele Leão Cyganski, Gabriel Matte Rios Fernandez, João Paulo Lyra Fialho Brêda, Rodrigo Cauduro Dias de Paiva, Adalberto Meller, Alexandre de Amorim Teixeira, Alexandre Abdalla Araújo, Marcus André Fuckner and Trent Biggs

Remote Sens. 2022, 14(11), 2526; https://doi.org/10.3390/rs14112526 - 25 May 2022

Cited by 9 | Viewed by 3508

Abstract

Evapotranspiration (

E T

) connects the land to the atmosphere, linking water, energy, and carbon cycles.

E T

is an essential climate variable with a fundamental importance, and accurate assessments of the spatiotemporal trends and variability in

E T

are needed from [...] Read more.

Evapotranspiration (

E T

) connects the land to the atmosphere, linking water, energy, and carbon cycles.

E T

is an essential climate variable with a fundamental importance, and accurate assessments of the spatiotemporal trends and variability in

E T

are needed from regional to continental scales. This study compared eight global actual

E T

datasets (

E T_{g l}

) and the average actual

E T

ensemble (

E T_{e n s}

) based on remote sensing, climate reanalysis, land-surface, and biophysical models to

E T

computed from basin-scale water balance (

E T_{w b}

) in South America on monthly time scale. The 50 small-to-large basins covered major rivers and different biomes and climate types. We also examined the magnitude, seasonality, and interannual variability of

E T

, comparing

E T_{g l}

and

E T_{e n s}

with

E T_{w b}

. Global

E T

datasets were evaluated between 2003 and 2014 from the following datasets: Breathing Earth System Simulator (BESS), ECMWF Reanalysis 5 (ERA5), Global Land Data Assimilation System (GLDAS), Global Land Evaporation Amsterdam Model (GLEAM), MOD16, Penman–Monteith–Leuning (PML), Operational Simplified Surface Energy Balance (SSEBop) and Terra Climate. By using

E T_{w b}

as a basis for comparison, correlation coefficients ranged from 0.45 (SSEBop) to 0.60 (

E T_{e n s}

), and RMSE ranged from 35.6 (

E T_{e n s}

) to 40.5 mm·month⁻¹ (MOD16). Overall,

E T_{g l}

estimates ranged from 0 to 150 mm·month⁻¹ in most basins in South America, while

E T_{w b}

estimates showed maximum rates up to 250 mm·month⁻¹.

E T_{g l}

varied by hydroclimatic regions: (i) basins located in humid climates with low seasonality in precipitation, including the Amazon, Uruguay, and South Atlantic basins, yielded weak correlation coefficients between monthly

E T_{g l}

and

E T_{w b}

, and (ii) tropical and semiarid basins (areas where precipitation demonstrates a strong seasonality, as in the São Francisco, Northeast Atlantic, Paraná/Paraguay, and Tocantins basins) yielded moderate-to-strong correlation coefficients. An assessment of the interannual variability demonstrated a disagreement between

E T_{g l}

and

E T_{w b}

in the humid tropics (in the Amazon), with

E T_{g l}

showing a wide range of interannual variability. However, in tropical, subtropical, and semiarid climates, including the Tocantins, São Francisco, Paraná, Paraguay, Uruguay, and Atlantic basins (Northeast, East, and South), we found a stronger agreement between

E T_{g l}

and

E T_{w b}

for interannual variability. Assessing

E T

datasets enables the understanding of land–atmosphere exchanges in South America, to improvement of

E T

estimation and monitoring for water management. Full article

(This article belongs to the Special Issue Remote Sensing of Land–Atmosphere Interactions)

► Show Figures

Graphical abstract

28 pages, 19133 KiB

Open AccessArticle

From Standard Weather Stations to Virtual Micro-Meteorological Towers in Ungauged Sites: Modeling Tool for Surface Energy Fluxes, Evapotranspiration, Soil Temperature, and Soil Moisture Estimations

by Jorge A. Celis, Hernan A. Moreno, Jeffrey B. Basara, Renee A. McPherson, Michael Cosh, Tyson Ochsner and Xiangming Xiao

Remote Sens. 2021, 13(7), 1271; https://doi.org/10.3390/rs13071271 - 26 Mar 2021

Cited by 3 | Viewed by 2967

Abstract

One of the benefits of training a process-based, land surface model is the capacity to use it in ungauged sites as a complement to standard weather stations for predicting energy fluxes, evapotranspiration, and surface and root-zone soil temperature and moisture. In this study, dynamic (i.e., time-evolving) vegetation parameters were derived from remotely sensed Moderate Resolution Imaging Spectroradiometer (MODIS) imagery and coupled with a physics-based land surface model (tin-based Real-time Integrated Basin Simulator (tRIBS)) at four eddy covariance (EC) sites in south-central U.S. to test the predictability of micro-meteorological, soil-related, and energy flux-related variables. One cropland and one grassland EC site in northern Oklahoma, USA, were used to tune the model with respect to energy fluxes, soil temperature, and moisture. Calibrated model parameters, mostly related to the soil, were then transferred to two other EC sites in Oklahoma with similar soil and vegetation types. New dynamic vegetation parameter time series were updated according to MODIS imagery at each site. Overall, the tRIBS model captured both seasonal and diurnal cycles of the energy partitioning and soil temperatures across all four stations, as indicated by the model assessment metrics, although large uncertainties appeared in the prediction of ground heat flux, surface, and root-zone soil moisture at some stations. The transferability of previously calibrated model parameters and the use of MODIS to derive dynamic vegetation parameters enabled rapid yet reasonable predictions. The model was proven to be a convenient complement to standard weather stations particularly for sites where eddy covariance or similar equipment is not available. Full article

(This article belongs to the Special Issue Remote Sensing of Land–Atmosphere Interactions)

► Show Figures

Graphical abstract

27 pages, 3376 KiB

Open AccessArticle

Combining High-Resolution Remote Sensing Products with a Crop Model to Estimate Carbon and Water Budget Components: Application to Sunflower

by Gaétan Pique, Rémy Fieuzal, Philippe Debaeke, Ahmad Al Bitar, Tiphaine Tallec and Eric Ceschia

Remote Sens. 2020, 12(18), 2967; https://doi.org/10.3390/rs12182967 - 11 Sep 2020

Cited by 10 | Viewed by 4028

Abstract

The global increase in food demand in the context of climate change requires a clear understanding of cropland function and of its impact on biogeochemical cycles. However, although gas exchange between croplands and the atmosphere is measurable in the field, it is difficult to quantify at the plot scale over relatively large areas because of the heterogeneous character of landscapes and differences in crop management. However, assessing accurate carbon and water budgets over croplands is essential to promote sustainable agronomic practices and reduce the water demand and the climatic impacts of croplands while maintaining sufficient yields. From this perspective, we developed a crop model, SAFYE-CO₂, that assimilates high spatial- and temporal-resolution (HSTR) remote sensing products to estimate daily crop biomass, water and CO₂ fluxes, annual yields, and carbon budgets at the parcel level over large areas. This modeling approach was evaluated for sunflower against two in situ datasets. First, the model’s output was compared to data acquired during two cropping seasons at the Auradé integrated carbon observation system (ICOS) instrumented site in southwestern France. The model accurately simulated the daily net CO₂ flux (root mean square error (RMSE) = 0.97 gC·m⁻²·d⁻¹ and determination coefficient (R²) = 0.83) and water flux (RMSE = 0.68 mm·d⁻¹ and R² = 0.79). The model’s performance was then evaluated against biomass and yield data collected from 80 plots located in southwestern France. The model was able to satisfactorily estimate biomass dynamics and yield (RMSE = 66 and 54 g·m⁻², respectively). To investigate the potential application of the proposed approach at a large scale, given that soil properties are important factors affecting the model, a sensitivity analysis of two existing soil products (GlobalSoilMap and SoilGrids) was carried out. Our results show that these products are not sufficiently accurate for inclusion as inputs to the model, which requires more accurate information on soil water retention capacity to assess water fluxes. Additionally, we argue that no water stress should be considered in the crop growth computation since this stress is already present because of remote sensing information in the proposed approach. This study should be considered a first step to fulfill the existing gap in quantifying carbon budgets at the plot scale over large areas and to accurately estimate the effects of management practices, such as the use of cover crops or specific crop rotations on cropland C and water budgets. Full article

(This article belongs to the Special Issue Remote Sensing of Land–Atmosphere Interactions)

► Show Figures

Graphical abstract

Other

Jump to: Research

15 pages, 6194 KiB

Open AccessLetter

Substantially Greater Carbon Emissions Estimated Based on Annual Land-Use Transition Data

by Jiaojiao Diao, Jinxun Liu, Zhiliang Zhu, Mingshi Li and Benjamin M. Sleeter

Remote Sens. 2020, 12(7), 1126; https://doi.org/10.3390/rs12071126 - 2 Apr 2020

Cited by 7 | Viewed by 3190

Abstract

Quantifying land-use and land-cover change (LULCC) effects on carbon sources and sinks has been very challenging because of the availability and quality of LULCC data. As the largest estuary in the United States, Chesapeake Bay is a rapidly changing region and is affected by human activities. A new annual land-use and land-cover (LULC) data product developed by the U.S. Geological Survey Land Change Monitoring and Analysis Program (LCMAP) from 2001 to 2011 was analyzed for transitions between agricultural land, developed land, grassland, forest land and wetland. The Land Use and Carbon Scenario Simulator was used to simulate effects of LULCC and ecosystem disturbance in the south of the Chesapeake Bay Watershed (CBW) on carbon storage and fluxes, with carbon parameters derived from the Integrated Biosphere Simulator. We found that during the study period: (1) areas of forest land, disturbed land, agricultural land and wetland decreased by 90, 82, 57, and 65 km², respectively, but developed lands gained 293 km² (29 km² annually); (2) total ecosystem carbon stock in the CBW increased by 13 Tg C from 2001 to 2011, mainly due to carbon sequestration of the forest ecosystem; (3) carbon loss was primarily attributed to urbanization (0.224 Tg C·yr⁻¹) and agricultural expansion (0.046 Tg C·yr⁻¹); and (4) estimated carbon emissions and harvest wood products were greater when estimated with the annual LULC input. We conclude that a dense time series of LULCC, such as that of the LCMAP program, may provide a more accurate accounting of the effects of land use change on ecosystem carbon, which is critical to understanding long-term ecosystem carbon dynamics. Full article

(This article belongs to the Special Issue Remote Sensing of Land–Atmosphere Interactions)

► Show Figures

Journal Menu

Journal Browser

Remote Sensing of Land–Atmosphere Interactions

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Published Papers (4 papers)

Research

Other

Further Information

Guidelines

MDPI Initiatives

Follow MDPI