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Remote Sensing
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Application of Satellite Remote Sensing Technology in Earth System Monitoring
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Application of Satellite Remote Sensing Technology in Earth System Monitoring

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

Deadline for manuscript submissions: 15 June 2024 | Viewed by 1771

Special Issue Editor

Dr. Stephan Havemann

E-Mail Website
Guest Editor
Met Office, Foundation and Weather Science, Exeter EX1 3PB, UK
Interests: atmospheric radiative transfer; satellite; airborne and ground-based remote sensing; retrieval of atmospheric and surface properties; electromagnetic scattering theory; cirrus; operational satellite data assimilation; numerical methods; big data; machine learning techniques
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Climate change, air quality, and environmental degradation are the main societal challenges in the twenty-first century. In order to address these challenges, we need increased information on the Earth’s system (the cryosphere, the ecosystems, the hydrosphere, and the solid Earth, as well as the oceans). A crucial component of Earth System Monitoring is satellite observations. Current satellite technology provides relevant information on atmospheric constituents, sea surface temperatures, soil moisture, snow cover, etc.

The objective of this Special Issue is to provide an overview of the state-of-the-art applied research using satellite remote sensing technology for Earth System Monitoring. We welcome studies on the application or assimilation of satellite observations in models and research presenting the most recent advances in:

  • land reanalysis,
  • cloud properties,
  • air temperature analyses,
  • coupled land–atmosphere assimilation,
  • numerical weather prediction,
  • hydrological forecast,
  • ocean dynamics,
  • carbon cycle monitoring,
  • etc.

Dr. Stephan Havemann
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

  • satellite remote sensing
  • earth system monitoring
  • long term ecological research
  • satellite altimetry
  • clouds
  • ocean dynamics
  • soil moisture
  • snow depth and cover
  • terrestrial water storage
  • inland water extent and temperature
  • biomass

Published Papers (2 papers)

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Research

25 pages, 30953 KiB  
Article
Mapping Integrated Crop–Livestock Systems Using Fused Sentinel-2 and PlanetScope Time Series and Deep Learning
by João P. S. Werner, Mariana Belgiu, Inacio T. Bueno, Aliny A. Dos Reis, Ana P. S. G. D. Toro, João F. G. Antunes, Alfred Stein, Rubens A. C. Lamparelli, Paulo S. G. Magalhães, Alexandre C. Coutinho, Júlio C. D. M. Esquerdo and Gleyce K. D. A. Figueiredo
Remote Sens. 2024, 16(8), 1421; https://doi.org/10.3390/rs16081421 - 17 Apr 2024
Viewed by 303
Abstract
Integrated crop–livestock systems (ICLS) are among the main viable strategies for sustainable agricultural production. Mapping these systems is crucial for monitoring land use changes in Brazil, playing a significant role in promoting sustainable agricultural production. Due to the highly dynamic nature of ICLS [...] Read more.
Integrated crop–livestock systems (ICLS) are among the main viable strategies for sustainable agricultural production. Mapping these systems is crucial for monitoring land use changes in Brazil, playing a significant role in promoting sustainable agricultural production. Due to the highly dynamic nature of ICLS management, mapping them is a challenging task. The main objective of this research was to develop a method for mapping ICLS using deep learning algorithms applied on Satellite Image Time Series (SITS) data cubes, which consist of Sentinel-2 (S2) and PlanetScope (PS) satellite images, as well as data fused (DF) from both sensors. This study focused on two Brazilian states with varying landscapes and field sizes. Targeting ICLS, field data were combined with S2 and PS data to build land use and land cover classification models for three sequential agricultural years (2018/2019, 2019/2020, and 2020/2021). We tested three experimental settings to assess the classification performance using S2, PS, and DF data cubes. The test classification algorithms included Random Forest (RF), Temporal Convolutional Neural Network (TempCNN), Residual Network (ResNet), and a Lightweight Temporal Attention Encoder (L-TAE), with the latter incorporating an attention-based model, fusing S2 and PS within the temporal encoders. Experimental results did not show statistically significant differences between the three data sources for both study areas. Nevertheless, the TempCNN outperformed the other classifiers with an overall accuracy above 90% and an F1-Score of 86.6% for the ICLS class. By selecting the best models, we generated annual ICLS maps, including their surrounding landscapes. This study demonstrated the potential of deep learning algorithms and SITS to successfully map dynamic agricultural systems. Full article
(This article belongs to the Special Issue Application of Satellite Remote Sensing Technology in Earth System Monitoring)
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16 pages, 3734 KiB  
Communication
Integrating NDVI-Based Within-Wetland Vegetation Classification in a Land Surface Model Improves Methane Emission Estimations
by Theresia Yazbeck, Gil Bohrer, Oleksandr Shchehlov, Eric Ward, Robert Bordelon, Jorge A. Villa and Yang Ju
Remote Sens. 2024, 16(6), 946; https://doi.org/10.3390/rs16060946 - 08 Mar 2024
Cited by 1 | Viewed by 865
Abstract
Earth system models (ESMs) are a common tool for estimating local and global greenhouse gas emissions under current and projected future conditions. Efforts are underway to expand the representation of wetlands in the Energy Exascale Earth System Model (E3SM) Land Model (ELM) by [...] Read more.
Earth system models (ESMs) are a common tool for estimating local and global greenhouse gas emissions under current and projected future conditions. Efforts are underway to expand the representation of wetlands in the Energy Exascale Earth System Model (E3SM) Land Model (ELM) by resolving the simultaneous contributions to greenhouse gas fluxes from multiple, different, sub-grid-scale patch-types, representing different eco-hydrological patches within a wetland. However, for this effort to be effective, it should be coupled with the detection and mapping of within-wetland eco-hydrological patches in real-world wetlands, providing models with corresponding information about vegetation cover. In this short communication, we describe the application of a recently developed NDVI-based method for within-wetland vegetation classification on a coastal wetland in Louisiana and the use of the resulting yearly vegetation cover as input for ELM simulations. Processed Harmonized Landsat and Sentinel-2 (HLS) datasets were used to drive the sub-grid composition of simulated wetland vegetation each year, thus tracking the spatial heterogeneity of wetlands at sufficient spatial and temporal resolutions and providing necessary input for improving the estimation of methane emissions from wetlands. Our results show that including NDVI-based classification in an ELM reduced the uncertainty in predicted methane flux by decreasing the model’s RMSE when compared to Eddy Covariance measurements, while a minimal bias was introduced due to the resampling technique involved in processing HLS data. Our study shows promising results in integrating the remote sensing-based classification of within-wetland vegetation cover into earth system models, while improving their performances toward more accurate predictions of important greenhouse gas emissions. Full article
(This article belongs to the Special Issue Application of Satellite Remote Sensing Technology in Earth System Monitoring)
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