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Remote Sensing Applications for the Biosphere
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Remote Sensing Applications for the Biosphere

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

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 7087

Special Issue Editors

Dr. Manuela Balzarolo

E-Mail Website
Guest Editor
Department of Biology, University of Antwerp, Universiteitsplein 1, 2610 Wilrijk, Belgium
Interests: terrestrial remote sensing; ecology; carbon cycle; drought; phenology; climate change
Special Issues, Collections and Topics in MDPI journals
Dr. Willem W. Verstraeten

E-Mail Website
Guest Editor
Royal Meteorological Institute (KMI), Ringlaan 3, B-1180 Brussels, Belgium
Interests: terrestrial and atmospheric remote sensing data; terrestrial water and carbon cycles; air pollution (NOX, ozone, aerosols); allergenic pollen; chemistry transport models
Special Issues, Collections and Topics in MDPI journals
Dr. Frank Veroustrate

E-Mail Website
Guest Editor
REDSTAR CM&V, Antwerpen, Belgium
Interests: terrestrial remote sensing; systems analysis; modelling; data assimilation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

A very thin layer holds most of Earth’s life in a complex mix of biotic and abiotic factors that interact in a subtle unique and ever-changing play. In this scene, remotely sensed signals result from the interaction of incoming, reflected and emitted electromagnetic radiation (EM) with atmospheric constituents, vegetation layers, soil surfaces, and oceans or water bodies. Vegetation, soil and water bodies are functional interfaces between terrestrial ecosystems and the atmosphere. These signals can be measured by optical, thermal and microwave remote sensing, including parts of the EM spectrum where fluorescence can be measured.

This Special Issue of Remote Sensing solicits contributions on strategies, methodologies or approaches leading to the development and assimilation in models of remote-sensing products originating from different EM regions, angular constellations, fluorescence as well as data measured in situ for model development and validation purposes. This Issue aims to publish both review and original research papers related to the following research topics:

  • Remote sensing of climate change;
  • Remote sensing of carbon and water cycles;
  • Remote sensing of biodiversity;
  • Remote sensing of food production;
  • Remote sensing of food security;
  • Remote sensing of nature preservation;
  • Remote sensing of epidemiology;
  • Remote sensing of anthropogenic and biogenic air pollution.

We also welcome papers presenting insights on the assimilation of remote sensing and in situ measurements in bio-geophysical and atmospheric models, as well as remote sensing extraction techniques themselves.

Finally, this Special Issue aims to bring together scientists developing remote sensing techniques, products and models leading to strategies with a higher bio-geophysical impact on the stability and sustainability of this very thin layer of the Earth on which we live.

Dr. Manuela Balzarolo
Dr. Willem W. Verstraeten
Dr. Frank Veroustrate
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

  • optical proximal and remote sensing
  • thermal proximal and remote sensing
  • sun induced fluorescence
  • modelling and data assimilation
  • time-series analysis
  • field spectroscopy

Published Papers (4 papers)

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29 pages, 11963 KiB  
Article
Cropland and Crop Type Classification with Sentinel-1 and Sentinel-2 Time Series Using Google Earth Engine for Agricultural Monitoring in Ethiopia
by Christina Eisfelder, Bruno Boemke, Ursula Gessner, Patrick Sogno, Genanaw Alemu, Rahel Hailu, Christian Mesmer and Juliane Huth
Remote Sens. 2024, 16(5), 866; https://doi.org/10.3390/rs16050866 - 29 Feb 2024
Viewed by 1114
Abstract
Cropland monitoring is important for ensuring food security in the context of global climate change and population growth. Freely available satellite data allow for the monitoring of large areas, while cloud-processing platforms enable a wide user community to apply remote sensing techniques. Remote [...] Read more.
Cropland monitoring is important for ensuring food security in the context of global climate change and population growth. Freely available satellite data allow for the monitoring of large areas, while cloud-processing platforms enable a wide user community to apply remote sensing techniques. Remote sensing-based estimates of cropped area and crop types can thus assist sustainable land management in developing countries such as Ethiopia. In this study, we developed a method for cropland and crop type classification based on Sentinel-1 and Sentinel-2 time-series data using Google Earth Engine. Field data on 18 different crop types from three study areas in Ethiopia were available as reference for the years 2021 and 2022. First, a land use/land cover classification was performed to identify cropland areas. We then evaluated different input parameters derived from Sentinel-2 and Sentinel-1, and combinations thereof, for crop type classification. We assessed the accuracy and robustness of 33 supervised random forest models for classifying crop types for three study areas and two years. Our results showed that classification accuracies were highest when Sentinel-2 spectral bands were included. The addition of Sentinel-1 parameters only slightly improved the accuracy compared to Sentinel-2 parameters alone. The variant including S2 bands, EVI2, and NDRe2 from Sentinel-2 and VV, VH, and Diff from Sentinel-1 was finally applied for crop type classification. Investigation results of class-specific accuracies reinforced the importance of sufficient reference sample availability. The developed methods and classification results can assist regional experts in Ethiopia to support agricultural monitoring and land management. Full article
(This article belongs to the Special Issue Remote Sensing Applications for the Biosphere)
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12 pages, 1715 KiB  
Communication
Photosynthetically Active Radiation and Foliage Clumping Improve Satellite-Based NIRv Estimates of Gross Primary Production
by Iolanda Filella, Adrià Descals, Manuela Balzarolo, Gaofei Yin, Aleixandre Verger, Hongliang Fang and Josep Peñuelas
Remote Sens. 2023, 15(8), 2207; https://doi.org/10.3390/rs15082207 - 21 Apr 2023
Cited by 1 | Viewed by 1773
Abstract
Monitoring gross primary production (GPP) is necessary for quantifying the terrestrial carbon balance. The near-infrared reflectance of vegetation (NIRv) has been proven to be a good predictor of GPP. Given that radiation powers photosynthesis, we hypothesized that (i) the addition of photosynthetic photon [...] Read more.
Monitoring gross primary production (GPP) is necessary for quantifying the terrestrial carbon balance. The near-infrared reflectance of vegetation (NIRv) has been proven to be a good predictor of GPP. Given that radiation powers photosynthesis, we hypothesized that (i) the addition of photosynthetic photon flux density (PPFD) information to NIRv would improve estimates of GPP and that (ii) a further improvement would be obtained by incorporating the estimates of radiation distribution in the canopy provided by the foliar clumping index (CI). Thus, we used GPP data from FLUXNET sites to test these possible improvements by comparing the performance of a model based solely on NIRv with two other models, one combining NIRv and PPFD and the other combining NIRv, PPFD and the CI of each vegetation cover type. We tested the performance of these models for different types of vegetation cover, at various latitudes and over the different seasons. Our results demonstrate that the addition of daily radiation information and the clumping index for each vegetation cover type to the NIRv improves its ability to estimate GPP. The improvement was related to foliage organization, given that the foliar distribution in the canopy (CI) affects radiation distribution and use and that radiation drives productivity. Evergreen needleleaf forests are the vegetation cover type with the greatest improvement in GPP estimation after the addition of CI information, likely as a result of their greater radiation constraints. Vegetation type was more determinant of the sensitivity to PPFD changes than latitude or seasonality. We advocate for the incorporation of PPFD and CI into NIRv algorithms and GPP models to improve GPP estimates. Full article
(This article belongs to the Special Issue Remote Sensing Applications for the Biosphere)
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28 pages, 7449 KiB  
Article
Water Stream Extraction via Feature-Fused Encoder-Decoder Network Based on SAR Images
by Da Yuan, Chao Wang, Lin Wu, Xu Yang, Zhengwei Guo, Xiaoyan Dang, Jianhui Zhao and Ning Li
Remote Sens. 2023, 15(6), 1559; https://doi.org/10.3390/rs15061559 - 13 Mar 2023
Cited by 3 | Viewed by 1753
Abstract
The extraction of water stream based on synthetic aperture radar (SAR) is of great significance in surface water monitoring, flood monitoring, and the management of water resources. However, in recent years, the research mainly uses the backscattering feature (BF) to extract water bodies. [...] Read more.
The extraction of water stream based on synthetic aperture radar (SAR) is of great significance in surface water monitoring, flood monitoring, and the management of water resources. However, in recent years, the research mainly uses the backscattering feature (BF) to extract water bodies. In this paper, a feature-fused encoder–decoder network was proposed for delineating the water stream more completely and precisely using both the BF and polarimetric feature (PF) from SAR images. Firstly, the standard BFs were extracted and PFs were obtained using model-based decomposition. Specifically, the newly model-based decomposition, more suitable for dual-pol SAR images, was selected to acquire three different PFs of surface water stream for the first time. Five groups of candidate feature combinations were formed with two BFs and three PFs. Then, a new feature-fused encoder–decoder network (FFEDN) was developed for mining and fusing both BFs and PFs. Finally, several typical areas were selected to evaluate the performance of different combinations for water stream extraction. To further verify the effectiveness of the proposed method, two machine learning methods and four state-of-the-art deep learning algorithms were utilized for comparison. The experimental results showed that the proposed method using the optimal feature combination achieved the highest accuracy, with a precision of 95.21%, recall of 91.79%, intersection over union (IoU) score of 87.73%, overall accuracy (OA) of 93.35%, and average accuracy (AA) of 93.41%. The results showed that the performance was higher when BF and PF were combined. In short, in this study, the effectiveness of PFs for water stream extraction was verified and the proposed FFEDN can further improve the accuracy of water stream extraction. Full article
(This article belongs to the Special Issue Remote Sensing Applications for the Biosphere)
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15 pages, 4052 KiB  
Technical Note
Towards a General Monitoring System for Terrestrial Primary Production: A Test Spanning the European Drought of 2018
by Keith J. Bloomfield, Roel van Hoolst, Manuela Balzarolo, Ivan A. Janssens, Sara Vicca, Darren Ghent and I. Colin Prentice
Remote Sens. 2023, 15(6), 1693; https://doi.org/10.3390/rs15061693 - 21 Mar 2023
Cited by 1 | Viewed by 1702
Abstract
(1) Land surface models require inputs of temperature and moisture variables to generate predictions of gross primary production (GPP). Differences between leaf and air temperature vary temporally and spatially and may be especially pronounced under conditions of low soil moisture availability. The Sentinel-3 [...] Read more.
(1) Land surface models require inputs of temperature and moisture variables to generate predictions of gross primary production (GPP). Differences between leaf and air temperature vary temporally and spatially and may be especially pronounced under conditions of low soil moisture availability. The Sentinel-3 satellite mission offers estimates of the land surface temperature (LST), which for vegetated pixels can be adopted as the canopy temperature. Could remotely sensed estimates of LST offer a parsimonious input to models by combining information on leaf temperature and hydration? (2) Using a light use efficiency model that requires only a handful of input variables, we generated GPP simulations for comparison with eddy-covariance inferred estimates available from flux sites within the Integrated Carbon Observation System. Remotely sensed LST and greenness data were input from Sentinel-3. Gridded air temperature data were obtained from the European Centre for Medium-Range Weather Forecasts. We chose the years 2018–2019 to exploit the natural experiment of a pronounced European drought. (3) Simulated GPP showed good agreement with flux-derived estimates. During dry conditions, simulations forced with LST performed better than those with air temperature for shrubland, grassland and savanna sites. (4) This study advances the prospect for a global GPP monitoring system that will rely primarily on remotely sensed inputs. Full article
(This article belongs to the Special Issue Remote Sensing Applications for the Biosphere)
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