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Special Issue "Remote Sensing Applications for the Biosphere"

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

Deadline for manuscript submissions: 30 November 2023 | Viewed by 3810

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Special Issue Editors

Dr. Manuela Balzarolo

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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

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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
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Dr. Frank Veroustrate

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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

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Open AccessCommunication

Photosynthetically Active Radiation and Foliage Clumping Improve Satellite-Based NIRv Estimates of Gross Primary Production

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Remote Sens. 2023, 15(8), 2207; https://doi.org/10.3390/rs15082207 - 21 Apr 2023

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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 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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Water Stream Extraction via Feature-Fused Encoder-Decoder Network Based on SAR Images

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Remote Sens. 2023, 15(6), 1559; https://doi.org/10.3390/rs15061559 - 13 Mar 2023

Cited by 1 | Viewed by 974

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. 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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Open AccessTechnical Note

Towards a General Monitoring System for Terrestrial Primary Production: A Test Spanning the European Drought of 2018

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Remote Sens. 2023, 15(6), 1693; https://doi.org/10.3390/rs15061693 - 21 Mar 2023

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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 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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