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Remote Sensing
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Ecological Environment Satellite System: Research and Application
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Ecological Environment Satellite System: Research and Application

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

Deadline for manuscript submissions: closed (15 September 2023) | Viewed by 2117

Special Issue Editors

Prof. Dr. John J. Qu

E-Mail Website
Guest Editor
Professor and Director, GENRI & ESTC, Department of Geography and GeoInformation Science (GGS), Global Environment and Natural Resources Institute (GENRI), College of Science, George Mason University, Fairfax, VA 22030, USA
Interests: remote sensing; earth system and climate science; soil moisture and drought monitoring; water-energy-food nexus; environment and fire science
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xianjun Hao

E-Mail Website
Guest Editor
GENRI & ESTC, Department of Geography and GeoInformation Science (GGS), Global Environment and Natural Resources Institute (GENRI), College of Science, George Mason University, Fairfax, VA 22030, USA
Interests: satellite remote sensing applications; earth sciences and climate change; soil moisture and drought monitoring; data science and high performance computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on recent advances in the research and application of satellite remote sensing technology in ecological and environmental monitoring. Climate change currently poses a significant threat to the Earth’s environment and sustainability, and it is thus important to monitor the impacts of climate change and human activity on the ecological environment, ecosystem functions, ecosystem health, and ecosystem sustainability.  Satellite remote sensing systems provide valuable data for ecological environment monitoring, including on land use and land cover changes, vegetation growth, natural resources, surface temperature, precipitation, snow cover, and natural hazards, etc. Meanwhile, advances in data science and artificial intelligence, especially machine learning, provide powerful capabilities that can be utilized in remote sensing data analysis, knowledge discovery, modeling, and decision-making supports.  This Special Issue invites authors to submit original research, review articles, and applications that explore satellite remote sensing measurements and that assist in monitoring ecological environment changes, analyzing and modelling the impacts of natural events and human activity on the ecological environment, and finding management solutions that are  beneficial for ecosystem sustainability.

We welcome papers that focus on the following  areas of interest:

  • Satellite remote sensing systems for ecological environment monitoring
  • Ecological environment monitoring with remote sensing data
  • Ecological impacts of natural hazards, including drought, wildland fires, hurricane, flooding, etc.
  • Impacts of climate change on ecosystems
  • Impacts of human activities on ecosystems
  • Data analysis and modeling techniques in ecological environment study
  • Machine learning techniques in ecological environment study
  • Soil ecosystem carbon climate nexus study
  • Decision making support for natural resource management

Prof. Dr. John J. Qu
Prof. Dr. Xianjun Hao
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

  • satellite remote sensing
  • ecological environment
  • climate change
  • human activities
  • natural hazards
  • ecosystem sustainability
  • machine learning

Published Papers (2 papers)

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Research

24 pages, 21361 KiB  
Article
Substantial Reduction in Vegetation Photosynthesis Capacity during Compound Droughts in the Three-River Headwaters Region, China
by Jun Miao, Ru An, Yuqing Zhang and Fei Xing
Remote Sens. 2023, 15(20), 4943; https://doi.org/10.3390/rs15204943 - 13 Oct 2023
Cited by 1 | Viewed by 809
Abstract
Solar-induced chlorophyll fluorescence (SIF) is a reliable proxy for vegetative photosynthesis and is commonly used to characterize responses to drought. However, there is limited research regarding the use of multiple high-resolution SIF datasets to analyze reactions to atmospheric drought and soil drought, especially [...] Read more.
Solar-induced chlorophyll fluorescence (SIF) is a reliable proxy for vegetative photosynthesis and is commonly used to characterize responses to drought. However, there is limited research regarding the use of multiple high-resolution SIF datasets to analyze reactions to atmospheric drought and soil drought, especially within mountain grassland ecosystems. In this study, we used three types of high-spatial-resolution SIF datasets (0.05°), coupled with meteorological and soil moisture datasets, to investigate the characteristics of atmospheric, soil, and compound drought types. We centered this investigation on the years spanning 2001–2020 in the Three-River Headwaters Region (TRHR). Our findings indicate that the TRHR experienced a combination of atmospheric drying and soil wetting due to increases in the standardized saturation vapor pressure deficit index (SVPDI) and standardized soil moisture index (SSMI). In the growing season, atmospheric drought was mainly distributed in the southern and eastern parts of the TRHR (reaching 1.7 months/year), while soil drought mainly occurred in the eastern parts of the TRHR (reaching 2 months/year). Compound drought tended to occur in the southern and eastern parts of the TRHR and trended upward during 2001–2020. All three SIF datasets consistently revealed robust photosynthetic activity in the southern and eastern parts of the TRHR, with SIF values generally exceeding 0.2 mW· m−2·nm−1·sr−1. Overall, the rise in SIF between 2001 and 2020 corresponds to enhanced greening of TRHR vegetation. Vegetation photosynthesis was found to be limited in July, attributable to a high vapor pressure deficit and low soil moisture. In the response of CSIF data to a drought event, compound drought (SVPDI ≥ 1 and SSMI ≤ −1) caused a decline of up to 14.52% in SIF across the source region of the Yellow River (eastern TRHR), while individual atmospheric drought and soil drought events caused decreases of only 5.06% and 8.88%, respectively. The additional effect of SIF produced by compound drought outweighed that of atmospheric drought as opposed to soil drought, suggesting that soil moisture predominantly governs vegetation growth in the TRHR. The reduction in vegetation photosynthesis capacity commonly occurring in July, characterized by a simultaneously high vapor pressure deficit and low soil moisture, was more pronounced in Yellow River’s source region as well. Compound drought conditions more significantly reduce SIF compared to singular drought events. Soil drought evidently played a greater role in vegetation growth stress than atmospheric drought in the TRHR via the additional effects of compound drought. Full article
(This article belongs to the Special Issue Ecological Environment Satellite System: Research and Application)
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19 pages, 4059 KiB  
Article
Air Temperature Monitoring over Low Latitude Rice Planting Areas: Combining Remote Sensing, Model Assimilation, and Machine Learning Techniques
by Minghao Lin, Qiang Fang, Jizhe Xia and Chenyang Xu
Remote Sens. 2023, 15(15), 3805; https://doi.org/10.3390/rs15153805 - 31 Jul 2023
Viewed by 855
Abstract
Air temperature (Ta) is essential for studying surface processes and human activities, particularly agricultural cultivation, which is strongly influenced by temperature. Remote sensing techniques that integrate multi-source data can estimate Ta with a high degree of accuracy, overcoming the shortcomings of traditional measurements [...] Read more.
Air temperature (Ta) is essential for studying surface processes and human activities, particularly agricultural cultivation, which is strongly influenced by temperature. Remote sensing techniques that integrate multi-source data can estimate Ta with a high degree of accuracy, overcoming the shortcomings of traditional measurements due to spatial heterogeneity. Based on in situ measurements in Guangdong Province from 2012 to 2018, this study applied three machine learning (ML) models and fused multi-source datasets to evaluate the performance of four data combinations in Ta estimation. Correlations of covariates were compared, focusing on rice planting areas (RA). The results showed that (1) The fusion of multi-source data improved the accuracy of model estimations, where the best performance was achieved by the random forest (RF) model combined with the ERA5 combination, with the highest R2 reaching 0.956, the MAE value of 0.996 °C, and the RMSE of 1.365 °C; (2) total precipitation (TP), wind speed (WD), normalized difference vegetation index (NDVI), and land surface temperature (LST) were significant covariates for long-term Ta estimations; (3) Rice planting improved the model performance in estimating Ta, and model accuracy decreased during the crop rotation in summer. This study provides a reference for the selection of temperature estimation models and covariate datasets. It offers a case for subsequent ML studies on remote sensing of temperatures over agricultural areas and the impact of agricultural cultivation on global warming. Full article
(This article belongs to the Special Issue Ecological Environment Satellite System: Research and Application)
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