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Remote Sensing for Natural Resources and Environmental Management of Arid and Semi-Arid Regions

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

Deadline for manuscript submissions: 31 March 2026 | Viewed by 1628

Special Issue Editors

Dr. Chong Wang

E-Mail Website
Guest Editor
School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China
Interests: vegetation phenology; permafrost; soil organic carbon; precipitation correction; remote sensing
Dr. Hao Wang

E-Mail Website
Guest Editor
School of Geography and Tourism, Shaanxi Normal University, Xi’an 710119, China
Interests: remote sensing of vegetation; ecological restoration; ecosystem services; social–ecological system
Special Issues, Collections and Topics in MDPI journals
Dr. Shuci Liu

E-Mail Website
Guest Editor
Department of Environment and Science, Queensland Government, Brisbane 4102, QLD, Australia
Interests: hydrology; water resources; water quality; climate change
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing technologies offer cost-effective, large-scale data collection for various applications, including dryland ecosystem analysis, soil degradation assessment, water resource management, and environmental parameter retrieval. Recent advancements, such as multi-sensor integration and artificial intelligence approaches, have improved our ability to monitor the structure and functions of ecosystems, soil properties, water quality and quantity, and hydroclimatic extremes. These approaches have revolutionized natural resource management, enabling efficient decision-making and policy formulation, and have contributed to sustainable development and resource preservation. However, challenges remain, including low signal-to-noise ratios in drylands, limited ground observational networks, and the need for specialized algorithms.

This Special Issue invites studies on the integration of multiple remote sensing data sources, utilization of means of artificial intelligence, machine learning, and big data in natural resource management and the environment in arid and semi-arid regions.

Topics covering anything from multiscale approaches to studies on the assessment of ecosystem function and service, the retrieval of ecosystem indices, and environmental parameters are welcome.

Dr. Chong Wang
Dr. Hao Wang
Dr. Shuci Liu
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 250 words) can be sent to the Editorial Office for assessment.

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

  • vegetation dynamics
  • ecosystem services
  • carbon cycle/sequestration
  • soil moisture
  • evaporation
  • water quality
  • scale effects
  • climate change

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Published Papers (2 papers)

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Research

32 pages, 23108 KB  
Article
Reconstruction of SMAP Soil Moisture Data Based on Residual Autoencoder Network with Convolutional Feature Extraction
by Yaojie Liu, Haoyu Fan, Yan Jin and Shaonan Zhu
Remote Sens. 2025, 17(22), 3729; https://doi.org/10.3390/rs17223729 - 16 Nov 2025
Viewed by 466
Abstract
Satellite-based surface soil moisture (SSM) products often contain spatial gaps and reduced reliability due to variations in vegetation cover and type, complex surface conditions such as heterogeneous topography and soil texture, or inherent limitations of satellite microwave sensors. This study presents a residual [...] Read more.
Satellite-based surface soil moisture (SSM) products often contain spatial gaps and reduced reliability due to variations in vegetation cover and type, complex surface conditions such as heterogeneous topography and soil texture, or inherent limitations of satellite microwave sensors. This study presents a residual autoencoder model named TsSMNet, which combines multi-source remote sensing inputs with statistical features derived from SSM time series, including central tendency, dispersion and variability, extremes and distribution, temporal dynamics, magnitude and energy, and count-based features, to reconstruct gap-free SSM estimates. The model incorporates one-dimensional convolutional layers to efficiently capture local continuity patterns within the flattened SSM representations while reducing parameter complexity. TsSMNet was used to generate seamless 9 km SSM data over China from 2016 to 2022, based on the SMAP product, and was evaluated using in situ observations from six networks in the International Soil Moisture Network. The results show that TsSMNet outperforms AutoResNet, Transformer, Random Forest and XGBoost models, reducing the root mean square error (RMSE) by an average of 17.1 percent and achieving a mean RMSE of 0.09 cm3/cm3. Feature importance analysis highlights the strong contribution of temporal predictors to model accuracy. Compared to its variant without time-series features, TsSMNet provides better spatial representation, improved consistency with in situ temporal observations, and enhanced evaluation metrics. The reconstructed product offers improved spatial coverage and continuity relative to the original SMAP data, supporting broader applications in regional-scale hydrological analysis and large-scale climate, ecological, and agricultural studies. Full article
(This article belongs to the Special Issue Remote Sensing for Natural Resources and Environmental Management of Arid and Semi-Arid Regions)
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32 pages, 9525 KB  
Article
Improving Remote Sensing Ecological Assessment in Arid Regions: Dual-Index Framework for Capturing Heterogeneous Environmental Dynamics in the Tarim Basin
by Yuxin Cen, Li He, Zhengwei He, Fang Luo, Yang Zhao, Jie Gan, Wenqian Bai and Xin Chen
Remote Sens. 2025, 17(21), 3511; https://doi.org/10.3390/rs17213511 - 22 Oct 2025
Viewed by 677
Abstract
Monitoring ecosystem dynamics in arid regions requires robust indicators that can capture spatial heterogeneity and diverse ecological drivers. In this study, we introduce and evaluate two novel ecological indices: the Arid-region Remote Sensing Ecological Index (ARSEI), specifically designed for desert environments, and the [...] Read more.
Monitoring ecosystem dynamics in arid regions requires robust indicators that can capture spatial heterogeneity and diverse ecological drivers. In this study, we introduce and evaluate two novel ecological indices: the Arid-region Remote Sensing Ecological Index (ARSEI), specifically designed for desert environments, and the Composite Remote Sensing Ecological Index (CoRSEI), which integrates both desert and non-desert systems. These indices are compared with the traditional Remote Sensing Ecological Index (RSEI) in the Tarim River Basin from 2000 to 2023. Principal component analysis (PCA) revealed that RSEI maintained the highest structural compactness (average PCA1 = 87.49%). In contrast, ARSEI (average PCA1 = 78.62%) enhanced sensitivity to albedo and vegetation (NDVI) in arid environments. Spearman correlation analysis further demonstrated that ARSEI was more strongly correlated with NDVI (ρ = 0.49) and precipitation (ρ = 0.62) than RSEI, confirming its improved responsiveness under water-limited conditions. CoRSEI exhibited higher internal consistency and spatial adaptability (mean values ranging from 0.45 to 0.56), with slight ecological improvements observed between 2000 and 2023. Ecological drivers varied across habitat types. In desert areas, evapotranspiration, precipitation, and soil moisture were the main determinants of ecological status, showing high coupling and synchrony. In non-desert regions, soil moisture and precipitation remained dominant, but vegetation indices and disturbance factors (e.g., fire density) exerted stronger long-term influences. Partial dependence analyses further confirmed nonlinear, region-specific responses, such as the threshold effects of precipitation on vegetation growth. Overall, our findings highlight the importance of differentiated ecological modeling. ARSEI enhances sensitivity in desert ecosystems, whereas CoRSEI captures landscape-scale variability across desert and non-desert regions. Both indices contribute to more accurate long-term ecological assessments in hyper-arid environments. Full article
(This article belongs to the Special Issue Remote Sensing for Natural Resources and Environmental Management of Arid and Semi-Arid Regions)
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