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Remote Sensing for Environmental Monitoring in Cold and Arid Regions: Advances and Perspectives

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

Deadline for manuscript submissions: 31 July 2026 | Viewed by 2898

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Special Issue Editors

Dr. Lihui Luo

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Guest Editor

Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
Interests: UAV remote sensing; AI; spatial modeling

Dr. Jie Xue

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Guest Editor

State Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
Interests: ecohydrology; restoration ecology; water resouces and cycling in drylands
Special Issues, Collections and Topics in MDPI journals

Dr. Miao Zhang

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Guest Editor

School of Geography and Tourism, Shaanxi Normal University, Xi'an, China
Interests: water cycle in arid zones; remote sensing
Special Issues, Collections and Topics in MDPI journals

Dr. Mingyang Lv

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Guest Editor

Institute of Space Earth Science, Nanjing University, Suzhou 215163, China
Interests: lunar-based earth observation; cryosphere remote sensing; AI4Mapping; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cold and arid regions, spanning high-latitude and high-altitude zones, are experiencing accelerated environmental changes driven by global warming, including glacier retreat, permafrost degradation, desertification, and ecosystem shifts. These transformations pose significant threats to water security, biodiversity, and human livelihoods. Remote sensing technologies—ranging from satellite imagery to AI-driven data analytics—have emerged as pivotal tools for monitoring these vast, inaccessible landscapes. However, challenges persist in data accuracy, model generalizability, and interdisciplinary integration, necessitating innovative approaches to advance the field.

This Special Issue aims to consolidate cutting-edge research on remote sensing applications in cold and arid environments. We seek contributions that address methodological advances, empirical discoveries, and future-oriented perspectives to enhance the precision, scalability, and societal relevance of environmental monitoring. By bridging theoretical innovation with practical solutions, this collection will support sustainable management and policy-making in vulnerable ecosystems.

Articles may address, but are not limited, to the following topics:

Advanced sensing technologies;
AI and data fusion;
Environmental process monitoring;
Cross-scale modeling;
Human–environment interactions;
New datasets and validation.

Dr. Lihui Luo
Dr. Jie Xue
Dr. Miao Zhang
Dr. Mingyang Lv
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

cold and arid region
frozen soil monitoring
glacier monitoring
desert monitoring
data fusion
spatial modeling

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Published Papers (3 papers)

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Research

31 pages, 6468 KB

Open AccessArticle

Groundwater Level Response Processes in Arid Northwest China Based on Remote Sensing and Causal Inference: From Influential Variables to Transmission Pathways

by Liang Zeng and Shaohui Chen

Remote Sens. 2026, 18(9), 1378; https://doi.org/10.3390/rs18091378 - 29 Apr 2026

Viewed by 132

Abstract

Groundwater level (GWL) variations in the arid regions of Northwest China are driven by both natural processes and human activities. Identifying causal links between hydrological variables is fundamental to understanding groundwater evolution and conducting dynamic simulations. This study integrates the Mann–Kendall test, Seasonal-Trend decomposition using Loess, and the Peter and Clark Momentum-threshold and Momentary Conditional Independence (PCMCI) causal inference to analyze GWL variation characteristics and causal response processes across seven sub-basins in the Tarim Basin using multi-source remote sensing data. Results show an overall decline in GWL, primarily in the north-central part of the basin, with the Kaidu–Konqi River Basin reaching a maximum rate of 0.51 m/year. The trend components reveal localized depletion alongside broad stability, while seasonal components exhibit three types of temporal shifts in fluctuations. A mismatch exists between the prevalence of environmental influences and their causal strength. Daytime land surface temperature (LST_D), surface runoff (RO), and evapotranspiration (ET) show the highest detection frequencies, yet volumetric soil water in layers 2 (SWVL₂) and RO exhibit the largest ranges in strength and drive variations at specific sites. Response times are asymmetric. Negative effects from ET on GWL transmit quickly, while positive recovery is slow. Conversely, positive recharge from volumetric soil water in layer 1 (SWVL₁) is faster than its negative lag. At the basin scale, surface processes recharge GWL while mediating indirect influences from other variables. Climate and agricultural irrigation act as direct sinks. Depending on local conditions, three regional patterns emerge: direct climate-driven depletion, obstructed shallow water retention, and indirect compensation from agricultural water use. Causal networks indicate that RO and SWVL₁ have the highest centrality and dominate water output, whereas SWVL₂ acts as a passive receiver. Pathways from the surface to GWL are also asymmetric. The most frequent path involves step-by-step infiltration along RO → ET → SWVL₁ → SWVL₂ → GWL. In contrast, the paths with the highest cumulative strength are shorter and faster, specifically RO → ET → GWL and RO → SWVL₁ → GWL. The identified pathways and lag parameters provide a direct basis for groundwater dynamic modeling and water resource management in the basin. Full article

(This article belongs to the Special Issue Remote Sensing for Environmental Monitoring in Cold and Arid Regions: Advances and Perspectives)

28 pages, 7267 KB

Open AccessArticle

Cryosphere Ecological Vulnerability in the Qilian Mountains Region: Trends, Drivers, and Adaptation

by Xiaoya Yi, Xingyu Xue, Changsheng Lu, Bowen Li, Mengyuan Liu, Jizu Chen, Youyan Jiang and Wentao Du

Remote Sens. 2026, 18(2), 268; https://doi.org/10.3390/rs18020268 - 14 Jan 2026

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Abstract

The rapid shrinkage of the climate-regulating cryosphere, driven by global warming and anthropogenic activities, underscores the urgency of understanding its impact on regional ecological vulnerability. This study develops a Sensitivity–Resilience–Pressure (SRP) model-based framework comprising 21 natural and socio-economic indicators, employs spatial autocorrelation and center of gravity migration to characterize spatiotemporal patterns in the Qilian Mountains region, and integrates Random Forests (RF) with Shapley Additive Explanations (SHAP) to identify key drivers. Results reveal a downward trend in the Ecological Vulnerability Index (EVI) from 2000 to 2020, with areas of very heavy vulnerability declining from 21.05% to 14.73%, indicating gradual ecological recovery. The study area exhibits moderate vulnerability, with the western region dominated by heavy and very heavy vulnerability, whereas the eastern region is characterized by potential and light vulnerability, indicating a high-west, low-east spatial pattern. A significant positive spatial autocorrelation is observed, revealing that areas with high vulnerability are highly clustered and primarily overlap with regions of high elevation and sparse vegetation. The RF–SHAP analysis demonstrates that natural factors dominate the EVI, with fractional vegetation cover, biological abundance, glacial meltwater volume, annual precipitation, and the landscape diversity index emerging as the main drivers, and the EVI changing sequentially as each indicator approaches its threshold: 0.16, 56.57, 2.23 mm, 400.73 mm, and 0.39. In conclusion, although ecological vulnerability in the Qilian Mountains has declined, future management strategies should leverage these threshold effects to implement precise, indicator-based monitoring and regulation. Full article

(This article belongs to the Special Issue Remote Sensing for Environmental Monitoring in Cold and Arid Regions: Advances and Perspectives)

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17 pages, 5663 KB

Open AccessArticle

Evaluating the Performance of Satellite-Derived Vegetation Indices in Gross Primary Productivity (GPP) Estimation at 30 m and 500 m Spatial Resolution

by Deli Cao, Xiaojuan Huang, Gang Liu, Lingwen Tian, Qi Xin and Yuli Yang

Remote Sens. 2025, 17(19), 3291; https://doi.org/10.3390/rs17193291 - 25 Sep 2025

Cited by 1 | Viewed by 1795

Abstract

Vegetation indices (VIs) have been extensively employed as proxies for gross primary productivity (GPP). However, it is unclear how the spatial resolution effects the performance of VIs in GPP estimation in different biomes when matching the flux tower footprint. Here, we examined the relationship with tower GPP between Landsat-footprint VIs and MODIS-footprint VIs. We first calculated Landsat-footprint VIs (e.g., Normalized Difference Vegetation Index (NDVI), enhanced vegetation index (EVI), two-band EVI (EVI2), near-infrared reflectance of vegetation (NIRv) and kernel Normalized Difference Vegetation Index (kNDVI)) averaged over all the pixels within the footprint and MODIS-footprint VIs with 3 × 3 km or 1 × 1 km around the tower, respectively. We then examined the relationship between Landsat- and MODIS-footprint VIs and tower GPP at monthly scale over 76 FLUXNET sites across ten vegetation types worldwide. The results showed that Landsat-footprint VIs had stronger performance than MODIS-footprint VIs for GPP estimation in all ecosystems, with high improvement on croplands, wetlands, and grasslands and moderate improvements on mixed forest, evergreen needleleaf forest, and deciduous broadleaf forest. Moreover, NIRv showed a stronger correlation with tower-based GPP than NDVI, EVI, EVI2, and kNDVI in ten ecosystems both at 30 m and 500 spatial resolutions. Our findings highlighted the critical role of VIs with high spatial resolution and footprint-aware matching in GPP estimation. Full article

(This article belongs to the Special Issue Remote Sensing for Environmental Monitoring in Cold and Arid Regions: Advances and Perspectives)

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