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Atmosphere
Special Issues
Vegetation–Atmosphere Interactions in a Changing Climate
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Vegetation–Atmosphere Interactions in a Changing Climate

A special issue of Atmosphere (ISSN 2073-4433).

Deadline for manuscript submissions: 31 May 2026 | Viewed by 3569

Special Issue Editors

Dr. Guirong Hou

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Guest Editor
College of Forestry, Sichuan Agricultural University, Chengdu 611130, China
Interests: forest management; soil hydrology; root ecology
Special Issues, Collections and Topics in MDPI journals
Dr. Xiaomin Chang

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Guest Editor
College of Forestry, Shanxi Agricultural University, Jinzhong, China
Interests: soil and water conservation; wind erosion; PM concentration; forest ecology; forest management
Dr. Guangpeng Fan

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Guest Editor
School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China
Interests: LiDAR; point cloud; geophysical image processing; image classification; GIS; RS; forest
Dr. Lin Xiao

E-Mail Website
Guest Editor
National Forestry and Grassland Administration Key Laboratory of Forest Resources Conservation and Ecological Safety on the Upper Reaches of the Yangtze River, College of Forestry, Sichuan Agricultural University, Chengdu 611130, China
Interests: remote sensing on snow; ecological remote sensing; remote sensing in forestry; forest system and climate change

Special Issue Information

Dear Colleagues,

The study of the climate has been evolving for decades. While scholarship has always focused on temperature, ecosystems, etc., when it comes to climate change, there has been a recent shift in the priority of this issue relative to other issues, with a new focus on vegetation–atmosphere interactions in a changing climate.

In light of this shift in focus, the open access journal Atmosphere will host a Special Issue on water transport in arid regions, forest ecohydrology, regional climate change, the restoration of vegetation degradation, remote sensing on snow, forest systems and climate change, and more. This Special Issue is also an appropriate venue for papers dealing with human thermal comfort and productivity, as recent research shows that desertification can contribute greatly to climate change. Ultimately, this Special Issue aims to present the latest comparable evidence on the impacts of desertification.

Raw results from subjective surveys, models, and review papers related to climate and forest hydrology in decertified regions are welcome. Authors are encouraged to include sections that address future issues, opportunities, and/or concerns related to their topic in 5-, 10-, and 20-year horizons.

Dr. Guirong Hou
Dr. Xiaomin Chang
Dr. Guangpeng Fan
Dr. Lin Xiao
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. Atmosphere is an international peer-reviewed open access monthly 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 2400 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

  • remote sensing
  • water and soil conservation
  • climate change
  • forest ecology
  • forest hydrology
  • structured forest management
  • wind erosion
  • particular matter concentration
  • forest LiDAR
  • landscape ecology

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Published Papers (3 papers)

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Research

20 pages, 4038 KB  
Article
Dynamics of Soil Moisture and Its Response to Meteorological Factors at Different Depths in an Arid Land, Northwest China
by Wenye Li, Wenpeng Li, Yuejun Zheng, Xusheng Wang and Xiaofan Qi
Atmosphere 2026, 17(3), 232; https://doi.org/10.3390/atmos17030232 - 25 Feb 2026
Viewed by 598
Abstract
Soil moisture is a critical variable in the eco-hydrological processes of arid regions; however, the vertical stratified mechanisms of soil moisture response to meteorological factors in artificial grassland remain inadequately quantified. Based on 10-min interval monitoring data from 2015 to 2024 in the [...] Read more.
Soil moisture is a critical variable in the eco-hydrological processes of arid regions; however, the vertical stratified mechanisms of soil moisture response to meteorological factors in artificial grassland remain inadequately quantified. Based on 10-min interval monitoring data from 2015 to 2024 in the middle reaches of the Heihe River, this study investigated the dynamics of soil moisture within a 0–160 cm depth profile in an arid artificial grassland. By integrating the Mann–Kendall trend test, Pearson correlation, time-lagged cross-correlation, multiple regression analysis and redundancy analysis, we systematically investigated the changing relationships between meteorological factors and soil moisture. The results revealed the following: (1) main meteorological factors driving surface processes (e.g., net radiation, air temperature, vapor pressure deficit) showed significant increasing trends with strong variability, while relative humidity decreased significantly, and these findings collectively point to a general trend of warming and drying in the region; (2) WS, Ta, rainfall, and RH are the principal factors explaining soil moisture variations, wherein temperature and humidity exhibit positive correlations with soil moisture; (3) RDA results showed that shallow soil moisture (0–20 cm) was primarily governed by air temperature and rainfall, whereas deep soil moisture was increasingly regulated by vapor pressure deficit; (4) time-lagged cross-correlation analysis showed that the response time of soil moisture to rainfall almost increased with soil depth, while the correlation coefficient gradually weakened from 0.43 to 0.06. This study quantitatively elucidates the stratified control mechanism of meteorological factors on the vertical pattern of soil moisture, contributing to a deeper understanding of the response of eco-hydrological processes under climate change and providing a scientific basis for water resource management, agricultural planning, and climate prediction. Full article
(This article belongs to the Special Issue Vegetation–Atmosphere Interactions in a Changing Climate)
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24 pages, 31821 KB  
Article
Response of Vegetation Net Primary Productivity to Extreme Climate in a Climate Transition Zone: Evidence from the Qinling Mountains
by Qiuqiang Zeng and Chengyuan Hao
Atmosphere 2025, 16(10), 1208; https://doi.org/10.3390/atmos16101208 - 18 Oct 2025
Cited by 2 | Viewed by 851
Abstract
The Qinling Mountains, situated in the climatic transition zone between northern and southern China, represent a critical region for climate and ecological studies due to their unique transitional characteristics and the rising frequency of extreme climate events. As net primary productivity (NPP) is [...] Read more.
The Qinling Mountains, situated in the climatic transition zone between northern and southern China, represent a critical region for climate and ecological studies due to their unique transitional characteristics and the rising frequency of extreme climate events. As net primary productivity (NPP) is a key indicator of ecosystem stability, clarifying its response to extreme climate events is essential for understanding ecological resilience in this region. In this study, daily observational data from 123 meteorological stations (1960–2023) were used to derive eight extreme temperature and precipitation indices. Combined with MODIS NPP data (2001–2023), we applied Theil–Sen slope estimation, Mann–Kendall significance testing, ridge regression, Pearson correlation analysis, and Moran’s I spatial autocorrelation to systematically investigate the spatiotemporal dynamics and driving mechanisms of NPP. The main findings are as follows: (1) From 2001 to 2023, the mean annual NPP in the Qinling region was 558.43 ± 134.27 gC·m−2·year−1, showing a significant increasing trend of 5.44 gC·m−2·year−1 (p < 0.05). (2) Extreme temperature indices exhibited significant changes, whereas among the precipitation indices, only the number of days with daily precipitation ≥ 20 mm (R20) showed a significant trend, suggesting that extreme temperatures exert a stronger influence in the region. (3) Correlation analysis indicated that temperature-related indices were generally positively correlated, precipitation-related indices displayed even stronger associations, and covariation existed among extreme precipitation events of varying intensities. Moreover, precipitation indices demonstrated relatively stable spatial distributions, while temperature indices fluctuated considerably. (4) Absolute contribution analysis further revealed that the number of days with daily minimum temperature below the 10th percentile (TN10p) contributed up to 3.53 gC·m−2·year−1 to annual NPP variation in the Henan subregion, whereas maximum rainfall over five consecutive days (Rx5day) exerted an overall negative effect on NPP (−0.77 gC·m−2·year−1). By integrating long-term meteorological observations with remote sensing products, this study quantitatively evaluates the differential impacts of extreme climate events on vegetation within a climatic transition zone, offering important implications for ecological conservation and adaptive management in the Qinling Mountains. Full article
(This article belongs to the Special Issue Vegetation–Atmosphere Interactions in a Changing Climate)
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22 pages, 3221 KB  
Article
Exploring NDVI Responses to Regional Climate Change by Leveraging Interpretable Machine Learning: A Case Study of Chengdu City in Southwest China
by Ying Xiang, Guirong Hou, Junjie Li, Yidan Zhang, Jie Lu, Zhexiu Yu, Fabao Niu and Hanqing Yang
Atmosphere 2025, 16(8), 974; https://doi.org/10.3390/atmos16080974 - 17 Aug 2025
Cited by 1 | Viewed by 1626
Abstract
Regional extreme climate change remains a major environmental issue of global concern. However, in the context of the joint effects of urban expansion and the urban ecological environment, the responses of the normalized difference vegetation index (NDVI) to regional climate change and its [...] Read more.
Regional extreme climate change remains a major environmental issue of global concern. However, in the context of the joint effects of urban expansion and the urban ecological environment, the responses of the normalized difference vegetation index (NDVI) to regional climate change and its driving mechanism remain unclear. This study takes Chengdu as an example, selects the air temperature (Ta), precipitation (P), wind speed (WS), and soil water content (SWC) within the period from 2001 to 2023 as influencing factors, and uses Theil-Sen median trend analysis and interpretable machine learning models (random forest (RF), BP neural network, support vector machine (SVM), and extreme gradient boosting (XG-Boost) models). The average absolute value of Shapley additive explanations (SHAPs) is adopted as an indicator to explore the key mechanism driving regional climate change in Chengdu in terms of NDVI changes. The analysis results reveal that the NDVI exhibited an extremely significant increasing trend during the study period (p = 8.6 × 10−6 < 0.001), and that precipitation showed a significant increasing trend (p = 1.2 × 10−4 < 0.001); however, the air temperature, wind speed, and soil-relative volumetric water content all showed insignificant increasing trends. A simulation of interpretable machine learning models revealed that the random forest (RF) model performed exceptionally well in terms of simulating the dynamics of the urban NDVI (R2 = 0.746), indicating that the RF model has an excellent ability to capture the complex ecological interactions of a city without prior assumptions. The dependence relationship between the simulation results and the main driving factors indicates that the Ta and P are the main factors affecting the NDVI changes. In contrast, the SWC and WS had relatively small influences on the NDVI changes. The prediction analysis results reveal that a monthly average temperature of 25 °C and a monthly average precipitation of approximately 130 mm are conducive to the stability of the NDVI in the study area. This study provides a reference for exploring the responses of NDVI changes to regional climate change in the context of urban expansion and urban ecological construction. Full article
(This article belongs to the Special Issue Vegetation–Atmosphere Interactions in a Changing Climate)
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