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Atmosphere
Special Issues
Observation of Climate Change and Cropland with Satellite Data
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Observation of Climate Change and Cropland with Satellite Data

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".

Deadline for manuscript submissions: 31 August 2025 | Viewed by 1817

Special Issue Editor

Dr. Mengmeng Hu

E-Mail
Guest Editor
Chinese Academy of Agricultural Sciences, Beijing, China
Interests: climate change; drought; land use change; agricultural remote sensing; environmental economics

Special Issue Information

Dear Colleagues,

Climate change poses significant risks and uncertainties to food production. It not only impacts the hydrological cycle by altering elements such as precipitation and evaporation but also directly affects regional cropland utilization. As one of the land uses most severely impacted, cropland is highly sensitive to climate change. Satellite technology offers extensive, continuous, and long-term data coverage, enabling the precise monitoring of temperature, precipitation patterns, greenhouse gases, spatial–temporal distributions of cropland, crop health, and yield forecasting. This information is crucial for developing effective strategies for precision agriculture, disaster management, and policies aimed at mitigating the adverse effects of climate change. Utilizing satellite data to observe climate change and cropland is essential for comprehensively understanding the impacts of climate dynamics on cropland, agricultural productivity, and the sustainable use of agricultural resources.

The data sources used include remote sensing data, UAV data, and ground observation data, with the encouragement to use advanced information technologies such as AI, machine learning, and data mining. This Special Issue aims to disseminate the latest research and applications of remote sensing technology in climate change and farmland monitoring. Potential research topics include, but are not limited to, the following:

  • Satellite technology and advancements in climate change observation;
  • Remote sensing for cropland monitoring and management;
  • Impact of drought and flood risks on cropland, crop health, yield, etc.;
  • Integration of satellite data with ground-based observations;
  • Climate modeling and simulation;
  • Cross-disciplinary approaches combining satellite data with other technologies.

Dr. Mengmeng Hu
Guest Editor

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. 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

  • climate change
  • cropland monitoring
  • machine learning
  • drought and flood risks
  • remote sensing

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Published Papers (3 papers)

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Research

22 pages, 5718 KiB  
Article
Drought Monitoring in the Agrotechnological Districts of the Semear Digital Center
by Tamires Lima da Silva, Luciana Alvim Santos Romani, Silvio Roberto Medeiros Evangelista, Mihai Datcu and Silvia Maria Fonseca Silveira Massruhá
Atmosphere 2025, 16(4), 465; https://doi.org/10.3390/atmos16040465 - 17 Apr 2025
Viewed by 304
Abstract
Drought affects the agricultural sector, posing challenges for farm management, particularly among medium- and small-scale producers. This study uses climate data from remote sensing products to evaluate drought trends in the Semear Digital Center’s Agrotechnological Districts (DATs), which are characterized by a high [...] Read more.
Drought affects the agricultural sector, posing challenges for farm management, particularly among medium- and small-scale producers. This study uses climate data from remote sensing products to evaluate drought trends in the Semear Digital Center’s Agrotechnological Districts (DATs), which are characterized by a high concentration of small- and medium-sized farms in Brazil. Precipitation data from Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) and land surface temperature data from Moderate Resolution Imaging Spectroradiometer (MODIS) were applied to calculate the Standardized Precipitation–Evapotranspiration Index (SPEI) for a 6-month timescale from 2000 to 2024, with analysis divided into 2000–2012 and 2013–2024. Some limitations were noted: MODIS systematically underestimated temperatures, while CHIRPS tended to underestimate precipitation for most of the DATs. Despite discrepancies, these datasets remain valuable for drought monitoring in areas where long-term ground weather station data are lacking for SPEI assessments. Agricultural drought frequency and severity increased in the 2013–2024 period. Exceptional, extreme, severe, and moderate drought events rose by 7.3, 5.4, 2.2 and 1.0 times, respectively. These trends highlight the importance of adopting smart farming technologies to enhance the resilience of the DATs to climate change. Full article
(This article belongs to the Special Issue Observation of Climate Change and Cropland with Satellite Data)
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25 pages, 6362 KiB  
Article
Assessing Climate Change Impacts on Cropland and Greenhouse Gas Emissions Using Remote Sensing and Machine Learning
by Nehir Uyar and Azize Uyar
Atmosphere 2025, 16(4), 418; https://doi.org/10.3390/atmos16040418 - 3 Apr 2025
Viewed by 347
Abstract
This study investigated the impact of grassland and cropland expansion on carbon (C) and nitrous oxide (N2O) emissions using remote sensing data and machine learning models. The research focused on agricultural land-use changes in South Sumatra from 1992 to 2018, utilizing [...] Read more.
This study investigated the impact of grassland and cropland expansion on carbon (C) and nitrous oxide (N2O) emissions using remote sensing data and machine learning models. The research focused on agricultural land-use changes in South Sumatra from 1992 to 2018, utilizing Landsat satellite imagery and Google Earth Engine (GEE) for spatial and temporal analysis. Machine learning algorithms, including gradient boosting trees (GBT), random forest (RF), support vector machines (SVM), and classification and regression trees (CART), were employed to estimate greenhouse gas emissions based on multiple environmental parameters. These parameters include enhanced vegetation index (EVI), land surface temperature (LST), normalized difference vegetation index (NDVI), albedo, elevation, humidity, population density, precipitation, soil moisture, and wind speed. The results revealed a strong correlation between agricultural expansion and increased C and N2O emissions, with RF and GBT models demonstrating superior predictive accuracy. Specifically, GBT and RF achieved the highest R2 value (0.71, 0.59) and the lowest error metrics in modeling emissions, whereas SVM performed poorly across all cases. The study highlights the effectiveness of machine learning in quantifying emission dynamics and underscores the necessity of sustainable land management strategies to mitigate greenhouse gas emissions. By integrating remote sensing and data-driven methodologies, this research contributes to climate change mitigation policies and precision agriculture strategies aimed at balancing food security and environmental sustainability. Full article
(This article belongs to the Special Issue Observation of Climate Change and Cropland with Satellite Data)
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18 pages, 3901 KiB  
Article
Assessing the Effects of Wheat Planting on Groundwater Under Climate Change: A Quantitative Adaptive Sliding Window Detection Strategy
by Lingling Fan, Shi Chen, Lang Xia, Yan Zha and Peng Yang
Atmosphere 2024, 15(12), 1501; https://doi.org/10.3390/atmos15121501 - 16 Dec 2024
Viewed by 694
Abstract
Climate change has led to changes in precipitation patterns, exacerbating the overextraction of groundwater for wheat irrigation. Although many studies have examined the effects of wheat cultivation on groundwater storage (GWS), few studies have directly assessed the effects of wheat planting on GWS. [...] Read more.
Climate change has led to changes in precipitation patterns, exacerbating the overextraction of groundwater for wheat irrigation. Although many studies have examined the effects of wheat cultivation on groundwater storage (GWS), few studies have directly assessed the effects of wheat planting on GWS. We proposed a wheat subsiding effect detection (WSED) strategy using time-series remote sensing image to assess the effect of wheat area on GWS across China. The subsiding magnitude of the WSED is calculated as the GWS difference between the wheat area and adjacent nonwheat area in the self-adaptive moving window (the size and position of the sliding window can be automatically adjusted based on the characteristics of the data at the central pixel location). The effects of the wheat area on groundwater storage differ greatly among the change types of wheat area and planting regionalization, characterized by the strong subsiding effect in the wheat stable area, gain area, and Huanghuaihai zone (HWW, the most important wheat-producing region in China mainly includes the provinces and municipalities of Beijing, Tianjin, Henan, Hebei, Shandong, Anhui, and Jiangsu). Nearly 80% of the wheat area in the stable and gain regions had lower groundwater depth than nonwheat areas with significant differences (p < 0.05), resulting in a clear declining groundwater trend of approximately −1 cm/year. This study provides quantitative evidence for the effects of wheat planting on GWS regarding agricultural production and climate change adaptations. Full article
(This article belongs to the Special Issue Observation of Climate Change and Cropland with Satellite Data)
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