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Remote Sensing in Agricultural and Environmental Water Monitoring and Impact Assessment

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

Deadline for manuscript submissions: closed (10 October 2023) | Viewed by 10687

Special Issue Editors

National Climate Center, Beijing 100081, China
Interests: remote sensing application; soil moisture; machine learning; climate change
Chinese Academy of Meteorological Sciences, Beijing 100081, China
Interests: vegetation mapping; agricultural models; data assimilation; crop yield estimation

Special Issue Information

Dear Colleagues,

Due to global climate change and human activities, the frequency and intensity of extreme events, such as droughts and floods, have been increasing significantly in all world regions, with widespread consequences. Monitoring the distribution and variation patterns of drought and floods accurately and in a timely manner will help to address the grand challenges, thereby enhancing food and water security, ecosystem services, and human living environments. Along with the rapid development of remote sensing technology, a number of satellite-based methods have demonstrated their potential to observe water information and related disasters over large scales and across different spatial resolution. This Special Issue aims to present original and innovative research in applications of remote sensing in agricultural and ecological drought monitoring, soil moisture detection, flood and water resources extraction, and impact assessment of water-related disasters. Those papers will provide the readers of Remote Sensing with a wide range of satellite data analysis, big data processing, information management and visualization, earth science, computer science, and new principles, methods, and models regarding water sensing and mapping.

Contributions may be on—but not limited to—the following topics:

  • Agricultural drought monitoring;
  • Drought impacts on crops;
  • Ecological drought monitoring;
  • Soil moisture detection;
  • Flood monitoring and its impacts;
  • Patterns of water resources;
  • Relationship between water-related disasters and climate change.

Prof. Dr. Shibo Fang
Dr. Lei Wang
Dr. Wen Zhuo
Dr. Ce Zhang
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

  • remote sensing
  • agricultural drought
  • ecological drought
  • flood
  • soil moisture
  • water resource
  • impact assessment

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Published Papers (7 papers)

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25 pages, 5487 KiB  
Article
Estimation of the Water Level in the Ili River from Sentinel-2 Optical Data Using Ensemble Machine Learning
by Ravil I. Mukhamediev, Alexey Terekhov, Gulshat Sagatdinova, Yedilkhan Amirgaliyev, Viktors Gopejenko, Nurlan Abayev, Yan Kuchin, Yelena Popova and Adilkhan Symagulov
Remote Sens. 2023, 15(23), 5544; https://doi.org/10.3390/rs15235544 - 28 Nov 2023
Cited by 1 | Viewed by 824
Abstract
Monitoring of the water level and river discharge is an important task, necessary both for assessment of water supply in the current season and for forecasting water consumption and possible prevention of catastrophic events. A network of ground hydrometric stations is used to [...] Read more.
Monitoring of the water level and river discharge is an important task, necessary both for assessment of water supply in the current season and for forecasting water consumption and possible prevention of catastrophic events. A network of ground hydrometric stations is used to measure the water level and consumption in rivers. Rivers located in sparsely populated areas in developing countries of Central Asia have a very limited hydrometric network. In addition to the sparse network of stations, in some cases remote probing data (virtual hydrometric stations) are used, which can improve the reliability of water level and discharge estimates, especially for large mountain rivers with large volumes of suspended sediment load and significant channel instability. The aim of this study is to develop a machine learning model for remote monitoring of water levels in the large transboundary (Kazakhstan-People’s Republic of China) Ili River. The optical data from the Sentinel-2 satellite are used as input data. The in situ (ground-based) data collected at the Ili-Dobyn gauging station are used as target values. Application of feature engineering and ensemble machine learning techniques has achieved good accuracy of water level estimation (Nash–Sutcliffe model efficiency coefficient (NSE) >0.8). The coefficient of determination of the model results obtained using cross-validation of random permutations is NSE = 0.89. The method demonstrates good stability under different variations of input data and ranges of water levels (NSE > 0.8). The average absolute error of the method ranges from 0.12 to 0.18 meters against the background of the maximum river water level spread of more than 4 meters. The obtained result is the best current result of water level prediction in the Ili River using the remote probing data and can be recommended for practical use for increasing the reliability of water level estimation and reverse engineering of data in the process of river discharge monitoring. Full article
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28 pages, 25351 KiB  
Article
Temporal and Spatial Variations of Potential and Actual Evapotranspiration and the Driving Mechanism over Equatorial Africa Using Satellite and Reanalysis-Based Observation
by Isaac Kwesi Nooni, Faustin Katchele Ogou, Jiao Lu, Francis Mawuli Nakoty, Abdoul Aziz Saidou Chaibou, Birhanu Asmerom Habtemicheal, Linda Sarpong and Zhongfang Jin
Remote Sens. 2023, 15(12), 3201; https://doi.org/10.3390/rs15123201 - 20 Jun 2023
Cited by 1 | Viewed by 1396
Abstract
This study investigated the actual evapotranspiration (AET) and potential evapotranspiration (PET) seasonality, trends, abrupt changes, and driving mechanisms with global sea surface temperature (SST) and atmospheric circulation patterns over Equatorial Africa (EQA) during 1980–2020. The spatiotemporal characteristics of mean ET were computed based [...] Read more.
This study investigated the actual evapotranspiration (AET) and potential evapotranspiration (PET) seasonality, trends, abrupt changes, and driving mechanisms with global sea surface temperature (SST) and atmospheric circulation patterns over Equatorial Africa (EQA) during 1980–2020. The spatiotemporal characteristics of mean ET were computed based on a 40-year average at annual and seasonal scales. The Mann-Kendall statistical test, the Sen slope test, and the Bayesian test were used to analyze trends and detect abrupt changes. The results showed that the mean annual PET (AET) for 1980–2020 was 110 (70) mm. Seasonal mean PET (AET) values were 112 (72) in summer, 110 (85) in autumn, 109 (84) in winter, and 110 (58) in spring. The MK test showed an increasing (decreasing) rate, and the Sen slope identified upward (downward) at a rate of 0.35 (0.05) mm yr−10. The PET and AET abrupt change points were observed to happen in 1995 and 2000. Both dry and wet regions showed observed weak (strong) correlation coefficient values of 0.3 (0.8) between PET/AET and climate factors, but significant spatiotemporal differences existed. Generally, air temperature, soil moisture, and relative humidity best explain ET dynamics rather than precipitation and wind speed. The regional atmospheric circulation patterns are directly linked to ET but vary significantly in space and time. From a policy perspective, these findings may have implications for future water resource management. Full article
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21 pages, 13210 KiB  
Article
Revealing the Driving Mechanisms of Land Surface Temperature Spatial Heterogeneity and Its Sensitive Regions in China Based on GeoDetector
by Yanru Yu, Shibo Fang and Wen Zhuo
Remote Sens. 2023, 15(11), 2814; https://doi.org/10.3390/rs15112814 - 29 May 2023
Cited by 4 | Viewed by 1169
Abstract
Land surface temperature (LST) has a critical impact on the energy balance of land surface processes and ecosystem stability. Meanwhile, LST is controlled by multiple factors at the surface, resulting in heterogeneity of its spatial distribution. To understand the drivers of LST spatial [...] Read more.
Land surface temperature (LST) has a critical impact on the energy balance of land surface processes and ecosystem stability. Meanwhile, LST is controlled by multiple factors at the surface, resulting in heterogeneity of its spatial distribution. To understand the drivers of LST spatial heterogeneity and their contributions, the effects of air temperature, normalized difference vegetation index (NDVI), soil moisture, net surface radiation, precipitation, aerosol optical depth (AOD), evapotranspiration, water vapor, digital elevation model (DEM), climate type, and land cover type on LST spatial heterogeneity was analyzed in this study with GeoDetector. The results showed that the explanatory ability of air temperature to impact the spatial heterogeneity of LST was the largest in each year with a mean value of 0.74, followed by water vapor with a mean value of 0.7, and the driving effect of the factors on LST showed an increasing trend year by year. However, the land cover type did not have an effect on the spatial heterogeneity of LST for the univariate analysis in this study. In addition, the interaction analysis indicated that the spatial distribution of LST was jointly driven by all the driving factors. Among them, air temperature had the strongest interaction with other factors, with the strength of the effect in the range of 0.73–0.8. In terms of the highly sensitive area of LST for each driver, AOD has the largest driving area, accounting for 15.8% of the total area, followed by WV, TA, and ET at about 11%, and the remaining variables are less than 10%. During the study period, the area of the highly sensitive region of LST for each factor showed an overall decreasing trend, indicating that the influence of the driving factors on LST will be stronger and more concentrated. Generally, this study provides meaningful understanding of the spatial heterogeneity of LST since 2003 and provides a scientific reference for coping with climate change, analyzing surface environmental patterns, and protecting ecological environment. Full article
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20 pages, 7521 KiB  
Article
Quantitative Assessment of Cropland Exposure to Agricultural Drought in the Greater Mekong Subregion
by Wenting Ming, Xian Luo, Xuan Luo, Yunshu Long, Xin Xiao, Xuan Ji and Yungang Li
Remote Sens. 2023, 15(11), 2737; https://doi.org/10.3390/rs15112737 - 24 May 2023
Cited by 3 | Viewed by 1238
Abstract
Accurate and reliable information on the spatiotemporal characteristics of agricultural drought is important in understanding complicated drought processes and their potential impacts. We proposed an integrated approach for detecting agricultural droughts and their cropland exposure using remote sensing data over the Greater Mekong [...] Read more.
Accurate and reliable information on the spatiotemporal characteristics of agricultural drought is important in understanding complicated drought processes and their potential impacts. We proposed an integrated approach for detecting agricultural droughts and their cropland exposure using remote sensing data over the Greater Mekong Subregion (GMS) collected from 2001 to 2020. The soil moisture (SM) dataset (0.05°) was first reconstructed based on an ESACCI SM dataset using a random forest (RF) model. Subsequently, the standardized soil moisture index (SSMI) was used to identify the agricultural droughts by a three-dimensional (latitude-longitude-time) identification method. In addition, the cropland’s exposure to agricultural droughts was evaluated. Results showed that: (1) the reconstructed SM data achieved spatial continuity and improved spatial resolution. The verified consequences showed that the reconstructed SM data agreed well with the in situ SM data. Additionally, the SSMI based on reconstructed SM had good correlations with the standardized precipitation evapotranspiration index (SPEI) calculated from station observations. (2) Twenty agricultural drought events lasting at least 3 months were identified over the GMS region. The averaged durations, areas, and severity were 7 months, 9 × 105 km2, and 45.6 × 105 month·km2, respectively. The four worst drought events ranked by severity were the 2019–2020 event, the 2015–2016 event, the 2009–2010 event, and the 2004–2005 event. (3) Based on the 20 identified agricultural drought events, cropland exposure was high in Myanmar, Thailand, and Cambodia. On average, the cropland exposure over the GMS was 1.71 × 105 km2, which accounts for 34% of the total cropland. Notably, the four severest drought events swept over 80% of the total cropland area. This study enriched our understanding of the development process of agricultural droughts from a space-time perspective, which was pivotal for assessing drought impacts and managing agricultural water resources. Full article
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13 pages, 5800 KiB  
Article
Enhanced Impact of Vegetation on Evapotranspiration in the Northern Drought-Prone Belt of China
by Jian Zeng, Qiang Zhang, Yu Zhang, Ping Yue, Zesu Yang, Sheng Wang, Liang Zhang and Hongyu Li
Remote Sens. 2023, 15(1), 221; https://doi.org/10.3390/rs15010221 - 30 Dec 2022
Cited by 2 | Viewed by 1526
Abstract
Evapotranspiration (ET) is an essential component of the land–atmosphere water cycle. In this work, the trend of ET and its dominant factors during 1982 to 2011 are investigated in the northern drought-prone belt of China (NDPB) based on five datasets, including the gridded [...] Read more.
Evapotranspiration (ET) is an essential component of the land–atmosphere water cycle. In this work, the trend of ET and its dominant factors during 1982 to 2011 are investigated in the northern drought-prone belt of China (NDPB) based on five datasets, including the gridded FLUXNET, using the Pearson correlation and linear regression methods. Specially, we focus on the increasing contribution of vegetation in the change of ET. During 1982–2011, summer ET significantly increased at the rate of 0.33 mm/year (p < 0.05) in the NDPB. However, similar to global-mean ET, the ET in NDPB also experienced a pronounced fluctuation during 1999 and 2002. The role of water supply differed remarkably before and after the fluctuation while the atmospheric demand maintained weak constraint on ET. Before the fluctuation (during 1982–2000), ET correlated significantly (p < 0.01) and positively with soil moisture, indicating ET was primarily limited by water supply. However, their correlation weakened remarkably after the fluctuation when soil moisture decreased to the lowest level for the past thirty years, indicating that neither moisture supply nor atmospheric demand dominated the ET during this period. In contrast, vegetation leaf area index (LAI) maintained consistent significant (p < 0.01) and positive correlation with ET before and after the fluctuation in the NDPB, and it reflected over 60% of the change in ET. Moreover, the LAI in NDPB increased by 19.6% which was more than double of the global-mean increase. The ET increase due to rising LAI offset the ET decrease due to reduction of soil moisture, and vegetation became the primary constraint on ET during 2001–2011. The expansion of vegetation may intensify the risk of drought and cause conflicting demands for water between the ecosystem and humans in the NDPB, especially in the case of weak summer monsoon. Full article
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18 pages, 4885 KiB  
Article
Primary Interannual Variability Patterns of the Growing-Season NDVI over the Tibetan Plateau and Main Climatic Factors
by Xin Mao, Hong-Li Ren and Ge Liu
Remote Sens. 2022, 14(20), 5183; https://doi.org/10.3390/rs14205183 - 17 Oct 2022
Cited by 6 | Viewed by 1349
Abstract
The Tibetan Plateau (TP) vegetation plays an important role in the local ecosystem, which responds significantly to climate change and can affect local and large-scale weather and climate anomalies. However, little attention has been paid to its year-to-year variation. In this paper, using [...] Read more.
The Tibetan Plateau (TP) vegetation plays an important role in the local ecosystem, which responds significantly to climate change and can affect local and large-scale weather and climate anomalies. However, little attention has been paid to its year-to-year variation. In this paper, using two NDVI datasets (GIMMS and MODIS) originated from satellite remote sensing, the variability characteristics of NDVI over the TP on the interannual time scale and associated local climatic factors were investigated. The results show that two primary patterns of NDVI governed TP during the main growing season (June–September, JJAS) for the period 1982–2020. The first one is a uniform pattern, with a consistent spatial variation over the entire TP, and the second is a dipole pattern, with an out-of-phase spatial variation of NDVI between the northern and southern TP. Interannual variations of the different climatic factors regulate the NDVI variability over the different regions of the TP. The interannual variability of the uniform NDVI pattern is mainly affected by the two local climatic factors, the preceding May–August precipitation and simultaneous JJAS sunshine duration. Specifically, NDVIs over the southern and eastern TP have a more significant response to the preceding precipitation and simultaneous sunshine duration, respectively. The variability of the dipole NDVI pattern is primarily modulated by the preceding May–August precipitation and simultaneous surface air temperature, ground surface temperature, and sunshine duration. However, NDVIs over the northern and southern TP have different degrees of response to the four climatic factors, with the most significant response being to preceding precipitation. The combined effect of these factors contributes to the formation of the interannual variability in the uniform and dipole patterns. This paper may shed light on deeply understanding the reasons for the inconsistency in variations of vegetation over the different regions of the TP under climate change. In addition to the effect of local climatic factors that this study focuses on, the influence of external climatic factors on the variability of the TP NDVI deserves further research in the future. Full article
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17 pages, 3735 KiB  
Technical Note
Extraction of Winter Wheat Planting Area Based on Multi-Scale Fusion
by Weiguo Li, Hong Zhang, Wei Li and Tinghuai Ma
Remote Sens. 2023, 15(1), 164; https://doi.org/10.3390/rs15010164 - 28 Dec 2022
Cited by 4 | Viewed by 1318
Abstract
It is difficult to accurately identify the winter wheat acreage in the Jianghuai region of China, and the fusion of high-resolution images and medium-resolution image data can improve the image quality and facilitate the identification and acreage extraction of winter wheat. Therefore, the [...] Read more.
It is difficult to accurately identify the winter wheat acreage in the Jianghuai region of China, and the fusion of high-resolution images and medium-resolution image data can improve the image quality and facilitate the identification and acreage extraction of winter wheat. Therefore, the objective of this study is to improve the accuracy of China’s medium-spatial resolution image data (environment and disaster monitoring and forecasting satellite data, HJ-1/CCD) in extracting the large area of winter wheat planted. The fusion and object-oriented classification of the 30 m × 30 m HJ-1/CCD multispectral image and 2 m × 2 m GF-1 panchromatic image (GF-1/PMS) of winter wheat at the jointing stage in the study area were studied. The GF-1/PMS panchromatic images were resampled at 8 m, 16 m and 24 m to produce panchromatic images with four spatial resolutions, including 2 m. They were fused with HJ-1/CCD multispectral images by Gram Schmidt (GS). The quality of the fused images was evaluated to pick adequate scale images for the field pattern of winter wheat cultivation in the study area. The HJ-1/CCD multispectral image was resampled to obtain an image with the same scale as the suitable scale fused image. In the two images, the training samples SFI (samples of fused image) and SRI (samples of resampled image) containing spectral and texture information were selected. The fused image (FI) and resampled image (RI) were used for winter wheat acreage extraction using an object-oriented classification method. The results indicated that the fusion effect of 16 m × 16 m fused image was better than 2 m × 2 m, 8 m × 8 m and 24 m × 24 m fused images, with mean, standard deviation, average gradient and correlation coefficient values of 161.15, 83.01, 4.55 and 0.97, respectively. After object-oriented classification, the overall accuracy of SFI for the classification of resampled image RI16m was 92.22%, and the Kappa coefficient was 0.90. The overall accuracy of SFI for the classification of fused image FI16m was 94.44%, and the Kappa coefficient was 0.93. The overall accuracy of SRI for the classification of resampled image RI16m was 84.44%, and the Kappa coefficient was 0.80. The classification effect of SFI for the fused image FI16m was the best, indicating that the object-oriented classification method combined with the fused image and the extraction samples of the fused image (SFI) could extract the winter wheat planting area with precision. In addition, the object-oriented classification method combining resampled images and the extraction samples of fused images (SFI) could extract the winter wheat planting area more effectively. These results indicated that the combination of medium spatial resolution HJ-1/CCD images and high spatial resolution GF-1 satellite images could effectively extract the planting area information of winter wheat in large regions. Full article
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