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Remote Sensing of Watershed
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Remote Sensing of Watershed

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 48391

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Dr. Jingzhe Wang

E-Mail Website
Guest Editor
School of Artificial Intelligence, Shenzhen Polytechnic, Shenzhen 518055, China
Interests: earth observation and remote sensing; spectral modeling; quantitative estimation of soil properties; digital soil mapping; GIS; spatial analysis; environmental sustainability
Special Issues, Collections and Topics in MDPI journals
Dr. Zhongwen Hu

E-Mail Website
Guest Editor
MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area & Guangdong Key Laboratory of Urban Informatics & Guangdong–Hong Kong-Macau Joint Laboratory for Smart Cities & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China
Interests: environmental remote sensing; remote sensing image classification; photogrammetry; sensor/data integration; vegetation mapping; unmanned aerial systems; object-based image analysis
Dr. Yangyi Wu

E-Mail Website
Guest Editor
Department of Geography, University of Utah, Salt Lake City, UT 84112-9155, USA
Interests: regional sustainable development; spatial modeling; statistics; spatiotemporal analysis; land use policy; sustainability in land management processes and urban planning; urban sprawl; coupled human-natural systems; environmental countermeasures
Special Issues, Collections and Topics in MDPI journals
Dr. Jie Zhang

E-Mail Website
Guest Editor
College of Information and Electrical Engineering, China Agricultural University, Beijing 100085, China
Interests: remote sensing; regional sustainable development; biogeochemical cycle and modeling; hydrological modeling; water quality monitoring; environmental management

Special Issue Information

Dear Colleagues,

As a nature unit, the watershed is the primary carrier of the global terrestrial water cycle. Due to its significant role in the nature system and human society, such as in water accessibility, agriculture, and human habitats, it has been a hot research topic recently. As a powerful tool in geographical analysis, remote sensing has been widely applied in watershed research because of its capacity to monitor Earth surface processes, measure human activities, and assess ecosystem services. In return, the watershed is a key research object in hydrology remote sensing studies. On the one hand, watershed hydrology remote sensing applies advanced concepts and the cutting edge of current hydrology remote sensing, such as satellite networking and full-spectrum inversion. On the other hand, the watershed is a unique study object that is more informative than single remote sensing pixels, resulting in uniqueness in terms of research content, methodologies, technical means, and practical applications. For these reasons, this Special Issue is dedicated to scientific reports on the remote sensing of watershed. This issue focuses on applying remote sensing techniques in watersheds in terms of hydrology, ecology, environment, and human activities.

Welcome topics include but are not limited to the following:

  • Data fusion of remote sensing data across the frequency spectrum (microwave to optical) and techniques (active and passive);
  • Development of novel applications (e.g., new algorithms/datasets, integration with models);
  • Ecosystem assessment and monitoring;
  • Remotely sensed monitoring of key ecological environmental parameters;
  • Inversion of water cycle parameters; water resource assessment;
  • Land use/cover changes (LUCC) and ecosystem service value assessment;
  • Water resources and water quality management;
  • Human–nature relationship in watersheds.

Dr. Jingzhe Wang
Dr. Zhongwen Hu
Dr. Yangyi Wu
Dr. Jie Zhang
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. 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
  • Land use/cover changes (LUCC)
  • Natural resource monitoring and mapping
  • Watershed management
  • Environmental modeling
  • Machine learning
  • Spatiotemporal variations

Published Papers (15 papers)

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Editorial

Jump to: Research

6 pages, 226 KiB  
Editorial
Remote Sensing of Watershed: Towards a New Research Paradigm
by Jingzhe Wang, Yangyi Wu, Zhongwen Hu and Jie Zhang
Remote Sens. 2023, 15(10), 2569; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15102569 - 14 May 2023
Cited by 3 | Viewed by 1641
Abstract
Watersheds are critical natural systems that serve as the foundation for sustaining life on Earth [...] Full article
(This article belongs to the Special Issue Remote Sensing of Watershed)

Research

Jump to: Editorial

20 pages, 43689 KiB  
Article
Reconstruction of Sentinel Images for Suspended Particulate Matter Monitoring in Arid Regions
by Pan Duan, Fei Zhang, Chi-Yung Jim, Mou Leong Tan, Yunfei Cai, Jingchao Shi, Changjiang Liu, Weiwei Wang and Zheng Wang
Remote Sens. 2023, 15(4), 872; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15040872 - 04 Feb 2023
Cited by 2 | Viewed by 2074
Abstract
Missing data is a common issue in remote sensing. Data reconstruction through multiple satellite data sources has become one of the most powerful ways to solve this issue. Continuous monitoring of suspended particulate matter (SPM) in arid lakes is vital for water quality [...] Read more.
Missing data is a common issue in remote sensing. Data reconstruction through multiple satellite data sources has become one of the most powerful ways to solve this issue. Continuous monitoring of suspended particulate matter (SPM) in arid lakes is vital for water quality solutions. Therefore, this research aimed to develop and evaluate the performance of two image reconstruction strategies, spatio-temporal fusion reflectance image inversion SPM and SPM spatio-temporal fusion, based on the measured SPM concentration data with Sentinel-2 and Sentinel-3. The results show that (1) ESTARFM (Enhanced Spatio-temporal Adaptive Reflection Fusion Model) performed better than FSDAF (Flexible Spatio-temporal Data Fusion) in the fusion image generation, particularly the red band, followed by the blue, green, and NIR (near-infrared) bands. (2) A single-band linear and non-linear regression model was constructed based on Sentinel-2 and Sentinel-3. Analysis of the accuracy and stability of the model led us to the conclusion that the red band model performs well, is fast to model, and has a wide range of applications (Sentinel-2, Sentinel-3, and fused high-accuracy images). (3) By comparing the two data reconstruction strategies of spatio-temporal fused image inversion SPM and spatio-temporal fused SPM concentration map, we found that the fused SPM concentration map is more effective and more stable when applied to multiple fused images. The findings can provide an important scientific reference value for further expanding the inversion research of other water quality parameters in the future and provide a theoretical basis as well as technical support for the scientific management of Ebinur Lake’s ecology and environment. Full article
(This article belongs to the Special Issue Remote Sensing of Watershed)
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17 pages, 6246 KiB  
Article
Change of Human Footprint in China and Its Implications for Carbon Dioxide (CO2) Emissions
by Yuan Li, Wujuan Mi, Yuheng Zhang, Li Ji, Qiusheng He, Yuanzhu Wang and Yonghong Bi
Remote Sens. 2023, 15(2), 426; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15020426 - 10 Jan 2023
Cited by 1 | Viewed by 1863
Abstract
Humans have altered the earth in unprecedented ways, and these changes have profound implications for global climate change. However, the impacts of human pressures on carbon dioxide (CO2) emissions over long time scales have not yet been clarified. Here, we used [...] Read more.
Humans have altered the earth in unprecedented ways, and these changes have profound implications for global climate change. However, the impacts of human pressures on carbon dioxide (CO2) emissions over long time scales have not yet been clarified. Here, we used the human footprint index (HF), which estimates the ecological footprint of humans in a given location, to explore the impacts of human pressures on CO2 emissions in China from 2000 to 2017. Human pressures (+13.6%) and CO2 emissions (+198.3%) in China are still on the rise during 2000–2017 and are unevenly distributed spatially. There was a significant positive correlation between human pressures and CO2 emissions in China, and northern China is the main driver of this correlation. The increase of CO2 emissions in China slowed down after 2011. Although human pressures on the environment are increasing, high-quality development measures have already had noticeable effects on CO2 emission reductions. Full article
(This article belongs to the Special Issue Remote Sensing of Watershed)
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19 pages, 8719 KiB  
Article
Comparison of Five Models for Estimating the Water Retention Service of a Typical Alpine Wetland Region in the Qinghai–Tibetan Plateau
by Meiling Sun, Jian Hu, Xueling Chen, Yihe Lü and Lixue Yang
Remote Sens. 2022, 14(24), 6306; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14246306 - 13 Dec 2022
Cited by 5 | Viewed by 1508
Abstract
Model evaluation of water retention (WR) services has been commonly applied for national or global scientific assessment and decision making. However, evaluation results from different models are significantly uncertain, especially on a small regional scale. We compared the spatial–temporal variations and driving factors [...] Read more.
Model evaluation of water retention (WR) services has been commonly applied for national or global scientific assessment and decision making. However, evaluation results from different models are significantly uncertain, especially on a small regional scale. We compared the spatial–temporal variations and driving factors of the WR service by five models (i.e., the InVEST model (InVEST), precipitation storage model (PRS), water balance model I (WAB I), water balance model II (WAB II), and NPP-based surrogate model (NBS) based on partial correlation analysis and spatial statistics on the Ramsar international alpine wetland region of the Qinghai–Tibetan Plateau (QTP). The results showed that the wetland area continued to decrease, and built-up land increased from 2000 to 2015. The average WR volume ranged from 2.50 to 13.65 billion m3·yr−1, with the order from high to low being the PRS, WAB I, WAB II, and InVEST models, and the average total WR capacity was 2.21 × 109 by the NBS model. The WR service followed an increasing trend from north to south by the InVEST, PRS, WAB I, and WAB II models, while the NBS model presented a river network pattern of high values. The WR values were mainly reduced from 2000 to 2010 and increased from 2010 to 2015 in the PRS, WAB I, WAB II, and InVEST models, but the NBS model showed the opposite trend. Precipitation determined the spatial distribution of WR service in the InVEST, PRS, WAB I, and WAB II models. Still, the spatial variation was affected by climate factors, while the NPP data influenced the NBS model. In addition, the InVEST model in estimating WR values in wetlands and the PRS and WAB I models poorly estimate runoff, while the WAB II model might be the most accurate. These findings help clarify the applicability of the WR models in an alpine wetland region and provide a valuable background for improving the effectiveness of model evaluation. Full article
(This article belongs to the Special Issue Remote Sensing of Watershed)
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18 pages, 12601 KiB  
Article
An Integrated Method for River Water Level Recognition from Surveillance Images Using Convolution Neural Networks
by Chen Chen, Rufei Fu, Xiaojian Ai, Chengbin Huang, Li Cong, Xiaohuan Li, Jiange Jiang and Qingqi Pei
Remote Sens. 2022, 14(23), 6023; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14236023 - 28 Nov 2022
Cited by 11 | Viewed by 3063
Abstract
Water conservancy personnel usually need to know the water level by water gauge images in real-time and with an expected accuracy. However, accurately recognizing the water level from water gauge images is still a complex problem. This article proposes a composite method applied [...] Read more.
Water conservancy personnel usually need to know the water level by water gauge images in real-time and with an expected accuracy. However, accurately recognizing the water level from water gauge images is still a complex problem. This article proposes a composite method applied in the Wuyuan City, Jiangxi Province, in China. This method can detect water gauge areas and number areas from complex and changeable scenes, accurately detect the water level line from various water gauges, and finally, obtain the accurate water level value. Firstly, FCOS is improved by fusing a contextual adjustment module to meet the requirements of edge computing and ensure considerable detection accuracy. Secondly, to deal with scenes with indistinct water level features, we also apply the contextual adjustment module for Deeplabv3+ to segment the water gauge area above the water surface. Then, the area can be used to obtain the position of the water level line. Finally, the results of the previous two steps are combined to calculate the water level value. Detailed experiments prove that this method solves the problem of water level recognition in complex hydrological scenes. Furthermore, the recognition error of the water level by this method is less than 1 cm, proving it is capable of being applied in real river scenes. Full article
(This article belongs to the Special Issue Remote Sensing of Watershed)
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21 pages, 12549 KiB  
Article
The Effect of Drought on Vegetation Gross Primary Productivity under Different Vegetation Types across China from 2001 to 2020
by Xiaoping Wu, Rongrong Zhang, Virgílio A. Bento, Song Leng, Junyu Qi, Jingyu Zeng and Qianfeng Wang
Remote Sens. 2022, 14(18), 4658; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14184658 - 18 Sep 2022
Cited by 31 | Viewed by 3001
Abstract
Climate change has exacerbated the frequency and severity of droughts worldwide. Evaluating the response of gross primary productivity (GPP) to drought is thus beneficial to improving our understanding of the impact of drought on the carbon cycle balance. Although many studies have investigated [...] Read more.
Climate change has exacerbated the frequency and severity of droughts worldwide. Evaluating the response of gross primary productivity (GPP) to drought is thus beneficial to improving our understanding of the impact of drought on the carbon cycle balance. Although many studies have investigated the relationship between vegetation productivity and dry/wet conditions, the capability of different drought indices of assessing the influence of water deficit is not well understood. Moreover, few studies consider the effects of drought on vegetation with a focus on periods of drought. Here, we investigated the spatial-temporal patterns of GPP, the standardized precipitation evapotranspiration index (SPEI), and the vapor pressure deficit (VPD) in China from 2001 to 2020 and examined the relationship between GPP and water deficit/drought for different vegetation types. The results revealed that SPEI and GPP were positively correlated over approximately 70.7% of the total area, and VPD was negatively correlated with GPP over about 66.2% of the domain. Furthermore, vegetation productivity was more negatively affected by water deficit in summer and autumn. During periods of drought, the greatest negative impact was on deciduous forests and croplands, and woody savannas were the least impacted. This research provides a scientific reference for developing mitigation and adaptation measures to lessen the impact of drought disasters under a changing climate. Full article
(This article belongs to the Special Issue Remote Sensing of Watershed)
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17 pages, 3200 KiB  
Article
Spatiotemporal Variations of Dryland Vegetation Phenology Revealed by Satellite-Observed Fluorescence and Greenness across the North Australian Tropical Transect
by Song Leng, Alfredo Huete, Jamie Cleverly, Qiang Yu, Rongrong Zhang and Qianfeng Wang
Remote Sens. 2022, 14(13), 2985; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14132985 - 22 Jun 2022
Cited by 24 | Viewed by 1988
Abstract
Accurate characterization of spatial patterns and temporal variations in dryland vegetation is of great importance for improving our understanding of terrestrial ecosystem functioning under changing climates. Here, we explored the spatiotemporal variability of dryland vegetation phenology using satellite-observed Solar-Induced chlorophyll Fluorescence (SIF) and [...] Read more.
Accurate characterization of spatial patterns and temporal variations in dryland vegetation is of great importance for improving our understanding of terrestrial ecosystem functioning under changing climates. Here, we explored the spatiotemporal variability of dryland vegetation phenology using satellite-observed Solar-Induced chlorophyll Fluorescence (SIF) and the Enhanced Vegetation Index (EVI) along the North Australian Tropical Transect (NATT). Substantial impacts of extreme drought and intense wetness on the phenology and productivity of dryland vegetation are observed by both SIF and EVI, especially in the arid/semiarid interior of Australia without detectable seasonality in the dry year of 2018–2019. The greenness-based vegetation index (EVI) can more accurately capture the seasonal and interannual variation in vegetation production than SIF (EVI r2: 0.47~0.86, SIF r2: 0.47~0.78). However, during the brown-down periods, the rate of decline in EVI is evidently slower than that in SIF and in situ measurement of gross primary productivity (GPP), due partially to the advanced seasonality of absorbed photosynthetically active radiation. Over 70% of the variability of EVI (except for Hummock grasslands) and 40% of the variability of SIF (except for shrublands) can be explained by the water-related drivers (rainfall and soil moisture). By contrast, air temperature contributed to 25~40% of the variability of the effective fluorescence yield (SIFyield) across all biomes. In spite of high retrieval noises and variable accuracy in phenological metrics (MAE: 8~60 days), spaceborne SIF observations, offsetting the drawbacks of greenness-based phenology products with a potentially lagged end of the season, have the promising capability of mapping and characterizing the spatiotemporal dynamics of dryland vegetation phenology. Full article
(This article belongs to the Special Issue Remote Sensing of Watershed)
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18 pages, 5705 KiB  
Article
Assessing the Impact of Extreme Droughts on Dryland Vegetation by Multi-Satellite Solar-Induced Chlorophyll Fluorescence
by Song Leng, Alfredo Huete, Jamie Cleverly, Sicong Gao, Qiang Yu, Xianyong Meng, Junyu Qi, Rongrong Zhang and Qianfeng Wang
Remote Sens. 2022, 14(7), 1581; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14071581 - 25 Mar 2022
Cited by 25 | Viewed by 3943
Abstract
Satellite-estimated solar-induced chlorophyll fluorescence (SIF) is proven to be an effective indicator for dynamic drought monitoring, while the capability of SIF to assess the variability of dryland vegetation under water and heat stress remains challenging. This study presents an analysis of the responses [...] Read more.
Satellite-estimated solar-induced chlorophyll fluorescence (SIF) is proven to be an effective indicator for dynamic drought monitoring, while the capability of SIF to assess the variability of dryland vegetation under water and heat stress remains challenging. This study presents an analysis of the responses of dryland vegetation to the worst extreme drought over the past two decades in Australia, using multi-source spaceborne SIF derived from the Global Ozone Monitoring Experiment-2 (GOME-2) and TROPOspheric Monitoring Instrument (TROPOMI). Vegetation functioning was substantially constrained by this extreme event, especially in the interior of Australia, in which there was hardly seasonal growth detected by neither satellite-based observations nor tower-based flux measurements. At a 16-day interval, both SIF and enhanced vegetation index (EVI) can timely capture the reduction at the onset of drought over dryland ecosystems. The results demonstrate that satellite-observed SIF has the potential for characterizing and monitoring the spatiotemporal dynamics of drought over water-limited ecosystems, despite coarse spatial resolution coupled with high-retrieval noise as compared with EVI. Furthermore, our study highlights that SIF retrieved from TROPOMI featuring substantially enhanced spatiotemporal resolution has the promising capability for accurately tracking the drought-induced variation of heterogeneous dryland vegetation. Full article
(This article belongs to the Special Issue Remote Sensing of Watershed)
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22 pages, 39249 KiB  
Article
Field Model-Based Cultural Diffusion Patterns and GIS Spatial Analysis Study on the Spatial Diffusion Patterns of Qijia Culture in China
by Yuanyuan Wang, Naiang Wang, Xuepeng Zhao, Xueran Liang, Jiang Liu, Ping Yang, Yipeng Wang and Yixin Wang
Remote Sens. 2022, 14(6), 1422; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14061422 - 15 Mar 2022
Cited by 3 | Viewed by 3215
Abstract
Cultural diffusion is one of the core issues among researchers in the field of cultural geography. This study aimed to examine the spatial diffusion patterns of the Qijia culture (QJC) to clarify the origin and formation process of Chinese field model-based cultural diffusion [...] Read more.
Cultural diffusion is one of the core issues among researchers in the field of cultural geography. This study aimed to examine the spatial diffusion patterns of the Qijia culture (QJC) to clarify the origin and formation process of Chinese field model-based cultural diffusion patterns (FM-CDP) and geographic information system (GIS) spatial analysis methods. It used the point data of Qijia cultural sites without time information and combined them with the relevant records of Qijia cultural and historical documents, as well as archaeological excavation materials. Starting with the spatial location information of cultural distribution, it comprehensively analysed the cultural hearth, regions, diffusion patterns, and diffusion paths. The results indicated the following. (1) The QJC’s heart is in the southeast of Gansu Province, where the Shizhaocun and Xishanping sites are distributed. (2) Five different levels of cultural regions were formed, which demonstrated different diffusion patterns at different regional scales. On a large regional scale, many cultural regions belong to relocation diffusion patterns. Meanwhile, at the small regional scale (in the Gansu–Qinghai region), there are two patterns of diffusion: expansion diffusion and relocation diffusion; however, the expansion diffusion pattern is the main one. (3) Based on the relationship between the QJC, altitude, and the water system, the culture also has the characteristics of diffusion to low altitude areas and a pattern of diffusion along water systems. (4) There is a circular structure of the core, periphery, and fringe regions of the QJC. Finally, (5) the dry and cold climate around 4000a B.P., the cultural exchange between Europe and the Asian continent (the introduction of barley, wheat, livestock and sheep, and copper smelting technology), and the war in the late Neolithic period were important factors affecting the diffusion of the QJC. Full article
(This article belongs to the Special Issue Remote Sensing of Watershed)
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19 pages, 5426 KiB  
Article
Vegetation Browning Trends in Spring and Autumn over Xinjiang, China, during the Warming Hiatus
by Moyan Li, Junqiang Yao, Jingyun Guan and Jianghua Zheng
Remote Sens. 2022, 14(5), 1298; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14051298 - 07 Mar 2022
Cited by 8 | Viewed by 2678
Abstract
Satellite-derived vegetation records (GIMMS3g-NDVI) report that climate warming promotes vegetation greening trends; however, the climate impacts on vegetation growth during the global warming hiatus period (1998–2012) remain unclear. In this study, we focused on the vegetation change trend in Xinjiang in spring and [...] Read more.
Satellite-derived vegetation records (GIMMS3g-NDVI) report that climate warming promotes vegetation greening trends; however, the climate impacts on vegetation growth during the global warming hiatus period (1998–2012) remain unclear. In this study, we focused on the vegetation change trend in Xinjiang in spring and autumn before and during the recent warming hiatus period, and their climate-driving mechanisms, which have not been examined in previous studies. Based on satellite records, our results indicated that the summer normalized difference vegetation index (NDVI) in Xinjiang experienced a greening trend, while a browning trend existed in spring and autumn during this period. The autumn NDVI browning trend in Xinjiang was larger than that in spring; however, the spring NDVI displayed a higher correlation with climatic factors than did the autumn NDVI. During the warming hiatus, spring climatic factors were the main controlling factors of spring NDVI, and spring vapor pressure deficit (VPD) had the highest positive correlation with spring NDVI, followed by spring temperature. The larger increase in air temperature in spring than in autumn resulted in increased VPD differences in spring and autumn. In autumn, summer climatic factors (e.g., VPD, WS, RH, and precipitation) were significantly correlated with the autumn NDVI during the warming hiatus. However, the autumn temperature was weakly correlated with the autumn NDVI. Our results have significant implications for understanding the response of vegetation growth to recent and future climatic conditions. Full article
(This article belongs to the Special Issue Remote Sensing of Watershed)
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19 pages, 6019 KiB  
Article
Spatial-Temporal Variation in Paddy Evapotranspiration in Subtropical Climate Regions Based on the SEBAL Model: A Case Study of the Ganfu Plain Irrigation System, Southern China
by Guangfei Wei, Jingjing Cao, Hua Xie, Hengwang Xie, Yang Yang, Conglin Wu, Yuanlai Cui and Yufeng Luo
Remote Sens. 2022, 14(5), 1201; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14051201 - 28 Feb 2022
Cited by 8 | Viewed by 1840
Abstract
The surface energy balance algorithm for land (SEBAL) is a commonly used method for estimating evapotranspiration (ET) at a regional scale; however, the cloudy and rainy characteristics of subtropical monsoon regions pose a greater challenge for estimating paddy field ET based on remote [...] Read more.
The surface energy balance algorithm for land (SEBAL) is a commonly used method for estimating evapotranspiration (ET) at a regional scale; however, the cloudy and rainy characteristics of subtropical monsoon regions pose a greater challenge for estimating paddy field ET based on remote sensing technology. To this end, a typical subtropical climate region in southern China (Ganfu Plain irrigation system) was selected as the study area. Subsequently, we evaluated the applicability of the SEBAL model for estimating the ET of paddy fields at the daily scale; derived the interannual variation (2000–2017) characteristics of early, middle, and late rice ET; and finally analyzed the spatial distribution patterns of rice in different hydrological years. The results demonstrated that: (1) the SEBAL model estimated ET accurately on a daily scale, with R2, NSE, and RMSE values of 0.85, 0.81, and 0.84 mm/day, respectively; (2) the ET of paddy fields in the irrigated area was higher in July and August and the interannual trend of ET of early rice was not obvious, with a declining trend observed in middle rice and late rice from 2000 to 2009, which was followed by an increasing trend from 2009 to 2017; and (3) variations in the spatial distribution of ET were significant for early and late rice at different precipitation levels and less obvious for middle rice in wet years but significant in dry years. Overall, this study verified the applicability of the SEBAL model for estimating ET in paddy fields in subtropical regions and provided a basis and reference for the rational allocation of water resources at a regional scale. Full article
(This article belongs to the Special Issue Remote Sensing of Watershed)
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23 pages, 4264 KiB  
Article
Effects of Mulching on Maize Yield and Evapotranspiration in the Heihe River Basin, Northwest China
by Qianxi Shen, Jun Niu, Bellie Sivakumar and Na Lu
Remote Sens. 2022, 14(3), 700; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14030700 - 02 Feb 2022
Cited by 3 | Viewed by 1827
Abstract
Plastic film mulching is an effective way to manage agricultural fields in water shortage areas. Through increasing the soil surface temperature at the early stage of crop growth and reducing the soil evaporation during the whole growth period, plastic film mulching can realize [...] Read more.
Plastic film mulching is an effective way to manage agricultural fields in water shortage areas. Through increasing the soil surface temperature at the early stage of crop growth and reducing the soil evaporation during the whole growth period, plastic film mulching can realize the effect of water saving and yield increase. This study examined the effects of plastic film mulching on crop yield and evapotranspiration (ET) in the Heihe River basin in Northwest China. By using remote sensing data, the gridding G-AquaCrop model was built to simulate the maize yield and ET in the basin under conditions of film mulching and no-film mulching. Through an analysis of changes in maize yield and ET before and after film mulching, suitable areas for film mulching in the whole basin were identified. Through comparative analysis, it was found that after plastic film mulching, maize yield in 12–41% of the Heihe River basin increased to a certain extent, reaching 8%. Furthermore, film mulching decreased ET by 5–30% in 34–41% of areas planted with maize. Based on these results, suggestions were made on suitable areas for expansion of maize cultivation to balance the benefits of water saving and production increase with environmental pollution. Furthermore, the way of assessing the suitable mulching area is obtained by examining the meteorological condition directly. The results of this study are of great significance for rational allocation of agricultural production resources and efficient utilization of agricultural water resources. Full article
(This article belongs to the Special Issue Remote Sensing of Watershed)
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18 pages, 42045 KiB  
Article
Multiple Effects of Topographic Factors on Spatio-Temporal Variations of Vegetation Patterns in the Three Parallel Rivers Region, Southeast Qinghai-Tibet Plateau
by Chunya Wang, Jinniu Wang, Niyati Naudiyal, Ning Wu, Xia Cui, Yanqiang Wei and Qingtao Chen
Remote Sens. 2022, 14(1), 151; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14010151 - 30 Dec 2021
Cited by 18 | Viewed by 10729
Abstract
Topographic factors are critical for influencing vegetation distribution patterns, and studying the interactions between them can enhance our understanding of future vegetation dynamics. We used the Moderate-resolution Imaging Spectroradiometer Normalized Differential Vegetation Index (MODIS NDVI) image dataset (2000–2019), combined with the Digital Elevation [...] Read more.
Topographic factors are critical for influencing vegetation distribution patterns, and studying the interactions between them can enhance our understanding of future vegetation dynamics. We used the Moderate-resolution Imaging Spectroradiometer Normalized Differential Vegetation Index (MODIS NDVI) image dataset (2000–2019), combined with the Digital Elevation Model (DEM), and vegetation type data for trend analysis, and explored NDVI variation and its relationship with topographic factors through an integrated geographically-weighted model in the Three Parallel Rivers Region (TPRR) of southeastern Qinghai-Tibet Plateau (QTP) in the past 20 years. Our results indicated that there was no significant increase of NDVI in the entire basin between 2000–2019, except for the Lancang River basin. In the year 2004, abrupt changes in NDVI were observed across the entire basin and each sub-basin. During 2000–2019, the mean NDVI value of the whole basin increased initially and then decreased with the increasing elevation. However, it changed marginally with variations in slope and aspect. We observed a distinct spatial heterogeneity in vegetation patterns with elevation, with higher NDVI in the southern regions NDVI than those in the north as a whole. Most of the vegetation cover was concentrated in the slope range of 8~35°, with no significant difference in distribution except flat land. Furthermore, from 2000 to 2019, the vegetation cover in the TPRR showed an improving trend with the changes of various topographic factors, with the largest improvement area (36.10%) in the slightly improved category. The improved region was mainly distributed in the source area of the Jinsha River basin and the southern part of the whole basin. Geographically weighted regression (GWR) analysis showed that elevation was negatively correlated with NDVI trends in most areas, especially in the middle reaches of Nujiang River basin and Jinsha River basin, where the influence of slope and aspect on NDVI change was considerably much smaller than elevation. Our results confirmed the importance of topographic factors on vegetation growth processes and have implications for understanding the sustainable development of mountain ecosystems. Full article
(This article belongs to the Special Issue Remote Sensing of Watershed)
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17 pages, 4176 KiB  
Article
Analysis on the Spatio-Temporal Changes of LST and Its Influencing Factors Based on VIC Model in the Arid Region from 1960 to 2017: An Example of the Ebinur Lake Watershed, Xinjiang, China
by Nigenare Amantai and Jianli Ding
Remote Sens. 2021, 13(23), 4867; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13234867 - 30 Nov 2021
Cited by 9 | Viewed by 2123
Abstract
LST (Land surface temperature) is an important indicator for monitoring dynamic changes in the earth’s resources and environment. However, the complexity of obtaining long-term, continuous LST data hinders the development of research on LST responses to meteorological factors or LUCC in areas where [...] Read more.
LST (Land surface temperature) is an important indicator for monitoring dynamic changes in the earth’s resources and environment. However, the complexity of obtaining long-term, continuous LST data hinders the development of research on LST responses to meteorological factors or LUCC in areas where data is lacking. The objective of this research was to use the VIC-3L (Variable Infiltration Capacity) based on multi-source remote sensing data to simulate and explore spatio-temporal changes in the LST, to analyze the relationship between the LST and meteorological elements by using cross-wavelet transform (XWT) and wavelet coherence (WTC), the relationship between the LST and LUCC by using three-phase remote sensing images of LUCC. The following results were obtained. The annual average LST of the study area is increasing at a rate of 0.027 °C per year. The annual average LST level is relatively high in the central and eastern regions. The average temperature has an important influence on LST, which is mainly reflected in the period scale of 1~4a in 1963–1972, 1980–1996, and 2004–2010. The sharp decline in open shrubs may have exacerbated the increase in LST in the study area. This study provides a scientific reference for studying LST in arid areas. Full article
(This article belongs to the Special Issue Remote Sensing of Watershed)
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21 pages, 14500 KiB  
Article
Mapping Aquaculture Areas with Multi-Source Spectral and Texture Features: A Case Study in the Pearl River Basin (Guangdong), China
by Yue Xu, Zhongwen Hu, Yinghui Zhang, Jingzhe Wang, Yumeng Yin and Guofeng Wu
Remote Sens. 2021, 13(21), 4320; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13214320 - 27 Oct 2021
Cited by 19 | Viewed by 3242
Abstract
Aquaculture has grown rapidly in the field of food industry in recent years; however, it brought many environmental problems, such as water pollution and reclamations of lakes and coastal wetland areas. Thus, the evaluation and management of aquaculture industry are needed, in which [...] Read more.
Aquaculture has grown rapidly in the field of food industry in recent years; however, it brought many environmental problems, such as water pollution and reclamations of lakes and coastal wetland areas. Thus, the evaluation and management of aquaculture industry are needed, in which accurate aquaculture mapping is an essential prerequisite. Due to the difference between inland and marine aquaculture areas and the difficulty in processing large amounts of remote sensing images, the accurate mapping of different aquaculture types is still challenging. In this study, a novel approach based on multi-source spectral and texture features was proposed to map simultaneously inland and marine aquaculture areas. Time series optical Sentinel-2 images were first employed to derive spectral indices for obtaining texture features. The backscattering and texture features derived from the synthetic aperture radar (SAR) images of Sentinel-1A were then used to distinguish aquaculture areas from other geographical entities. Finally, a supervised Random Forest classifier was applied for large scale aquaculture area mapping. To address the low efficiency in processing large amounts of remote sensing images, the proposed approach was implemented on the Google Earth Engine (GEE) platform. A case study in the Pearl River Basin (Guangdong Province) of China showed that the proposed approach obtained aquaculture map with an overall accuracy of 89.5%, and the implementation of proposed approach on GEE platform greatly improved the efficiency for large scale aquaculture area mapping. The derived aquaculture map may support decision-making services for the sustainable development of aquaculture areas and ecological protection in the study area, and the proposed approach holds great potential for mapping aquacultures on both national and global scales. Full article
(This article belongs to the Special Issue Remote Sensing of Watershed)
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