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Water
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Remote Sensing Application on Soil Moisture
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Remote Sensing Application on Soil Moisture

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "New Sensors, New Technologies and Machine Learning in Water Sciences".

Deadline for manuscript submissions: closed (15 June 2022) | Viewed by 19208

Special Issue Editors

Prof. Dr. Kebiao Mao

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Guest Editor
Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Interests: artificial intelligence; deep learning; retrieval paradigm; soil moisture retrieval; land surface temperature retrieval; water vapor content retrieval; near surface temperature retrieval
Special Issues, Collections and Topics in MDPI journals
Dr. Chunxiang Shi

E-Mail
Guest Editor
National Meteorological Information Center, China Meteorological Administration (CMA), Beijing 100081, China
Interests: precipitation; data assimilation; parameters; land surface; sensible heat flux; soil moisture; SMOS; brightness temperature; TRMM; precipitation measurement; rain gages
Prof. Dr. Shibo Fang

E-Mail Website
Guest Editor
National Meteorological Information Center, China Meteorological Administration (CMA), Beijing 100081, China
Interests: global warming; drought; water logging; crop yield; NDVI; SAR; passive microwave
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Surface soil moisture is one of the key variables in the hydrological process that affects the exchange of water and energy fluxes at the surface–atmosphere interface. The accurate measurement of temporal and spatial changes in soil moisture is essential for a large number of environmental studies.

At present, the main method for measuring soil moisture is satellite remote sensing. Satellite sensors can observe a large area, but the spatial resolution depends on factors such as the microwave frequency, antenna size, and ground height. The spatial resolutions of most passive radiometers are currently within 10 kilometers, which is too rough for applications such as watershed hydrology. Many studies and applications require soil moisture data with higher spatial resolution. Reducing the data size also requires auxiliary data and model products.

The purpose of this Special Issue is to summarize the existing remote-sensing techniques for observing soil moisture and to propose more advanced and effective methods and products for verifying soil moisture.

We aim at the following aspects but not limited to:

  • Algorithm development for the estimation of soil moisture;
  • Soil moisture estimation;
  • The validation of soil moisture products;
  • The spatial downscaling of soil moisture;
  • Soil moisture product intercomparison and error quantification;
  • The applications of soil moisture;
  • Agriculture and drought monitoring;
  • Meteorological forecasting;
  • Model evaluation;
  • Land–atmosphere interaction;
  • Big data analytics in hydrology;
  • Evapotranspiration;
  • Hydrologic and crop modeling;
  • Hydrometeorology;
  • Irrigation;
  • Multi- to hyperspectral analysis.

Prof. Dr. Kebiao Mao
Dr. Chunxiang Shi
Prof. Dr. Shibo Fang
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. Water 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 2600 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
  • Soil moisture
  • Passive microwave
  • Synthetic aperture radar
  • GPS
  • Beidou
  • Time series analysis
  • Watershed catchment

Published Papers (6 papers)

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Research

15 pages, 1464 KiB  
Article
Artificial Neural Networks for the Prediction of the Reference Evapotranspiration of the Peloponnese Peninsula, Greece
by Stavroula Dimitriadou and Konstantinos G. Nikolakopoulos
Water 2022, 14(13), 2027; https://0-doi-org.brum.beds.ac.uk/10.3390/w14132027 - 24 Jun 2022
Cited by 25 | Viewed by 2687
Abstract
The aim of the study was to investigate the utility of artificial neural networks (ANNs) for the estimation of reference evapotranspiration (ETo) on the Peloponnese Peninsula in Greece for two representative months of wintertime and summertime during 2016–2019 and to test if using [...] Read more.
The aim of the study was to investigate the utility of artificial neural networks (ANNs) for the estimation of reference evapotranspiration (ETo) on the Peloponnese Peninsula in Greece for two representative months of wintertime and summertime during 2016–2019 and to test if using fewer inputs could lead to satisfactory predictions. Datasets from sixty-two meteorological stations were employed. The available inputs were mean temperature (Tmean), sunshine (N), solar radiation (Rs), net radiation (Rn), vapour pressure deficit (es-ea), wind speed (u2) and altitude (Z). Nineteen Multi-layer Perceptron (MLP) and Radial Basis Function (RBF) models were tested and compared against the corresponding FAO-56 Penman Monteith (FAO PM) estimates of a previous study, via statistical indices. The MLP1 7-2 model with all the variables as inputs outperformed the rest of the models (RMSE = 0.290 mm d−1, R2 = 98%). The results indicate that even ANNs with simple architecture can be very good predictive models of ETo for the Peloponnese, based on the literature standards. The MLP1 model determined Tmean, followed by u2, as the two most influential factors for ETo. Moreover, when one input was used (Tmean, Rn), RBFs slightly outperformed MLPs (RMSE < 0.385 mm d−1, R2 ≥ 96%), which means that even a sole-input ANN resulted in satisfactory predictions of ETo. Full article
(This article belongs to the Special Issue Remote Sensing Application on Soil Moisture)
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17 pages, 4257 KiB  
Article
Estimating the Soil Erosion Response to Land-Use Land-Cover Change Using GIS-Based RUSLE and Remote Sensing: A Case Study of Miyun Reservoir, North China
by Wenfeng Gong, Tiedong Liu, Xuanyu Duan, Yuxin Sun, Yangyang Zhang, Xinyu Tong and Zixuan Qiu
Water 2022, 14(5), 742; https://0-doi-org.brum.beds.ac.uk/10.3390/w14050742 - 25 Feb 2022
Cited by 18 | Viewed by 3875
Abstract
Soil erosion by water is a major cause of land degradation. Agricultural practices and many other ecological environmental problems contribute to land degradation worldwide, especially in arid and semi-arid areas. Miyun County, which is located in a mountainous region of North China, is [...] Read more.
Soil erosion by water is a major cause of land degradation. Agricultural practices and many other ecological environmental problems contribute to land degradation worldwide, especially in arid and semi-arid areas. Miyun County, which is located in a mountainous region of North China, is an important natural ecological zone and surface source of drinking water for Beijing and is very vulnerable to soil erosion due to its thin soil layer and human activities. Landsat images from 2003 and 2013 were used to analyze the land-use and land-cover change (LULCC) over this period. The revised universal soil loss equation (RUSLE) model integrated with Geographic Information System (GIS) was used to quantify soil loss and to map erosion risk. In addition, the response of soil erosion to LULCC was evaluated. The results showed that the areas under cropland, forest, and water bodies increased over the study period by 66.03, 243.44, and 9.01 km2, respectively. The increase in forested land indicated that the improved ground vegetation cover was due to the implementation of active ecological measures. Between 2003 and 2013, light soil erosion increased by 587.46 km2, and extremely severe soil erosion increased by 9.57 km2. The extents of slight, moderate, severe, and very severe soil erosion, however, decreased by 8.02, 445.21, 142.69, and 1.11 km2, respectively. A total of 57.5% of land with moderate soil erosion has been converted to light soil erosion, which could be highly beneficial for the improvement of vegetation control of soil and water losses. In terms of area, forestland exhibited the greatest increase, while moderate soil erosion exhibited the greatest decrease over the study period. Land-use change led to an alteration in the intensity of soil erosion due to changes or loss of vegetation. The conversion from high intensity soil erosion to low intensity was attributed to the implementation of ecological environmental protection. The results generated from this study may be useful for planners and land-use managers to make appropriate decisions for soil conservation. Full article
(This article belongs to the Special Issue Remote Sensing Application on Soil Moisture)
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18 pages, 11913 KiB  
Article
The Spatial-Temporal Characteristics of Soil Moisture and Its Persistence over Australia in the Last 20 Years
by Jiangtao Cai, Tiexi Chen, Qingyun Yan, Xin Chen and Renjie Guo
Water 2022, 14(4), 598; https://0-doi-org.brum.beds.ac.uk/10.3390/w14040598 - 16 Feb 2022
Cited by 2 | Viewed by 2635
Abstract
Persistence is an important feature of soil moisture, which affects many important processes such as land–air interaction and ecohydrological processes. Soil moisture datasets from reanalysis, remote-sensing observations and land surface models have been widely used in various ecohydrological studies, however, due to the [...] Read more.
Persistence is an important feature of soil moisture, which affects many important processes such as land–air interaction and ecohydrological processes. Soil moisture datasets from reanalysis, remote-sensing observations and land surface models have been widely used in various ecohydrological studies, however, due to the complexity of hydrological processes, the essential features of soil moisture such as spatial-temporal characteristics and persistence still need to be further quantified. This study focused on the Australia region and used in situ observation from fourteen International Soil Moisture Network sites to evaluate soil moisture from six gridded products, including satellite remote-sensing records (ESA CCI), output of reanalysis (ERA5-Land) and land surface models (GLDAS and GLEAM). High correlation coefficients between observations and the other soil moisture datasets were gotten. Regional averaged inter-annual variations of soil moisture were relatively large with some dry periods (2002–2010, 2013–2016) and wet periods (2011–2012) indicated by these gridded products. General coherent spatial patterns were found in long-term soil moisture with large differences in the lateral inflow area of the Great Artesian Basin. The coefficient of variation of these soil moisture datasets generally decreased from northwest to southeast, but the enhanced vegetation index coefficient of variation was larger in the southwest corner, northeast (non-coastal areas) and the lateral inflow area. Persistence calculated from various soil moisture datasets had quite large differences compared with measurements. Meanwhile, little coherence was gotten among different surface soil moisture datasets, the persistence of deep soil moisture seemed to be significantly overestimated. Therefore, models still need to improve the temporal characteristics with the persistence rather than the correlation coefficient. Full article
(This article belongs to the Special Issue Remote Sensing Application on Soil Moisture)
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21 pages, 6386 KiB  
Article
Spatiotemporal Change Analysis of Soil Moisture Based on Downscaling Technology in Africa
by Zijin Yuan, Nusseiba NourEldeen, Kebiao Mao, Zhihao Qin and Tongren Xu
Water 2022, 14(1), 74; https://0-doi-org.brum.beds.ac.uk/10.3390/w14010074 - 02 Jan 2022
Cited by 5 | Viewed by 3113
Abstract
Evaluating the long-term spatiotemporal variability in soil moisture (SM) over Africa is crucial for understanding how crop production is affected by drought or flooding. However, the lack of continuous and stable long-term series and high-resolution soil moisture records impedes such research. To overcome [...] Read more.
Evaluating the long-term spatiotemporal variability in soil moisture (SM) over Africa is crucial for understanding how crop production is affected by drought or flooding. However, the lack of continuous and stable long-term series and high-resolution soil moisture records impedes such research. To overcome the inconsistency of different microwave sensors (Advanced Microwave Scanning Radiometer-EOS, AMSR-E; Soil Moisture and Ocean Salinity, SMOS; and Advanced Microwave Scanning Radiometer 2, AMSR2) in measuring soil moisture over time and depth, we built a time series reconstruction model to correct SM, and then used a Spatially Weighted Downscaling Model to downscale the SM data from three different sensors to a 1 km spatial resolution. The verification of the reconstructed data shows that the product has high accuracy, and can be used for application and analysis. The spatiotemporal trends of SM in Africa were examined for 2003–2017. The analysis indicated that soil moisture is declining in Africa as a whole, and it is notably higher in central Africa than in other subregions. The most significant decrease in SM was observed in the savanna zone (slope < −0.08 m3 m−3 and P < 0.001), followed by South Africa and Namibia (slope < −0.07 m3 m−3 and P < 0.01). Seasonally, the most significant downward trends in SM were observed during the spring, mainly over eastern and central Africa (slope < −0.07 m3 m−3, R < −0.58 and P < 0.001). The analysis of spatiotemporal changes in soil moisture can help improve the understanding of hydrological cycles, and provide benchmark information for drought management in Africa. Full article
(This article belongs to the Special Issue Remote Sensing Application on Soil Moisture)
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28 pages, 9869 KiB  
Article
Sensitive Feature Evaluation for Soil Moisture Retrieval Based on Multi-Source Remote Sensing Data with Few In-Situ Measurements: A Case Study of the Continental U.S.
by Ling Zhang, Zixuan Zhang, Zhaohui Xue and Hao Li
Water 2021, 13(15), 2003; https://0-doi-org.brum.beds.ac.uk/10.3390/w13152003 - 21 Jul 2021
Cited by 5 | Viewed by 1766
Abstract
Soil moisture (SM) plays an important role for understanding Earth’s land and near-surface atmosphere interactions. Existing studies rarely considered using multi-source data and their sensitiveness to SM retrieval with few in-situ measurements. To solve this issue, we designed a SM retrieval method (Multi-MDA-RF) [...] Read more.
Soil moisture (SM) plays an important role for understanding Earth’s land and near-surface atmosphere interactions. Existing studies rarely considered using multi-source data and their sensitiveness to SM retrieval with few in-situ measurements. To solve this issue, we designed a SM retrieval method (Multi-MDA-RF) using random forest (RF) based on 29 features derived from passive microwave remote sensing data, optical remote sensing data, land surface models (LSMs), and other auxiliary data. To evaluate the importance of different features to SM retrieval, we first compared 10 filter or embedded type feature selection methods with sequential forward selection (SFS). Then, RF was employed to establish a nonlinear relationship between the in-situ SM measurements from sparse network stations and the optimal feature subset. The experiments were conducted in the continental U.S. (CONUS) using in-situ measurements during August 2015, with only 5225 training samples covering the selected feature subset. The experimental results show that mean decrease accuracy (MDA) is better than other feature selection methods, and Multi-MDA-RF outperforms the back-propagation neural network (BPNN) and generalized regression neural network (GRNN), with the R and unbiased root-mean-square error (ubRMSE) values being 0.93 and 0.032 cm3/cm3, respectively. In comparison with other SM products, Multi-MDA-RF is more accurate and can well capture the SM spatial dynamics. Full article
(This article belongs to the Special Issue Remote Sensing Application on Soil Moisture)
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20 pages, 3043 KiB  
Article
Simulations and Analysis of GNSS Multipath Observables for Frozen and Thawed Soil under Complex Surface Conditions
by Chao Gao, Weihua Bai, Zhiqiang Wang, Xuerui Wu, Lijun Liu, Nan Deng and Junming Xia
Water 2021, 13(14), 1986; https://0-doi-org.brum.beds.ac.uk/10.3390/w13141986 - 20 Jul 2021
Cited by 2 | Viewed by 1906
Abstract
The transition of the freeze–thaw state of the land surface soil occurs every year with the season and is closely related to the human living environment. The freezing and thawing changes of the ground surface have important effects on hydrological activities, meteorological conditions, [...] Read more.
The transition of the freeze–thaw state of the land surface soil occurs every year with the season and is closely related to the human living environment. The freezing and thawing changes of the ground surface have important effects on hydrological activities, meteorological conditions, and ecological gas dynamics. Traditional monitoring methods have their limitations. In the past two decades, the emerging GNSS-R/IR (Global Navigation Satellite System-Reflectometry/Interference Reflectometry) technology has provided a new method for monitoring the surface f state; however, fewer works have paid attention to the scattering mechanism models in the current study. In this paper, a forward GNSS multipath model suitable for a complex cold surface is developed. The dielectric constant model with different surface parameters is added. The calculation of snow layer attenuation is employed to take the snow cover into consideration. Based on the first-order radiation transfer equation model, a polarization synthesis method is used to obtain the circularly and linearly polarized vegetation specular scattering characteristics. The surface characteristics and antenna model are coupled. A more detailed forward GNSS multipath model of frozen and thawed soil under complex surface conditions is established. The model is used to simulate and analyze the forward GNSS multipath (Signal to Noise Ratio (SNR), phase and pseudorange) responses of frozen and thawed soil under complex surface conditions (soil salinity, snow and vegetation coverage). Studies have shown that when the soil changes from freezing to thawing due to the change in the phase of the water in the soil, the dielectric constant and BRCS (bi-static radar cross-section) increase, causing the increase in the amplitude of the multipath observation. The higher the salinity content, the larger the amplitude of the multipath observation. The attenuation of the snow cover and the vegetation layer will lead to the reduction of the multipath observation amplitude. For the first time, the model developed by this paper reveals the GNSS multipath observation response of frozen and thawed soil under complex surface conditions in detail, which can provide some theoretical support for subsequent experimental design and data analysis. Full article
(This article belongs to the Special Issue Remote Sensing Application on Soil Moisture)
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