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Remote Sensing
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Soil Erosion Estimation Based on Remote Sensing Data
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Soil Erosion Estimation Based on Remote Sensing Data

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

Deadline for manuscript submissions: 30 April 2024 | Viewed by 2154

Special Issue Editors

Dr. Xihua Yang

E-Mail Website
Guest Editor
Principal Research Scientist, New South Wales Department of Planning, Industry and Environment, University Technology Sydney, P.O. Box 624, Parramatta, NSW 2150, Australia
Interests: remote sensing and geospatial information system (GIS) applications in agriculture and soil erosion modeling
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Alfredo Huete

E-Mail Website
Guest Editor
School of Life Sciences, University of Technology Sydney, Ultimo, NSW 2007, Australia
Interests: biophysical remote sensing; terrestrial ecohydrology; land surface phenology; carbon and water fluxes; geostationary and low earth observations; time series analyses; climate change impacts; vegetation health and ecosystem resilience; ecological forecasting
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xiaoping Zhang

E-Mail Website
Guest Editor
Institute of Soil and Water Conservation, Northwest A&F University, Xianyang 712100, China
Interests: soil erosion and modelling; soil and water conservation and benefits; hydrological modelling; water and sediment balance; vegetation restoration and soil properties; remote-sensing-detected stratified vegetation coverage

Special Issue Information

Dear Colleagues,

Soil erosion (e.g., hillslope erosion, gully erosion, wind erosion) is a serious problem in many parts of the world, and it is likely to remain so into the foreseeable future. It negatively impacts soil quality, agricultural productivity, water quality and biodiversity. The assessment of soil erosion is useful in planning, conservation, climate adaptation and the development of optimum land management practices in order to reduce or mitigate erosion. Remote sensing data constitute important sources of information for mapping, monitoring, and predicting soil erosion, providing a cost-effective means of investigating soil erosion where there are not accessible territories or direct field methods are expensive.

This Special Issue aims to publish studies covering different uses of remote sensing data to extract useful information for the estimation of soil erosion including water and wind erosion. Multisource data integration studies (e.g., multispectral, thermal, geostationary, satellite rainfall and weather radar data), multiscale approaches, and discussions of a variety of other issues are welcome. We also welcome the submission of manuscripts that investigate the developments and applications of erosion models and algorithms for erosion factors (such as rainfall erosivity, slope-steepness, vegetation cover and management).

Articles may address, but are not limited to, the following topics:

  • Spatial monitoring of soil erosion at regional and global scales;
  • Geostationary satellites for assessing vegetation, wind erosion and dust storm dynamics;
  • Impact of soil erosion on agricultural productivity and economics;
  • Multisensor data fusion techniques for vegetation cover and erosion assessment;
  • Time-series rainfall erosivity and erosion modelling and the climate impacts;
  • Exploitation of remote sensing data for soil erosion estimation (such as LiDAR, Planet, Sentinel-3);
  • New algorithms for water and wind erosion estimation or existing erosion model applications;
  • Analysis of the soil erosion factors and the spatio-temporal variations;
  • Prediction of the rainfall erosivity and erosion risk under current and future climates;
  • Explore new technologies and data and the applications in soil erosion modelling;
  • Soil conservation scheme and anti-erosion measures.

Dr. Xihua Yang
Prof. Dr. Alfredo Huete
Prof. Dr. Xiaoping 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

  • soil erosion
  • remote sensing
  • spatial modelling
  • soil conservation

Published Papers (3 papers)

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Research

25 pages, 30507 KiB  
Article
Spatial Quantification of Cropland Soil Erosion Dynamics in the Yunnan Plateau Based on Sampling Survey and Multi-Source LUCC Data
by Guokun Chen, Jingjing Zhao, Xingwu Duan, Bohui Tang, Lijun Zuo, Xiao Wang and Qiankun Guo
Remote Sens. 2024, 16(6), 977; https://doi.org/10.3390/rs16060977 - 10 Mar 2024
Viewed by 617
Abstract
The mapping and dynamic monitoring of large-scale cropland erosion rates are critical for agricultural planning but extremely challenging. In this study, using field investigation data collected from 20,155 land parcels in 2817 sample units in the National Soil Erosion Survey, as well as [...] Read more.
The mapping and dynamic monitoring of large-scale cropland erosion rates are critical for agricultural planning but extremely challenging. In this study, using field investigation data collected from 20,155 land parcels in 2817 sample units in the National Soil Erosion Survey, as well as land use change data for two decades from the National Land Use/Cover Database of China (NLUD-C), we proposed a new point-to-surface approach to quantitatively assess long-term cropland erosion based on the CSLE model and non-homologous data voting. The results show that cropland in Yunnan suffers from serious problems, with an unsustainable mean soil erosion rate of 40.47 t/(ha·a) and an erosion ratio of 70.11%, which are significantly higher than those of other land types. Engineering control measures (ECMS) have a profound impact on reducing soil erosion; the soil erosion rates of cropland with and without ECMs differ more than five-fold. Over the past two decades, the cropland area in Yunnan has continued to decrease, with a net reduction of 7461.83 km2 and a ratio of −10.55%, causing a corresponding 0.32 × 108 t (12.12%) reduction in cropland soil loss. We also quantified the impact of different LUCC scenarios on cropland erosion, and extraordinarily high variability was found in soil loss in different basins and periods. Conversion from cropland to forest contributes the most to cropland erosion reduction, while conversion from grassland to cropland contributes 56.18% of the increase in soil erosion. Considering the current speed of cropland regulation, it is the sharp reduction in land area that leads to cropland erosion reduction rather than treatments. The choice between the Grain for Green Policy and Cropland Protecting Strategy in mountainous areas should be made carefully, with understanding and collaboration between different roles. Full article
(This article belongs to the Special Issue Soil Erosion Estimation Based on Remote Sensing Data)
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20 pages, 19569 KiB  
Article
Erosion Gully Networks Extraction Based on InSAR Refined Digital Elevation Model and Relative Elevation Algorithm—A Case Study in Huangfuchuan Basin, Northern Loess Plateau, China
by Pingda Lu, Bin Zhang, Chenfeng Wang, Mengyun Liu and Xiaoping Wang
Remote Sens. 2024, 16(5), 921; https://doi.org/10.3390/rs16050921 - 06 Mar 2024
Viewed by 545
Abstract
The time-effective mapping of erosion gullies is crucial for monitoring and early detection of developing erosional progression. However, current methods face challenges in obtaining large-scale erosion gully networks rapidly due to limitations in data availability and computational complexity. This study developed a rapid [...] Read more.
The time-effective mapping of erosion gullies is crucial for monitoring and early detection of developing erosional progression. However, current methods face challenges in obtaining large-scale erosion gully networks rapidly due to limitations in data availability and computational complexity. This study developed a rapid method for extracting erosion gully networks by integrating interferometric synthetic aperture radar (InSAR) and the relative elevation algorithm (REA) within the Huangfuchuan Basin, a case basin in the northern Loess Plateau, China. Validation in the study area demonstrated that the proposed method achieved an F1 score of 81.94%, representing a 9.77% improvement over that of the reference ASTER GDEM. The method successfully detected small reliefs of erosion gullies using the InSAR-refined DEM. The accuracy of extraction varied depending on the characteristics of the gullies in different locations. The F1 score showed a positive correlation with gully depth (R2 = 0.62), while the fragmented gully heads presented a higher potential of being missed due to the resolution effect. The extraction results provided insights into the erosion gully networks in the case study area. A total of approximately 28,000 gullies were identified, exhibiting pinnate and trellis patterns. Most of the gullies had notable intersecting angles exceeding 60°. The basin’s average depth was 64 m, with the deepest gully being 140 m deep. Surface fragmentation indicated moderate erosive activity, with the southeastern loess region showing more severe erosion than the Pisha sandstone-dominated central and northwestern regions. The method described in this study offers a rapid approach to map gullies, streamlining the workflow of erosion gully extraction and enabling efficiently targeted interventions for erosion control efforts. Its practical applicability and potential to leverage open-source data make it accessible for broader application in similar regions facing erosion challenges. Full article
(This article belongs to the Special Issue Soil Erosion Estimation Based on Remote Sensing Data)
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17 pages, 5691 KiB  
Article
Estimating the CSLE Biological Conservation Measures’ B-Factor Using Google Earth’s Engine
by Youfu Wu, Haijing Shi and Xihua Yang
Remote Sens. 2024, 16(5), 847; https://doi.org/10.3390/rs16050847 - 28 Feb 2024
Viewed by 460
Abstract
The biological conservation measures factor (B) in the Chinese Soil loss Equation (CSLE) model is one of the main components in evaluating soil erosion, and the accurate calculation of the B-factor at the regional scale is fundamental in predicting regional soil erosion and [...] Read more.
The biological conservation measures factor (B) in the Chinese Soil loss Equation (CSLE) model is one of the main components in evaluating soil erosion, and the accurate calculation of the B-factor at the regional scale is fundamental in predicting regional soil erosion and dynamic changes. In this study, we developed an optimal computational procedure for estimating and mapping the B-factor in the Google Earth Engine (GEE) cloud computing environment using multiple data sources through data suitability assessment and image fusion. Taking the Yanhe River Basin in the Loess Plateau of China as an example, we evaluated the availability of daily precipitation data (CHIRPS, ERA5, and PERSIANN-CDR data) against the data at national meteorological stations. We estimated the B-factor from Sentinel-2 data and proposed a new method, namely the trend migration method, to patch the missing values in Sentinel-2 data using three other remote sensing data (MOD09GA, Landsat 7, and Landsat 8). We then calculated and mapped the B-factor in the Yanhe River Basin based on rainfall erosivity, vegetation coverage, and land use types. The results show that the ERA5 precipitation dataset outperforms the CHIRPS and PERSIANN-CDR data in estimating rainfall and rainfall erosivity, and it can be utilized as an alternative data source for meteorological stations in soil erosion modeling. Compared to the harmonic analysis of time series (HANTS), the trend migration method proposed in this study is more suitable for patching the missing parts of Sentinel-2 data. The restored high-resolution Sentinel-2 data fit nicely with the 10 m resolution land use data, enhancing the B-factor calculation accuracy at local and region scales. The B-factor computation procedure developed in this study is applicable to various river basin and regional scales for soil erosion monitoring. Full article
(This article belongs to the Special Issue Soil Erosion Estimation Based on Remote Sensing Data)
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