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Land and Soil Health Assessment and Monitoring Based on Remote Sensing

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

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 11215

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Special Issue Editor

Dr. Tor-Gunnar Vågen

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Guest Editor

World Agroforestry (ICRAF), P.O. Box 30677, 00100 Nairobi, Kenya
Interests: spatial assessments of ecosystem health using remote sensing and spatial data analytics, including soil and land health mapping across landscapes

Special Issue Information

Dear Colleagues,

There is currently a whole range of open access remote sensing data available, including from NASA’s Landsat and MODIS platforms, as well as the European Space Agency’s (ESA) family of Sentinel satellites. This, together with an increasing suite of open source software and tools, has resulted in a veritable revolution in terms of the application of satellite data across a wide range of disciplines. Recent developments in the field of machine learning, including artificial intelligence (AI), are also transforming geospatial analytics in general and remote sensing in particular. One important application of remote sensing is in the assessment of land and soil health, particularly considering the increasing levels of environmental degradation globally and the many challenges facing agriculture and the global food system in general. Remote sensing has the potential to provide spatially explicit assessments of a range of different indicators used in assessing land and soil health, also reflecting the dynamics of these indicators over time. This Special Issue focuses on applications of remote sensing in assessing land and soil health across multiple spatial scales. We encourage submissions covering topics such as the following:

Use of remote sensing in mapping and monitoring of functional soil properties, such as soil organic carbon (SOC)
Remote sensing-based assessments of land degradation processes
Use of machine learning and artificial intelligence in the context of the remote sensing-based assessment of land health, including applications in biodiversity and conservation
Applications of multi-source remote sensing data fusion in assessing spatiotemporal dynamics of key land health indicators

Dr. Tor-Gunnar Vågen
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 health
Soil functional properties
Soil organic carbon (SOC)
Land degradation
Biodiversity
Machine learning
Artificial intelligence (AI)
Data fusion

Published Papers (3 papers)

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Research

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26 pages, 5942 KiB

Open AccessArticle

Integration of L-Band Derived Soil Roughness into a Bare Soil Moisture Retrieval Approach from C-Band SAR Data

by Mohamad Hamze, Nicolas Baghdadi, Marcel M. El Hajj, Mehrez Zribi, Hassan Bazzi, Bruno Cheviron and Ghaleb Faour

Remote Sens. 2021, 13(11), 2102; https://doi.org/10.3390/rs13112102 - 27 May 2021

Cited by 13 | Viewed by 3449

Abstract

Surface soil moisture (SSM) is a key variable for many environmental studies, including hydrology and agriculture. Synthetic aperture radar (SAR) data in the C-band are widely used nowadays to estimate SSM since the Sentinel-1 provides free-of-charge C-band SAR images at high spatial resolution with high revisit time, whereas the use of L-band is limited due to the low data availability. In this context, the main objective of this paper is to develop an operational approach for SSM estimation that mainly uses data in the C-band (Sentinel-1) with L-bands (ALOS/PALSAR) as additional data to improve SSM estimation accuracy. The approach is based on the use of the artificial neural networks (NNs) technique to firstly derive the soil roughness (Hrms) from the L-band (HH polarization) to then consider the L-band-derived Hrms and C-band SAR data (VV and VH polarizations) in the input vectors of NNs for SSM estimation. Thus, the Hrms estimated from the L-band at a given date is assumed to be constant for a given times series of C-band images. The NNs were trained and validated using synthetic and real databases. The results showed that the use of the L-band-derived Hrms in the input vector of NN in addition to C-band SAR data improved SSM estimation by decreasing the error (bias and RMSE), mainly for SSM values lower than 15 vol.% and regardless of Hrms values. Based on the synthetic database, the NNs that neglect the Hrms underestimate and overestimate the SSM (bias ranges between −8.0 and 4.0 vol.%) for Hrms values lower and higher than 1.5 cm, respectively. For Hrms <1.5 cm and most SSM values higher than 10 vol.%, the use of Hrms as an input in the NNs decreases the underestimation of the SSM (bias ranges from −4.5 to 0 vol.%) and provides a more accurate estimation of the SSM with a decrease in the RMSE by approximately 2 vol.%. Moreover, for Hrms values between 1.5 and 2.0 cm, the overestimation of SSM slightly decreases (bias decreased by around 1.0 vol.%) without a significant improvement of the RMSE. In addition, for Hrms >2.0 cm and SSM between 8 to 22 vol.%, the accuracy on the SSM estimation improved and the overestimation decreased by 2.2 vol.% (from 4.5 to 2.3 vol.%). From the real database, the use of Hrms estimated from the L-band brought a significant improvement of the SSM estimation accuracy. For in situ SSM less than 15 vol.%, the RMSE decreased by 1.5 and 2.2 vol.% and the bias by 1.2 and 2.6 vol.%, for Hrms values lower and higher than 1.5 cm, respectively. Full article

(This article belongs to the Special Issue Land and Soil Health Assessment and Monitoring Based on Remote Sensing)

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19 pages, 3646 KiB

Open AccessArticle

Retrieval and Mapping of Soil Organic Carbon Using Sentinel-2A Spectral Images from Bare Cropland in Autumn

by Ke Wang, Yanbing Qi, Wenjing Guo, Jielin Zhang and Qingrui Chang

Remote Sens. 2021, 13(6), 1072; https://doi.org/10.3390/rs13061072 - 11 Mar 2021

Cited by 22 | Viewed by 4037

Abstract

Soil is the largest carbon reservoir on the terrestrial surface. Soil organic carbon (SOC) not only regulates global climate change, but also indicates soil fertility level in croplands. SOC prediction based on remote sensing images has generated great interest in the research field of digital soil mapping. The short revisiting time and wide spectral bands available from Sentinel-2A (S2A) remote sensing data can provide a useful data resource for soil property prediction. However, dense soil surface coverage reduces the direct relationship between soil properties and S2A spectral reflectance such that it is difficult to achieve a successful SOC prediction model. Observations of bare cropland in autumn provide the possibility to establish accurate SOC retrieval models using the S2A super-spectral reflectance. Therefore, in this study, we collected 225 topsoil samples from bare cropland in autumn and measured the SOC content. We also obtained S2A spectral images of the western Guanzhong Plain, China. We established four SOC prediction models, including random forest (RF), support vector machine (SVM), partial least-squares regression (PLSR), and artificial neural network (ANN) based on 15 variables retrieved from the S2A images, and compared the prediction accuracy using RMSE (root mean square error), R² (coefficient of determination), and RPD (ratio of performance to deviation). Based on the optimal model, the spatial distribution of SOC was mapped and analyzed. The results indicated that the inversion model with the RF algorithm achieved the highest accuracy, with an R² of 0.8581, RPD of 2.1313, and RMSE of 1.07. The variables retrieved from the shortwave infrared (SWIR) bands (B11 and B12) usually had higher variable importance, except for the ANN model. SOC content mapped with the RF model gradually decreased with increasing distance from the Wei river, and values were higher in the west than in the east. These results matched the SOC distribution based on measurements at the sample sites. This research provides evidence that soil properties such as SOC can be retrieved and spatially mapped based on S2A images that are obtained from bare cropland in autumn. Full article

(This article belongs to the Special Issue Land and Soil Health Assessment and Monitoring Based on Remote Sensing)

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Other

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14 pages, 2455 KiB

Open AccessTechnical Note

Detection of Changes in Arable Chernozemic Soil Health Based on Landsat TM Archive Data

by Igor Savin, Elena Prudnikova, Yury Chendev, Anastasia Bek, Dmitry Kucher and Petr Dokukin

Remote Sens. 2021, 13(12), 2411; https://doi.org/10.3390/rs13122411 - 19 Jun 2021

Cited by 4 | Viewed by 2045

Abstract

When soils are used for a long period of time as arable land, their properties change. This can lead to soil degradation and loss of fertility, as well as other important soil biosphere functions. Obtaining data on the trends in arable soil conditions over large areas using traditional field survey methods is expensive and time-consuming. Currently, there are large archives of satellite data that can be used to monitor the status of arable soils. The analysis of changes in the color of the surface of arable chernozem soils of the Belgorod region, for the period from 1985 to the present, has been carried out based on the analysis of Landsat TM5 satellite data and information about the spectral reflectance of the soils of the region. It is found that, on most parts of arable lands of the region, the color of the soil surface has not changed significantly since 1985. Color changes were revealed on 11% of the analyzed area. The greatest changes are connected with the humus content and moisture content of soils. The three most probable reasons for the change of humus content in an arable horizon of soils are as follows: the dehumidification of soils during plowing; the reduction of the humus content due to water erosion; and the increase in humus content due to changes in the land-use system of the region in recent years. The change in soil moisture regime has mainly been found in arable lands in river valleys, most likely conditioned by the natural evolution of soils. Trends of increasing soil moisture are prevalent. The revealed regularities testify to the high stability of arable soils in the region during the last few decades. Full article

(This article belongs to the Special Issue Land and Soil Health Assessment and Monitoring Based on Remote Sensing)

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