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Dear Colleagues,

Many human interests, such as food production and water quality, are fundamentally connected to soils. However, soil properties are neither static nor homogeneous in space and time. Consequently, there is a strong demand for up-to-date soil information. Acquiring spatial soil variability is often time- and cost-intensive when performed analytically in the laboratory. This is especially true for large-scale applications with a high number of soil samples. By contrast, some applications, e.g., precision farming, require the identification of even short- or medium-term changes in the nutrient status of the soils. Therefore, remote sensing has become the most powerful tool for soil property assessment. Data can be collected from the ground, aerial platforms or satellites. UAVs offer the opportunity to acquire data with high spatial resolution and flexibility. From a methodical perspective, deep learning and artificial intelligence (AI) provide new and very promising conceptual approaches for the analysis of remote sensing data. In the context of soil property assessment from remote sensing data, more research is required to apply these modern methods to this relevant topic.

Thus, we would like to invite you to share your research and to participate in the submission of articles for this Special Issue with respect to the following topics, related to remote-sensing-based soil properties quantification:

Prediction of soil properties from different platforms;
Deep learning approaches for quantification of soil properties;
Quantification of soil properties for the assessment of soil condition status;
Scaling issues on RS data from lab/in situ to airborne/spaceborne;
Multisensoral approaches for quantification of soil properties.

Dr. Thomas Jarmer
Dr. Anna Brook
Guest Editors

Manuscript Submission Information

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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 properties
Quantification
Multisensoral analysis
Spatial assessment
Soil condition
Deep learning

Published Papers (2 papers)

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Research

19 pages, 5140 KiB

Open AccessArticle

Multispectral Models from Bare Soil Composites for Mapping Topsoil Properties over Europe

by José Lucas Safanelli, Sabine Chabrillat, Eyal Ben-Dor and José A. M. Demattê

Remote Sens. 2020, 12(9), 1369; https://doi.org/10.3390/rs12091369 - 26 Apr 2020

Cited by 49 | Viewed by 5481

Abstract

Reflectance of light across the visible, near-infrared and shortwave infrared (VIS-NIR-SWIR, 0.4–2.5 µm) spectral region is very useful for investigating mineralogical, physical and chemical properties of soils, which can reduce the need for traditional wet chemistry analyses. As many collections of multispectral satellite data are available for environmental studies, a large extent with medium resolution mapping could be benefited from the spectral measurements made from remote sensors. In this paper, we explored the use of bare soil composites generated from the large historical collections of Landsat images for mapping cropland topsoil attributes across the European extent. For this task, we used the Geospatial Soil Sensing System (GEOS3) for generating two bare soil composites of 30 m resolution (named synthetic soil images, SYSI), which were employed to represent the median topsoil reflectance of bare fields. The first (framed SYSI) was made with multitemporal images (2006–2012) framed to the survey time of the Land-Use/Land-Cover Area Frame Survey (LUCAS) soil dataset (2009), seeking to be more compatible to the soil condition upon the sampling campaign. The second (full SYSI) was generated from the full collection of Landsat images (1982–2018), which although displaced to the field survey, yields a higher proportion of bare areas for soil mapping. For evaluating the two SYSIs, we used the laboratory spectral data as a reference of topsoil reflectance to calculate the Spearman correlation coefficient. Furthermore, both SYSIs employed machine learning for calibrating prediction models of clay, sand, soil organic carbon (SOC), calcium carbonates (CaCO₃), cation exchange capacity (CEC), and pH determined in water, using the gradient boosting regression algorithm. The original LUCAS laboratory spectra and a version of the data resampled to the Landsat multispectral bands were also used as reference of prediction performance using VIS-NIR-SWIR multispectral data. Our results suggest that generating a bare soil composite displaced to the survey time of soil observations did not improve the quality of topsoil reflectance, and consequently, the prediction performance of soil attributes. Despite the lower spectral resolution and the variability of soils in Europe, a SYSI calculated from the full collection of Landsat images can be employed for topsoil prediction of clay and CaCO₃ contents with a moderate performance (testing R², root mean square error (RMSE) and ratio of performance to interquartile range (RPIQ) of 0.44, 9.59, 1.77, and 0.36, 13.99, 1.54, respectively). Thus, this study shows that although there exist some constraints due to the spatial and temporal variation of soil exposures and among the Landsat sensors, it is possible to use bare soil composites for mapping key soil attributes of croplands across the European extent. Full article

(This article belongs to the Special Issue Remote Sensing Based Quantification of Soil Properties)

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18 pages, 11416 KiB

Open AccessArticle

Multispectral Remote Sensing Data Are Effective and Robust in Mapping Regional Forest Soil Organic Carbon Stocks in a Northeast Forest Region in China

by Shuai Wang, Jinhu Gao, Qianlai Zhuang, Yuanyuan Lu, Hanlong Gu and Xinxin Jin

Remote Sens. 2020, 12(3), 393; https://doi.org/10.3390/rs12030393 - 26 Jan 2020

Cited by 15 | Viewed by 3170

Abstract

Accurately mapping the spatial distribution information of soil organic carbon (SOC) stocks is a key premise for soil resource management and environment protection. Rapid development of satellite remote sensing provides a great opportunity for monitoring SOC stocks at a large scale. In this study, based on 12 environmental variables of multispectral remote sensing, topography and climate and 236 soil sampling data, three different boosted regression tree (BRT) models were compared to obtain the most accurate map of SOC stocks covering the forest area of Lvshun District in the Northeast China. Four validation indexes, including mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R²), and Lin’s concordance correlation coefficient (LCCC) were calculated to evaluate the performance of the three models. The results showed that the full variable model performed the best, except the model using multispectral remote sensing variables. In the full variable model, the regional SOC stocks are primarily determined by multispectral remote sensing variables, followed by topographic and climatic variables, with the relative importance of variables in the model being 63%, 28%, and 9%, respectively. The average prediction results of full variables model and only multispectral remote sensing variables model were 8.99 and 9.32 kg m⁻², respectively. Our results indicated that there is a strong dependence of SOC stocks on multispectral remote sensing data when forest ecosystems have dense natural vegetation. Our study suggests that the multispectral remote sensing variables should be used to map SOC stocks of forest ecosystems in our study region. Full article

(This article belongs to the Special Issue Remote Sensing Based Quantification of Soil Properties)

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