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Bridging the Proximal and Remote Sensing Spectroscopy for Soil Properties Estimation and Monitoring

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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 (15 June 2022) | Viewed by 14993

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Special Issue Editors

Dr. Maria Knadel

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Guest Editor

Department of Agroecology, Aarhus University, Blichers Allé 20, 8830 Tjele, Denmark
Interests: vis–NIR spectroscopy; proximal soil sensing; on-the-go spectroscopy; soil characterization and mapping
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Prof. Dr. Sabine Chabrillat

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Guest Editor

1. GFZ German Research Center for Geosciences, Telegrafenberg, D-14473 Potsdam, Germany
2. Institute of Soil Science, Leibniz University Hannover, D-30419 Hannover, Germany
Interests: hyperspectral remote sensing; digital soil mapping; arid areas; land degradation
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Dr. Johanna Wetterlind

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Guest Editor

Department of Soil and Environment, Swedish University of Agricultural Sciences, Uppsala, Sweden
Interests: visNIR and MIR spectroscopy; proximal soil sensing; soil characterization and mapping; plant nutrition; organic matter characterization and turnover; precision agriculture; translating science into policy and practice

Dr. Asa Gholizadeh

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Co-Guest Editor

Faculty of Agrobiology, Food and Natural Resources, Department of Soil Science and Soil Protection, Czech University of Life Sciences Prague, 16500 Prague, Czech Republic
Interests: proximal and remote sensing
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Special Issue Information

Dear Colleagues,

The sustainable management of soil health and its state require constant assessment and monitoring of a high number of soil properties at different time frames and spatial scales, which presents a challenge when utilizing costly and time-consuming conventional analytical methods.

Reflectance spectroscopy has proven to be a reliable, cheap, and environmentally friendly technique for the estimation of basic and some functional soil properties. Its application extends from the laboratory benchtop and in situ portable or on-the-go sensors to the most recent remote (drone, aircraft and spaceborne) sensors, enabling a much bigger scale of investigation and potentially enabling a mapping of the spatial distribution of soil properties.

In this Special Issue, we would like to invite contributions reporting on the application of soil spectroscopy across visible near infrared; vis–NIR (400–2500 nm), mid-wave infrared; MWIR (3000–5000 nm) and long-wave Infrared; the LWIR (7000–12000 nm) spectral range; and focusing on:

Broadening the spectrum of proximal spectroscopy towards assessment, monitoring, and mapping of soil functional and advanced properties;
Presenting novel approaches to monitoring soil properties using remote sensing spectroscopy (also known as hyperspectral remote sensing–imaging spectroscopy);
Contributinf to the application of current and upcoming satellite hyperspectral missions for soil properties monitoring.

Moreover, considering the inevitable perspective of the fusion between proximal and remote soil spectroscopy, we would like to invite contributions bridging the two areas of research and the related challenges together. The relevant topics, among others, may include:

Spectral libraries (both laboratory and in situ), their standardization, and harmonization methods;
Calibration transfers between instruments and among different communities;
Examples of fusion between spectral libraries and remote spectroscopy for soil properties estimation;
Novel modeling techniques;
Multiscale and simulation approaches.

This includes research and applications within precision agriculture, pedology, soil health monitoring, soil management, and environmental protection.

Dr. Maria Knadel
Dr. Sabine Chabrillat
Dr. Johanna Wetterlind
Dr. Asa Gholizadeh
Guest Editors

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Keywords

Proximal soil spectroscopy
Remote sensing spectroscopy
Imaging spectroscopy
Vis-NIRS
MWIR
LWIR
Soil monitoring
Soil mapping

Published Papers (3 papers)

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Research

18 pages, 3861 KiB

Open AccessArticle

Estimation of Pb Content Using Reflectance Spectroscopy in Farmland Soil near Metal Mines, Central China

by Danyun Zhao, Danni Xie, Fang Yin, Lei Liu, Jilu Feng and Tariq Ashraf

Remote Sens. 2022, 14(10), 2420; https://doi.org/10.3390/rs14102420 - 18 May 2022

Cited by 9 | Viewed by 2599

Abstract

The contamination of farmlands with hazardous metals from mining puts the safety of agricultural commodities at risk. For remediation, it is crucial to map the spatial distribution of contaminated soil. Typical sampling-based procedures are time-consuming and labor-intensive. The use of visible, near-infrared, and short-wave infrared reflectance (VNIR-SWIR) spectroscopy to detect soil heavy metal pollution is an alternative. With the aim of investigating a methodology of detecting the most sensitive bands using VNIR-SWIR spectra to find lead (Pb) anomalies in agriculture soil near mining activities, the area in Xiaoqinling Mountain, downstream from a series of active gold mines, was selected to test the feasibility of utilizing VNIR-SWIR spectroscopy to map soil Pb. A total of 115 soil samples were collected for laboratory Pb analysis and spectral measurement. Partial least squares regression (PLSR) was adopted to estimate the soil Pb content by building the prediction model, and the model was optimized by finding the optimal number of bands involved. The spatial distribution of Pb concentration was mapped using the ordinary kriging (OK) interpolation method. This study found that five spectral bands (522 nm, 1668 nm, 2207 nm, 2296 nm, and 2345 nm) were sensitive to soil Pb content. The optimized prediction model’s coefficient of determination (R²), residual prediction deviation (RPD), and root mean square error (RMSE) were 0.711, 1.860, and 0.711 ln(mg/kg), respectively. Additionally, the result of OK interpolation was convincing and accurate (R² = 0.775, RMSE = 0.328 ln(mg/kg)), comparing maps from estimated and ground truth data. This study proves that it is feasible to use VNIR-SWIR spectral data for in situ estimation of the soil Pb content. Full article

(This article belongs to the Special Issue Bridging the Proximal and Remote Sensing Spectroscopy for Soil Properties Estimation and Monitoring)

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20 pages, 10616 KiB

Open AccessArticle

Soil Organic Matter Prediction Model with Satellite Hyperspectral Image Based on Optimized Denoising Method

by Xiangtian Meng, Yilin Bao, Qiang Ye, Huanjun Liu, Xinle Zhang, Haitao Tang and Xiaohan Zhang

Remote Sens. 2021, 13(12), 2273; https://doi.org/10.3390/rs13122273 - 10 Jun 2021

Cited by 50 | Viewed by 3599

Abstract

In order to improve the signal-to-noise ratio of the hyperspectral sensors and exploit the potential of satellite hyperspectral data for predicting soil properties, we took MingShui County as the study area, which the study area is approximately 1481 km², and we selected Gaofen-5 (GF-5) satellite hyperspectral image of the study area to explore an applicable and accurate denoising method that can effectively improve the prediction accuracy of soil organic matter (SOM) content. First, fractional-order derivative (FOD) processing is performed on the original reflectance (OR) to evaluate the optimal FOD. Second, singular value decomposition (SVD), Fourier transform (FT) and discrete wavelet transform (DWT) are used to denoise the OR and optimal FOD reflectance. Third, the spectral indexes of the reflectance under different denoising methods are extracted by optimal band combination algorithm, and the input variables of different denoising methods are selected by the recursive feature elimination (RFE) algorithm. Finally, the SOM content is predicted by a random forest prediction model. The results reveal that 0.6-order reflectance describes more useful details in satellite hyperspectral data. Five spectral indexes extracted from the reflectance under different denoising methods have a strong correlation with the SOM content, which is helpful for realizing high-accuracy SOM predictions. All three denoising methods can reduce the noise in hyperspectral data, and the accuracies of the different denoising methods are ranked DWT > FT > SVD, where 0.6-order-DWT has the highest accuracy (R² = 0.84, RMSE = 3.36 g kg⁻¹, and RPIQ = 1.71). This paper is relatively novel, in that GF-5 satellite hyperspectral data based on different denoising methods are used to predict SOM, and the results provide a highly robust and novel method for mapping the spatial distribution of SOM content at the regional scale. Full article

(This article belongs to the Special Issue Bridging the Proximal and Remote Sensing Spectroscopy for Soil Properties Estimation and Monitoring)

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30 pages, 11138 KiB

Open AccessArticle

Soil Color and Mineralogy Mapping Using Proximal and Remote Sensing in Midwest Brazil

by Raúl Roberto Poppiel, Marilusa Pinto Coelho Lacerda, Rodnei Rizzo, José Lucas Safanelli, Benito Roberto Bonfatti, Nélida Elizabet Quiñonez Silvero and José Alexandre Melo Demattê

Remote Sens. 2020, 12(7), 1197; https://doi.org/10.3390/rs12071197 - 8 Apr 2020

Cited by 26 | Viewed by 7342

Abstract

Soil color and mineralogy are used as diagnostic criteria to distinguish different soil types. In the literature, 350–2500 nm spectra were successfully used to predict soil color and mineralogy, but these attributes currently are not mapped for most Brazilian soils. In this paper, we provided the first large-extent maps with 30 m resolution of soil color and mineralogy at three depth intervals for 850,000 km² of Midwest Brazil. We obtained soil 350–2500 nm spectra from 1397 sites of the Brazilian Soil Spectral Library at 0–20 cm, 20–60, and 60–100 cm depths. Spectra was used to derive Munsell hue, value, and chroma, and also second derivative spectra of the Kubelka–Munk function, where key spectral bands were identified and their amplitude measured for mineral quantification. Landsat composites of topsoil and vegetation reflectance, together with relief and climate data, were used as covariates to predict Munsell color and Fe–Al oxides, and 1:1 and 2:1 clay minerals of topsoil and subsoil. We used random forest for soil modeling and 10-fold cross-validation. Soil spectra and remote sensing data accurately mapped color and mineralogy at topsoil and subsoil in Midwest Brazil. Hematite showed high prediction accuracy (R² > 0.71), followed by Munsell value and hue. Satellite topsoil reflectance at blue spectral region was the most relevant predictor (25% global importance) for soil color and mineralogy. Our maps were consistent with pedological expert knowledge, legacy soil observations, and legacy soil class map of the study region. Full article

(This article belongs to the Special Issue Bridging the Proximal and Remote Sensing Spectroscopy for Soil Properties Estimation and Monitoring)

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