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Topsoil Characterization by Means of 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 (31 March 2023) | Viewed by 18760

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Special Issue Editors

Dr. Ruxandra Vintila

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Guest Editor

1. National Research and Development Institute for Soil Science, Agrochemistry and Environment (ICPA), Department of Soil and Environment Informatics, Bucharest, Romania
2. Academy of Agricultural and Forestry Sciences (ASAS), Bucharest, Romania
Interests: earth observation applications; precision agriculture; soil ecosystem services; socioecological resilience; spatial data organization and processing

Dr. Frank Veroustrate

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Guest Editor

REDSTAR CM&V, Antwerpen, Belgium
Interests: terrestrial remote sensing; systems analysis; modelling; data assimilation
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Dear Colleagues,

Soil resources of the Earth are vital for preserving life on this planet due to their unique ecosystem services. Etymologically, humus, human, and humble share, for a good reason, the same root. These resources are, however, limited and non-renewable at a reasonable time scale. In addition, soils are now threatened, as evidenced in the fact that in recent years, the Technosphere (i.e., all material production generated by human activities) has begun to exceed the Biosphere at an accelerated rate, both in weight and diversity.

Despite the demand and research efforts to address these problems innovatively, there are currently no onboard sensors dedicated to soils. Operational sensors can now provide valuable and sometimes irreplaceable information about the properties and the state of the uppermost layer of the soil, which is called “topsoil”. This layer, ranging from 5 to 30 cm, is usually the first affected by threats such as organic matter decline, erosion, compaction, salinization, contamination, sealing, landslides, or land subsidence. Additionally, climate change can have serious effects on the water and energy budgets of the topsoil affecting the Earth Critical Zone. Numerous publications have already shown that remote sensing technologies can contribute to estimate, map, and monitor topsoil properties and its state.

This Special Issue invites you to highlight significant achievements so far, as well as the challenges and limits of current remote sensing technologies to provide useful information on topsoil. We also invite you to show new opportunities offered by the present constellation of satellites, along with data processing advancements that address research gaps you have identified and/or support various policies.

Dr. Ruxandra Vintila
Dr. Frank Veroustrate
Guest Editors

Manuscript Submission Information

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Keywords

Property estimation, mapping, monitoring
Main topsoil physicochemical properties
Surface soil moisture
Land surface temperature; evapotranspiration
Ground surface temperature; periglacial landforms; permafrost landforms
Desertification; desertification-prone areas
Land degradation; land degradation-prone areas
Landslide susceptibility; landslide hazard; landslide-prone areas
Land subsidence; land subsidence-prone areas
Harmonized methodologies to support long-term integrated environmental policies

Published Papers (7 papers)

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Research

15 pages, 6446 KiB

Open AccessArticle

The Feasibility of Remotely Sensed Near-Infrared Reflectance for Soil Moisture Estimation for Agricultural Water Management

by Ebrahim Babaeian and Markus Tuller

Remote Sens. 2023, 15(11), 2736; https://doi.org/10.3390/rs15112736 - 24 May 2023

Cited by 1 | Viewed by 1973

Abstract

In-depth knowledge about soil moisture dynamics is crucial for irrigation management in precision agriculture. This study evaluates the feasibility of high spatial resolution near-infrared remote sensing with unmanned aerial systems for soil moisture estimation to provide decision support for precision irrigation management. A [...] Read more.

N T R

) and the normalized difference vegetation index (

N D V I

) is introduced and used for estimation and mapping of root zone soil moisture and plant extractable water. The performance of the proposed approach was evaluated via comparison with ground soil moisture measurements with advanced time domain reflectometry sensors. We found the estimates based on the

N T R - N D V I

trapezoid model to be highly correlated with the ground soil moisture measurements. We believe that the presented approach shows great potential for farm-scale precision irrigation management but acknowledge that more research for different cropping systems, soil textures, and climatic conditions is needed to make the presented approach viable for the application by crop producers. Full article

(This article belongs to the Special Issue Topsoil Characterization by Means of Remote Sensing)

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22 pages, 4169 KiB

Open AccessArticle

Multi-Horizon Predictive Soil Mapping of Historical Soil Properties Using Remote Sensing Imagery

by Preston T. Sorenson, Jeremy Kiss, Angela K. Bedard-Haughn and Steve Shirtliffe

Remote Sens. 2022, 14(22), 5803; https://doi.org/10.3390/rs14225803 - 17 Nov 2022

Cited by 3 | Viewed by 1894

Abstract

There is increasing demand for more detailed soil maps to support fine-scale land use planning, soil carbon management, and precision agriculture in Saskatchewan. Predictive soil mapping that incorporates a combination of environmental covariates provides a cost-effective tool for generating finer resolution soil maps. This study focused on mapping soil properties for multiple soil horizons in Saskatchewan using historical legacy soil data in combination with remote sensing band indices, bare soil composite imagery, climate data, and terrain attributes. Mapped soil properties included soil organic carbon content (SOC), total nitrogen, cation exchange capacity (CEC), electrical conductivity (EC), inorganic carbon (IOC), sand and clay content, and total profile soil organic carbon stocks. For each of these soil properties, a recursive feature elimination was undertaken to reduce the number of features in the overall model. This process involved iteratively removing features such that random forest out-of-bag error was minimized. Final random forest models were built for each property and evaluated using an independent test dataset. Overall, predictive models were successful for SOC (R² = 0.71), total nitrogen (R² = 0.65), CEC (R² = 0.46), sand content (R² = 0.44) and clay content (R² = 0.55). The methods used in this study enable mapping of a greater geographic region of Saskatchewan compared to those previously established that relied solely on bare soil composite imagery. Full article

(This article belongs to the Special Issue Topsoil Characterization by Means of Remote Sensing)

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8 pages, 889 KiB

Open AccessCommunication

An Extensive Field-Scale Dataset of Topsoil Organic Carbon Content Aimed to Assess Remote Sensed Datasets and Data-Derived Products from Modeling Approaches

by Ahmed Laamrani, Paul R. Voroney, Daniel D. Saurette, Aaron A. Berg, Line Blackburn, Adam W. Gillespie and Ralph C. Martin

Remote Sens. 2022, 14(21), 5519; https://doi.org/10.3390/rs14215519 - 02 Nov 2022

Cited by 3 | Viewed by 1635

Abstract

The geosciences suffer from a lack of large georeferenced datasets that can be used to assess and monitor the role of soil organic carbon (SOC) in plant growth, soil fertility, and CO₂ sequestration. Publicly available, large field-scale georeferenced datasets are often limited in number and design to serve these purposes. This study provides the first publicly accessible dataset of georeferenced topsoil SOC measurements (n = 840) over a 26-hectare (ha) agricultural field located in southern Ontario, Canada, with a sampling density of ~32 points per ha. As SOC is usually influenced by site topography (i.e., slope and landscape position), each point of the database is associated with a wide range of remote sensing topographic derivatives; as well as with normalized difference vegetation index (NDVI) based value. The NDVI data were extracted from remote sensing Sentinel-2 imagery from over a five-year period (2017–2021). In this paper, the methodology for topsoil sampling, SOC measurement in the lab, as well as producing the suite of topographic derivatives is described. We discuss the opportunities that the database offers in terms of spatially explicit and continuous soil information to support international efforts in digital soil mapping (i.e., SoilGrids250m) as well as other potential applications detailed in the discussion section. We believe that the database with very dense point location measurements can help in conducting carbon stocks and sequestration studies. Such information can be used to help bridge the gap between ground data and remotely sensed datasets or data-derived products from modeling approaches intended to evaluate field-scale rates of agricultural carbon accumulation. The generated topsoil database in this study is archived and publicly available on the Zenodo open-access repository. Full article

(This article belongs to the Special Issue Topsoil Characterization by Means of Remote Sensing)

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22 pages, 5618 KiB

Open AccessArticle

Geostatistical Modelling of Soil Spatial Variability by Fusing Drone-Based Multispectral Data, Ground-Based Hyperspectral and Sample Data with Change of Support

by Antonella Belmonte, Carmela Riefolo, Francesco Lovergine and Annamaria Castrignanò

Remote Sens. 2022, 14(21), 5442; https://doi.org/10.3390/rs14215442 - 29 Oct 2022

Cited by 2 | Viewed by 1631

Abstract

Traditional soil characterization methods are time consuming, laborious and invasive and do not allow for long-term repeatability of measurements. The overall aim of this paper was to assess and model spatial variability of the soil in an olive grove in south Italy by using data from two sensors of different types: a multi-spectral on-board drone radiometer and a hyperspectral visible-near infrared-shortwave infrared (VIS-NIR-SWIR) reflectance radiometer, as well as sample data, to arrive at a delineation of homogeneous areas. The hyperspectral data were processed using Continuum Removal (CR) methodology to obtain information about the content and composition of clay. Differently, the multispectral data were firstly upscaled to the support of soil data using geostatistics and taking into account the change of support. Secondly, the data acquired with the two different sensors were integrated with soil granulometric properties by using two multivariate geostatistical techniques: multi-collocated cokriging to achieve a more exhaustive and finer-scale soil characterization, and multi-collocated factor cokriging to extract synthetic scale-dependent indices (regionalized factors) for the delineation of soil in homogeneous zones. This paper shows the impact of change of support on the uncertainty of soil prediction that can have a significant effect on decision making in Precision Agriculture. Moreover, four regionalized factors at two different scales (two for each scale) were retained and mapped. Each factor provided a different delineation of the field with areas characterized by different granulometries and clay compositions. The applied method is sufficiently flexible and could be applied to any number and type of sensors. Full article

(This article belongs to the Special Issue Topsoil Characterization by Means of Remote Sensing)

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14 pages, 2929 KiB

Open AccessArticle

Estimating Soil Organic Matter Content in Desert Areas Using In Situ Hyperspectral Data and Feature Variable Selection Algorithms in Southern Xinjiang, China

by Peimin Yang, Jie Hu, Bifeng Hu, Defang Luo and Jie Peng

Remote Sens. 2022, 14(20), 5221; https://doi.org/10.3390/rs14205221 - 18 Oct 2022

Cited by 18 | Viewed by 2523

Abstract

Soil organic matter (SOM) is a key factor for evaluating soil fertility. Rapidly monitoring organic matter content in desert soil can provide a scientific basis for the rational development and utilization of reserve arable land resources. Although spectral inversion accuracy for SOM under laboratory-controlled conditions is high, it is time-consuming and costly compared to the in situ spectroscopic determination method. However, in situ spectroscopy causes losses in accuracy due to interference from external environmental factors (e.g., the surface roughness of soil, changes in weather conditions, atmospheric water vapor, etc.). Therefore, reducing or removing the interference of external environmental factors to improve the accuracy of in situ spectroscopy for estimating SOM is challenging. In this study, visible and near-infrared (Vis-NIR) in situ spectral data were collected from 135 topsoil (0–20 cm) samples in a desert area of northwestern China, and organic matter content was measured. Three spectral pre-processing methods—the standard normal transform (SNV), reciprocal logarithm (log(1/R)) and normalization (NOR)—combined with three feature variable selection methods—the particle swarm algorithm (PSO), ant colony algorithm (ACO) and simulated annealing (SA) algorithm—were used to filter the spectral feature bands of SOM, and then partial least squares regression (PLSR), a back propagation neural network (BPNN) and a convolutional neural network (CNN) were used to construct the estimation models of SOM. The results indicated that the SNV could enhance the spectral information related to SOM and improve the accuracy of model estimation, and it was one of the most effective spectral pretreatment methods. Compared with the model constructed with the full-band spectroscopy method, the feature variable selection method could effectively improve the estimation accuracy of the Vis-NIR in situ spectroscopy model. The most obvious improvement was found with PSO, where R² and RPD were improved by more than 0.34 and 0.16, respectively, and RMSE was reduced by more than 0.29 g kg⁻¹. The accuracy of the CNN model was higher than that of the BPNN and PLSR models, both for the inversion model of SOM built from full-band spectral data and the bands selected by the characteristic variable selection method. SNV-PSO-CNN is the optimal hybrid model for in situ spectral measurement of SOM (R² = 0.71, RPD = 1.88, RMSE = 1.67 g kg⁻¹) and can realize the quantitative in situ spectral inversion of SOM in desert soils. Full article

(This article belongs to the Special Issue Topsoil Characterization by Means of Remote Sensing)

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16 pages, 7490 KiB

Open AccessArticle

Airborne Radiometric Surveys and Machine Learning Algorithms for Revealing Soil Texture

by Andrea Maino, Matteo Alberi, Emiliano Anceschi, Enrico Chiarelli, Luca Cicala, Tommaso Colonna, Mario De Cesare, Enrico Guastaldi, Nicola Lopane, Fabio Mantovani, Maurizio Marcialis, Nicola Martini, Michele Montuschi, Silvia Piccioli, Kassandra Giulia Cristina Raptis, Antonio Russo, Filippo Semenza and Virginia Strati

Remote Sens. 2022, 14(15), 3814; https://doi.org/10.3390/rs14153814 - 08 Aug 2022

Cited by 8 | Viewed by 2289

Abstract

Soil texture is key information in agriculture for improving soil knowledge and crop performance, so the accurate mapping of this crucial feature is imperative for rationally planning cultivations and for targeting interventions. We studied the relationship between radioelements and soil texture in the Mezzano Lowland (Italy), a 189 km² agricultural plain investigated through a dedicated airborne gamma-ray spectroscopy survey. The K and Th abundances were used to retrieve the clay and sand content by means of a multi-approach method. Linear (simple and multiple) and non-linear (machine learning algorithms with deep neural networks) predictive models were trained and tested adopting a 1:50,000 scale soil texture map. The comparison of these approaches highlighted that the non-linear model introduces significant improvements in the prediction of soil texture fractions. The predicted maps of the clay and of the sand content were compared with the regional soil maps. Although the macro-structures were equally present, the airborne gamma-ray data permits us shedding light on finer features. Map areas with higher clay content were coincident with paleo-channels crossing the Mezzano Lowland in Etruscan and Roman periods, confirmed by the hydrographic setting of historical maps and by the geo-morphological features of the study area. Full article

(This article belongs to the Special Issue Topsoil Characterization by Means of Remote Sensing)

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32 pages, 13227 KiB

Open AccessFeature PaperArticle

Drivers of Organic Carbon Stocks in Different LULC History and along Soil Depth for a 30 Years Image Time Series

by Mahboobeh Tayebi, Jorge Tadeu Fim Rosas, Wanderson de Sousa Mendes, Raul Roberto Poppiel, Yaser Ostovari, Luis Fernando Chimelo Ruiz, Natasha Valadares dos Santos, Carlos Eduardo Pellegrino Cerri, Sérgio Henrique Godinho Silva, Nilton Curi, Nélida Elizabet Quiñonez Silvero and José A. M. Demattê

Remote Sens. 2021, 13(11), 2223; https://doi.org/10.3390/rs13112223 - 07 Jun 2021

Cited by 24 | Viewed by 4420

Abstract

Soil organic carbon (SOC) stocks are a remarkable property for soil and environmental monitoring. The understanding of their dynamics in crop soils must go forward. The objective of this study was to determine the impact of temporal environmental controlling factors obtained by satellite images over the SOC stocks along soil depth, using machine learning algorithms. The work was carried out in São Paulo state (Brazil) in an area of 2577 km². We obtained a dataset of boreholes with soil analyses from topsoil to subsoil (0–100 cm). Additionally, remote sensing covariates (30 years of land use history, vegetation indexes), soil properties (i.e., clay, sand, mineralogy), soil types (classification), geology, climate and relief information were used. All covariates were confronted with SOC stocks contents, to identify their impact. Afterwards, the abilities of the predictive models were tested by splitting soil samples into two random groups (70 for training and 30% for model testing). We observed that the mean values of SOC stocks decreased by increasing the depth in all land use and land cover (LULC) historical classes. The results indicated that the random forest with recursive features elimination (RFE) was an accurate technique for predicting SOC stocks and finding controlling factors. We also found that the soil properties (especially clay and CEC), terrain attributes, geology, bioclimatic parameters and land use history were the most critical factors in controlling the SOC stocks in all LULC history and soil depths. We concluded that random forest coupled with RFE could be a functional approach to detect, map and monitor SOC stocks using environmental and remote sensing data. Full article

(This article belongs to the Special Issue Topsoil Characterization by Means of Remote Sensing)

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