Remote Sensing

Research

34 pages, 12304 KiB

Open AccessArticle

Updating of the Archival Large-Scale Soil Map Based on the Multitemporal Spectral Characteristics of the Bare Soil Surface Landsat Scenes

by Dmitry I. Rukhovich, Polina V. Koroleva, Alexey D. Rukhovich and Mikhail A. Komissarov

Remote Sens. 2023, 15(18), 4491; https://doi.org/10.3390/rs15184491 - 12 Sep 2023

Cited by 4 | Viewed by 1330

Abstract

For most of the arable land in Russia (132–137 million ha), the dominant and accurate soil information is stored in the form of map archives on paper without coordinate reference. The last traditional soil map(s) (TSM, TSMs) were created over 30 years ago. [...] Read more.

For most of the arable land in Russia (132–137 million ha), the dominant and accurate soil information is stored in the form of map archives on paper without coordinate reference. The last traditional soil map(s) (TSM, TSMs) were created over 30 years ago. Traditional and/or archival soil map(s) (ASM, ASMs) are outdated in terms of storage formats, dates, and methods of production. The technology of constructing a multitemporal soil line (MSL) makes it possible to update ASMs and TSMs based on the processing of big remote-sensing data (RSD). To construct an MSL, the spectral characteristics of the bare soil surface (BSS) are used. The BSS on RSD is distinguished within the framework of the conceptual apparatus of the spectral neighborhood of the soil line. The filtering of big RSD is based on deep machine learning. In the course of the work, a vector georeferenced version of the ASM and an updated soil map were created based on the coefficient “C” of the MSL. The maps were verified based on field surveys (76 soil pits). The updated map is called the map of soil interpretation of the coefficient “C” (SIC “C”). The SIC “C” map has a more detailed legend compared to the ASM (7 sections/chapters instead of 5), greater accuracy (smaller errors of the first and second kind), and potential suitability for calculating soil organic matter/carbon (SOM/SOC) reserves (soil types/areals in the SIC “C” map are statistically significant are divided according to the thickness of the organomineral horizon and the content of SOM in the plowed layer). When updating, a systematic underestimation of the numbers of contours and areas of soils with manifestations of negative/degradation soil processes (slitization and erosion) on the TSM was established. In the process of updating, all three shortcomings of the ASMs/TSMs (archaic storage, dates, and methods of creation) were eliminated. The SIC “C” map is digital (thematic raster), modern, and created based on big data processing methods. For the first time, the actualization of the soil map was carried out based on the MSL characteristics (coefficient “C”). Full article

(This article belongs to the Special Issue Remote Sensing for Soil Mapping and Monitoring)

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17 pages, 10813 KiB

Open AccessArticle

A Framework for Retrieving Soil Organic Matter by Coupling Multi-Temporal Remote Sensing Images and Variable Selection in the Sanjiang Plain, China

by Haiyi Ma, Changkun Wang, Jie Liu, Xinyi Wang, Fangfang Zhang, Ziran Yuan, Chengshuo Yao and Xianzhang Pan

Remote Sens. 2023, 15(12), 3191; https://doi.org/10.3390/rs15123191 - 20 Jun 2023

Cited by 5 | Viewed by 1817

Abstract

Soil organic matter (SOM) is an important soil property for agricultural production. Rising grain demand has increased the intensity of cultivated land development in the Sanjiang Plain of China, and there is a strong demand for SOM monitoring in this region. Therefore, Baoqing [...] Read more.

Soil organic matter (SOM) is an important soil property for agricultural production. Rising grain demand has increased the intensity of cultivated land development in the Sanjiang Plain of China, and there is a strong demand for SOM monitoring in this region. Therefore, Baoqing County of the Sanjiang Plain, an important grain production area, was considered the study area. In the study, we proposed a framework for high-accuracy SOM retrieval by coupling multi-temporal remote sensing (RS) images and variable selection algorithms. A total of 73 surface soil samples (0–20 cm) were collected in 2010, and Landsat 5 images acquired during the bare soil period (April, May, and June) were selected from 2008 to 2011. Three variable selection algorithms, namely, Genetic Algorithm, Random Frog and Competitive Adaptive Reweighted Sampling (CARS), were combined with Partial Least Squares Regression (PLSR) to build SOM retrieval models on the spectral bands and indices of the images. The results using a single-date image showed that the combination of variable selection algorithms and PLSR outperformed using PLSR alone, and CARS showed the best performance (R² = 0.34, RMSE = 15.66 g/kg) among all the algorithms. Therefore, only CARS was applied to SOM retrieval in the different year interval groups. To investigate the effect of the image acquisition time, all images were divided into various year interval groups, and the resulting images were then stacked. The results using multi-temporal images showed that the SOM retrieval accuracy improved as the year interval lengthened. The optimal result (R² = 0.59, RMSE = 11.81 g/kg) was obtained from the 2008–2011 group, wherein the difference indices derived from the images of 2009, 2010, and 2011 dominated the selected spectral variables. Moreover, the spatial prediction of SOM based on the optimal model was consistent with the distribution of SOM. Our study suggested that the proposed framework that couples stacked multi-temporal RS images with variable selection algorithms has potential for SOM retrieval. Full article

(This article belongs to the Special Issue Remote Sensing for Soil Mapping and Monitoring)

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18 pages, 3150 KiB

Open AccessArticle

Organic Matter Retrieval in Black Soil Based on Oblique Extremum Signatures

by Mingyue Zhang, Maozhi Wang, Daming Wang, Shangkun Wang and Wenxi Xu

Remote Sens. 2023, 15(10), 2508; https://doi.org/10.3390/rs15102508 - 10 May 2023

Viewed by 1460

Abstract

How to extract the indicative signatures from the spectral data is an important issue for further retrieval based on remote sensing technique. This study provides new insight into extracting indicative signatures by identifying oblique extremum points, rather than local extremum points traditionally known [...] Read more.

How to extract the indicative signatures from the spectral data is an important issue for further retrieval based on remote sensing technique. This study provides new insight into extracting indicative signatures by identifying oblique extremum points, rather than local extremum points traditionally known as absorption points. A case study on retrieving soil organic matter (SOM) contents from the black soil region in Northeast China using spectral data revealed that the oblique extremum method can effectively identify weak absorption signatures hidden in the spectral data. Moreover, the comparison of retrieval outcomes using various indicative signature extraction methods reveals that the oblique extremum method outperforms the correlation analysis and traditional extremum methods. The experimental findings demonstrate that the radial basis function (RBF) neural network retrieval model exposes the nonlinear relationship between reflectance (or reflectance transformation results) and the SOM contents. Additionally, an improved oblique extremum method based on the second-order derivative is provided. Overall, this research presents a novel perspective on indicative signature extraction, which could potentially offer better retrieval performance than traditional methods. Full article

(This article belongs to the Special Issue Remote Sensing for Soil Mapping and Monitoring)

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24 pages, 2999 KiB

Open AccessArticle

Sentinel-2 and Sentinel-1 Bare Soil Temporal Mosaics of 6-Year Periods for Soil Organic Carbon Content Mapping in Central France

by Diego Urbina-Salazar, Emmanuelle Vaudour, Anne C. Richer-de-Forges, Songchao Chen, Guillaume Martelet, Nicolas Baghdadi and Dominique Arrouays

Remote Sens. 2023, 15(9), 2410; https://doi.org/10.3390/rs15092410 - 4 May 2023

Cited by 15 | Viewed by 4889

Abstract

Satellite-based soil organic carbon content (SOC) mapping over wide regions is generally hampered by the low soil sampling density and the diversity of soil sampling periods. Some unfavorable topsoil conditions, such as high moisture, rugosity, the presence of crop residues, the limited amplitude [...] Read more.

Satellite-based soil organic carbon content (SOC) mapping over wide regions is generally hampered by the low soil sampling density and the diversity of soil sampling periods. Some unfavorable topsoil conditions, such as high moisture, rugosity, the presence of crop residues, the limited amplitude of SOC values and the limited area of bare soil when a single image is used, are also among the influencing factors. To generate a reliable SOC map, this study addresses the use of Sentinel-2 (S2) temporal mosaics of bare soil (S2Bsoil) over 6 years jointly with soil moisture products (SMPs) derived from Sentinel 1 and 2 images, SOC measurement data and other environmental covariates derived from digital elevation models, lithology maps and airborne gamma-ray data. In this study, we explore (i) the dates and periods that are preferable to construct temporal mosaics of bare soils while accounting for soil moisture and soil management; (ii) which set of covariates is more relevant to explain the SOC variability. From four sets of covariates, the best contributing set was selected, and the median SOC content along with uncertainty at 90% prediction intervals were mapped at a 25-m resolution from quantile regression forest models. The accuracy of predictions was assessed by 10-fold cross-validation, repeated five times. The models using all the covariates had the best model performance. Airborne gamma-ray thorium, slope and S2 bands (e.g., bands 6, 7, 8, 8a) and indices (e.g., calcareous sedimentary rocks, “calcl”) from the “late winter–spring” time series were the most important covariates in this model. Our results also indicated the important role of neighboring topographic distances and oblique geographic coordinates between remote sensing data and parent material. These data contributed not only to optimizing SOC mapping performance but also provided information related to long-range gradients of SOC spatial variability, which makes sense from a pedological point of view. Full article

(This article belongs to the Special Issue Remote Sensing for Soil Mapping and Monitoring)

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19 pages, 5538 KiB

Open AccessArticle

On-Site Soil Monitoring Using Photonics-Based Sensors and Historical Soil Spectral Libraries

by Konstantinos Karyotis, Nikolaos L. Tsakiridis, Nikolaos Tziolas, Nikiforos Samarinas, Eleni Kalopesa, Periklis Chatzimisios and George Zalidis

Remote Sens. 2023, 15(6), 1624; https://doi.org/10.3390/rs15061624 - 17 Mar 2023

Cited by 6 | Viewed by 3204

Abstract

In-situ infrared soil spectroscopy is prone to the effects of ambient factors, such as moisture, shadows, or roughness, resulting in measurements of compromised quality, which is amplified when multiple sensors are used for data collection. Aiming to provide accurate estimations of common physicochemical [...] Read more.

In-situ infrared soil spectroscopy is prone to the effects of ambient factors, such as moisture, shadows, or roughness, resulting in measurements of compromised quality, which is amplified when multiple sensors are used for data collection. Aiming to provide accurate estimations of common physicochemical soil properties, such as soil organic carbon (SOC), texture, pH, and calcium carbonates based on in-situ reflectance captured by a set of low-cost spectrometers operating at the shortwave infrared region, we developed an AI-based spectral transfer function that maps fields to laboratory spectra. Three test sites in Cyprus, Lithuania, and Greece were used to evaluate the proposed methodology, while the dataset was harmonized and augmented by GEO-Cradle regional soil spectral library (SSL). The developed dataset was used to calibrate and validate machine learning models, with the attained predictive performance shown to be promising for directly estimating soil properties in-situ, even with sensors with reduced spectral range. Aiming to set a baseline scenario, we completed the exact same modeling experiment under laboratory conditions and performed a one-to-one comparison between field and laboratory modelling accuracy metrics. SOC and pH presented an R² of 0.43 and 0.32 when modeling the in-situ data compared to 0.63 and 0.41 of the laboratory case, respectively, while clay demonstrated the highest accuracy with an R² value of 0.87 in-situ and 0.90 in the laboratory. Calcium carbonates were also attempted to be modeled at the studied spectral region, with the expected accuracy loss from the laboratory to the in-situ to be observable (R² = 0.89 for the laboratory and 0.67 for the in-situ) but the reduced dataset variability combined with the calcium carbonate characteristics that are spectrally active in the region outside the spectral range of the used in-situ sensor, induced low RPIQ values (less than 0.50), signifying the importance of the suitable sensor selection. Full article

(This article belongs to the Special Issue Remote Sensing for Soil Mapping and Monitoring)

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16 pages, 4289 KiB

Open AccessArticle

Exploring the Potential of vis-NIR Spectroscopy as a Covariate in Soil Organic Matter Mapping

by Meihua Yang, Songchao Chen, Xi Guo, Zhou Shi and Xiaomin Zhao

Remote Sens. 2023, 15(6), 1617; https://doi.org/10.3390/rs15061617 - 16 Mar 2023

Cited by 4 | Viewed by 1925

Abstract

Robust soil organic matter (SOM) mapping is required by farms, but their generation requires a large number of samples to be chemically analyzed, which is cost prohibitive. Recently, research has shown that visible and near-infrared (vis-NIR) reflectance spectroscopy is a fast and accurate [...] Read more.

Robust soil organic matter (SOM) mapping is required by farms, but their generation requires a large number of samples to be chemically analyzed, which is cost prohibitive. Recently, research has shown that visible and near-infrared (vis-NIR) reflectance spectroscopy is a fast and accurate technique for estimating SOM in a cost-effective manner. However, few studies have focused on using vis-NIR spectroscopy as a covariate to improve the accuracy of spatial modeling. In this study, our objective was to compare the mapping accuracy from a spatial model using kriging methods with and without the covariate of vis-NIR spectroscopy. We split the 261 samples into a calibration set (104) for building the spectral predictive model, a test set for generating the vis-NIR augmented set from the prediction of the fitted spectral predictive model (131), and a validation set (26) for evaluating map accuracy. We used two datasets (235 samples) for Kriging: a laboratory-based dataset (Ld, observations from calibration and test datasets) and a laboratory-based dataset with vis-NIR augmented predictions (Au.p, observations from calibration and predictions from test dataset), a laboratory-based dataset with vis-NIR spectra as the covariance (Ld.co) and augmented dataset with predictions using vis-NIR with vis-NIR spectra for the covariance (Au.p.co). The first one to seven accumulated principal components of vis-NIR spectra were used as the covariates when we used the measurement of Ld.co and Au.p.co. The map accuracy was evaluated by the validation set for the four datasets using Kriging. The results indicated that adding vis-NIR spectra as covariates had great potential in improving the map accuracy using kriging, and much higher accuracies were observed for Ld.p.co (RMSE of 5.51 g kg⁻¹) and Au.p.co (RMSE of 5.66 g kg⁻¹) than without using vis-NIR spectra as covariates for Ld (RMSE of 7.12 g kg⁻¹) and Au.p (RMSE of 7.69 g kg⁻¹). With a similar model performance to Ld.p.co, Au.p.co can reduce the cost of laboratory analysis for 60% of soil samples, demonstrating its advantage in cost-efficiency for spatial modeling of soil information. Therefore, we conclude that vis-NIR spectra can be used as a cost-effective technique to obtain augmented data to improve fine-resolution spatial mapping of soil information. Full article

(This article belongs to the Special Issue Remote Sensing for Soil Mapping and Monitoring)

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20 pages, 3917 KiB

Open AccessArticle

Increasing Accuracy of the Soil-Agricultural Map by Sentinel-2 Images Analysis—Case Study of Maize Cultivation under Drought Conditions

by Anna Jędrejek, Jan Jadczyszyn and Rafał Pudełko

Remote Sens. 2023, 15(5), 1281; https://doi.org/10.3390/rs15051281 - 25 Feb 2023

Cited by 4 | Viewed by 2865

Abstract

The properties of soil constitute one of the most important features of the environment that determine the potential for food production in a given region. Knowledge of the soil texture and agroclimate allows for the proper selection of species and agrotechnics in plant [...] Read more.

The properties of soil constitute one of the most important features of the environment that determine the potential for food production in a given region. Knowledge of the soil texture and agroclimate allows for the proper selection of species and agrotechnics in plant production. However, in contrast to the agroclimate, the soil may show a large spatial variation of physical and chemical characteristics within the plot. In regions where the soil diversity is so high that the available soil maps are not sufficient, the only method that allows for precise mapping of the soil mosaic is remote sensing. This paper presents the concepts of using Sentinel-2 multispectral satellite images to detail the available soil-agriculture map at a scale of 1:25,000. In the presented work, the following research hypothesis has been formulated: spatial and temporal analysis of high-resolution satellite images can be used to improve the quality of a large-scale archival soil-agriculture map. It is possible due to the spatial differentiation of the spectral reflection from the field (canopy), which is influenced by soil conditions—especially the differentiation of physical properties (granulometric composition) in soil profiles which determine the possibility of water retention during drought conditions. The research carried out as a case study of maize remote sensing confirmed the hypothesis. It was based on the selection of the most appropriate term (maize development period: BBCH 79, 6-decade drought index: CBW = −206 mm) and the vegetation index (NDVI). This made it possible to make the scale of the map 10 times more detailed. The obtained results are the first step in developing a general model (based on remote sensing) for detailing the soil-agriculture map for Poland, which will significantly improve the accuracy of the drought monitoring system developed by the Institute of Soil Science and Plant Cultivation (Poland). Full article

(This article belongs to the Special Issue Remote Sensing for Soil Mapping and Monitoring)

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25 pages, 15396 KiB

Open AccessArticle

Transferability of Covariates to Predict Soil Organic Carbon in Cropland Soils

by Tom Broeg, Michael Blaschek, Steffen Seitz, Ruhollah Taghizadeh-Mehrjardi, Simone Zepp and Thomas Scholten

Remote Sens. 2023, 15(4), 876; https://doi.org/10.3390/rs15040876 - 4 Feb 2023

Cited by 18 | Viewed by 3965

Abstract

Precise knowledge about the soil organic carbon (SOC) content in cropland soils is one requirement to design and execute effective climate and food policies. In digital soil mapping (DSM), machine learning algorithms are used to predict soil properties from covariates derived from traditional [...] Read more.

Precise knowledge about the soil organic carbon (SOC) content in cropland soils is one requirement to design and execute effective climate and food policies. In digital soil mapping (DSM), machine learning algorithms are used to predict soil properties from covariates derived from traditional soil mapping, digital elevation models, land use, and Earth observation (EO). However, such DSM models are trained for a specific dataset and region and have so far only allowed limited general statements to be made that would enable the models to be transferred to different regions. In this study, we test the transferability of SOC models for cropland soils using five different covariate groups: multispectral soil reflectance composites (satellite), soil legacy data (soil), digital elevation model derivatives (terrain), climate parameters (climate), and combined models (combined). The transferability was analyzed using data from two federal states in southern Germany: Bavaria and Baden-Wuerttemberg. First, baseline models were trained for each state with combined models performing best in both cases (R² = 0.68/0.48). Next, the models were transferred and tested with soil samples from the other state whose data were not used during model calibration. Only satellite and combined models were transferable, but accuracy declined in both cases. In the final step, models were trained with samples from both states (mixed-data models) and applied to each state separately. This process significantly improved the accuracies of satellite, terrain, and combined models, while it showed no effect on climate models and decreased the models based on soil covariates. The experiment underlines the importance of EO for the transfer and extrapolation of DSM models. Full article

(This article belongs to the Special Issue Remote Sensing for Soil Mapping and Monitoring)

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18 pages, 4730 KiB

Open AccessEditor’s ChoiceArticle

A CNN-LSTM Model for Soil Organic Carbon Content Prediction with Long Time Series of MODIS-Based Phenological Variables

by Lei Zhang, Yanyan Cai, Haili Huang, Anqi Li, Lin Yang and Chenghu Zhou

Remote Sens. 2022, 14(18), 4441; https://doi.org/10.3390/rs14184441 - 6 Sep 2022

Cited by 48 | Viewed by 7041

Abstract

The spatial distribution of soil organic carbon (SOC) serves as critical geographic information for assessing ecosystem services, climate change mitigation, and optimal agriculture management. Digital mapping of SOC is challenging due to the complex relationships between the soil and its environment. Except for [...] Read more.

The spatial distribution of soil organic carbon (SOC) serves as critical geographic information for assessing ecosystem services, climate change mitigation, and optimal agriculture management. Digital mapping of SOC is challenging due to the complex relationships between the soil and its environment. Except for the well-known terrain and climate environmental covariates, vegetation that interacts with soils influences SOC significantly over long periods. Although several remote-sensing-based vegetation indices have been widely adopted in digital soil mapping, variables indicating long term vegetation growth have been less used. Vegetation phenology, an indicator of vegetation growth characteristics, can be used as a potential time series environmental covariate for SOC prediction. A CNN-LSTM model was developed for SOC prediction with inputs of static and dynamic environmental variables in Xuancheng City, China. The spatially contextual features in static variables (e.g., topographic variables) were extracted by the convolutional neural network (CNN), while the temporal features in dynamic variables (e.g., vegetation phenology over a long period of time) were extracted by a long short-term memory (LSTM) network. The ten-year phenological variables derived from moderate-resolution imaging spectroradiometer (MODIS) observations were adopted as predictors with historical temporal changes in vegetation in addition to the commonly used static variables. The random forest (RF) model was used as a reference model for comparison. Our results indicate that adding phenological variables can produce a more accurate map, as tested by the five-fold cross-validation, and demonstrate that CNN-LSTM is a potentially effective model for predicting SOC at a regional spatial scale with long-term historical vegetation phenology information as an extra input. We highlight the great potential of hybrid deep learning models, which can simultaneously extract spatial and temporal features from different types of environmental variables, for future applications in digital soil mapping. Full article

(This article belongs to the Special Issue Remote Sensing for Soil Mapping and Monitoring)

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18 pages, 2765 KiB

Open AccessArticle

Digital Mapping of Soil Organic Carbon with Machine Learning in Dryland of Northeast and North Plain China

by Xianglin Zhang, Jie Xue, Songchao Chen, Nan Wang, Zhou Shi, Yuanfang Huang and Zhiqing Zhuo

Remote Sens. 2022, 14(10), 2504; https://doi.org/10.3390/rs14102504 - 23 May 2022

Cited by 20 | Viewed by 4014

Abstract

Due to the importance of soil organic carbon (SOC) in supporting ecosystem services, accurate SOC assessment is vital for scientific research and decision making. However, most previous studies focused on single soil depth, leading to a poor understanding of SOC in multiple depths. [...] Read more.

Due to the importance of soil organic carbon (SOC) in supporting ecosystem services, accurate SOC assessment is vital for scientific research and decision making. However, most previous studies focused on single soil depth, leading to a poor understanding of SOC in multiple depths. To better understand the spatial distribution pattern of SOC in Northeast and North China Plain, we compared three machine learning algorithms (i.e., Cubist, Extreme Gradient Boosting (XGBoost) and Random Forest (RF)) within the digital soil mapping framework. A total of 386 sampling sites (1584 samples) following specific criteria covering all dryland districts and counties and soil types in four depths (i.e., 0–10, 10–20, 20–30 and 30–40 cm) were collected in 2017. After feature selection from 249 environmental covariates by the Genetic Algorithm, 29 variables were used to fit models. The results showed SOC increased from southern to northern regions in the spatial scale and decreased with soil depths. From the result of independent verification (validation dataset: 80 sampling sites), RF (R²: 0.58, 0.71, 0.73, 0.74 and RMSE: 3.49, 3.49, 2.95, 2.80 g kg⁻¹ in four depths) performed better than Cubist (R²: 0.46, 0.63, 0.67, 0.71 and RMSE: 3.83, 3.60, 3.03, 2.72 g kg⁻¹) and XGBoost (R²: 0.53, 0.67, 0.70, 0.71 and RMSE: 3.60, 3.60, 3.00, 2.83 g kg⁻¹) in terms of prediction accuracy and robustness. Soil, parent material and organism were the most important covariates in SOC prediction. This study provides the up-to-date spatial distribution of dryland SOC in Northeast and North China Plain, which is of great value for evaluating dynamics of soil quality after long-term cultivation. Full article

(This article belongs to the Special Issue Remote Sensing for Soil Mapping and Monitoring)

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23 pages, 2945 KiB

Open AccessArticle

Bio-Inspired Hybridization of Artificial Neural Networks: An Application for Mapping the Spatial Distribution of Soil Texture Fractions

by Ruhollah Taghizadeh-Mehrjardi, Mostafa Emadi, Ali Cherati, Brandon Heung, Amir Mosavi and Thomas Scholten

Remote Sens. 2021, 13(5), 1025; https://doi.org/10.3390/rs13051025 - 8 Mar 2021

Cited by 41 | Viewed by 4304

Abstract

Soil texture and particle size fractions (PSFs) are a critical characteristic of soil that influences most physical, chemical, and biological properties of soil; furthermore, reliable spatial predictions of PSFs are crucial for agro-ecological modeling. Here, series of hybridized artificial neural network (ANN) models [...] Read more.

Soil texture and particle size fractions (PSFs) are a critical characteristic of soil that influences most physical, chemical, and biological properties of soil; furthermore, reliable spatial predictions of PSFs are crucial for agro-ecological modeling. Here, series of hybridized artificial neural network (ANN) models with bio-inspired metaheuristic optimization algorithms such as a genetic algorithm (GA-ANN), particle swarm optimization (PSO-ANN), bat (BAT-ANN), and monarch butterfly optimization (MBO-ANN) algorithms, were built for predicting PSFs for the Mazandaran Province of northern Iran. In total, 1595 composite surficial soil samples were collected, and 64 environmental covariates derived from terrain, climatic, remotely sensed, and categorical datasets were used as predictors. Models were tested using a repeated 10-fold nested cross-validation approach. The results indicate that the hybridized ANN methods were far superior to the reference approach using ANN with a backpropagation training algorithm (BP-ANN). Furthermore, the MBO-ANN approach was consistently determined to be the best approach and yielded the lowest error and uncertainty. The MBO-ANN model improved the predictions in terms of RMSE by 20% for clay, 10% for silt, and 24% for sand when compared to BP-ANN. The physiographical units, soil types, geology maps, rainfall, and temperature were the most important predictors of PSFs, followed by the terrain and remotely sensed data. This study demonstrates the effectiveness of bio-inspired algorithms for improving ANN models. The outputs of this study will support and inform sustainable soil management practices, agro-ecological modeling, and hydrological modeling for the Mazandaran Province of Iran. Full article

(This article belongs to the Special Issue Remote Sensing for Soil Mapping and Monitoring)

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Jump to: Research, Other

38 pages, 5895 KiB

Open AccessReview

Remote Sensing Data for Digital Soil Mapping in French Research—A Review

by Anne C. Richer-de-Forges, Qianqian Chen, Nicolas Baghdadi, Songchao Chen, Cécile Gomez, Stéphane Jacquemoud, Guillaume Martelet, Vera L. Mulder, Diego Urbina-Salazar, Emmanuelle Vaudour, Marie Weiss, Jean-Pierre Wigneron and Dominique Arrouays

Remote Sens. 2023, 15(12), 3070; https://doi.org/10.3390/rs15123070 - 12 Jun 2023

Cited by 14 | Viewed by 4542

Abstract

Soils are at the crossroads of many existential issues that humanity is currently facing. Soils are a finite resource that is under threat, mainly due to human pressure. There is an urgent need to map and monitor them at field, regional, and global [...] Read more.

Soils are at the crossroads of many existential issues that humanity is currently facing. Soils are a finite resource that is under threat, mainly due to human pressure. There is an urgent need to map and monitor them at field, regional, and global scales in order to improve their management and prevent their degradation. This remains a challenge due to the high and often complex spatial variability inherent to soils. Over the last four decades, major research efforts in the field of pedometrics have led to the development of methods allowing to capture the complex nature of soils. As a result, digital soil mapping (DSM) approaches have been developed for quantifying soils in space and time. DSM and monitoring have become operational thanks to the harmonization of soil databases, advances in spatial modeling and machine learning, and the increasing availability of spatiotemporal covariates, including the exponential increase in freely available remote sensing (RS) data. The latter boosted research in DSM, allowing the mapping of soils at high resolution and assessing the changes through time. We present a review of the main contributions and developments of French (inter)national research, which has a long history in both RS and DSM. Thanks to the French SPOT satellite constellation that started in the early 1980s, the French RS and soil research communities have pioneered DSM using remote sensing. This review describes the data, tools, and methods using RS imagery to support the spatial predictions of a wide range of soil properties and discusses their pros and cons. The review demonstrates that RS data are frequently used in soil mapping (i) by considering them as a substitute for analytical measurements, or (ii) by considering them as covariates related to the controlling factors of soil formation and evolution. It further highlights the great potential of RS imagery to improve DSM, and provides an overview of the main challenges and prospects related to digital soil mapping and future sensors. This opens up broad prospects for the use of RS for DSM and natural resource monitoring. Full article

(This article belongs to the Special Issue Remote Sensing for Soil Mapping and Monitoring)

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15 pages, 5937 KiB

Open AccessTechnical Note

Remote-Sensing-Based Sampling Design and Prescription Mapping for Soil Acidity

by Joaquin J. Casanova, Jenny L. Carlson and Melissa LeTourneau

Remote Sens. 2023, 15(12), 3105; https://doi.org/10.3390/rs15123105 - 14 Jun 2023

Viewed by 1589

Abstract

Soil acidification is a major problem in the inland Pacific Northwest. A potential solution is the application of lime to neutralize acidity and raise pH. As lime is an expensive input, precision variable rate application is necessary. However, high-resolution mapping of pH and [...] Read more.

Soil acidification is a major problem in the inland Pacific Northwest. A potential solution is the application of lime to neutralize acidity and raise pH. As lime is an expensive input, precision variable rate application is necessary. However, high-resolution mapping of pH and buffer pH for lime prescription requires costly sampling and analysis. To reduce the amount of sampling needed, remote sensing, which correlates with soil pH and buffer pH, can be used to determine optimal sampling locations and allow optimal interpolation. We used soil and crop data from the Washington State University R.J. Cook Agronomy Farm to develop an optimal sampling plan for a farmer’s property, followed that sampling design, and used the measured pH and buffer pH to fit a Bayesian hierarchical model using remote sensing variables specific to that farmer’s land. Following this, a new model was developed for the research farm with similar covariates. Ultimately, on the farmer’s field, we observed a root mean square error (RMSE) of 0.2487 for soil pH at a depth of 0–10 cm and 0.1221 for modified Mehlich buffer at 0–10 cm of depth. For the research farm, where buffer pH was not measured, we saw an RMSE of 0.3272 for soil pH at 0–10 cm of depth and 0.3381 for soil pH at 10–20 cm of depth. The ability make predictions of soil acidity with uncertainty using this technique allows for prescription lime application while optimizing soil sampling and testing. Further, this paper serves as a case study of on-farm research. Full article

(This article belongs to the Special Issue Remote Sensing for Soil Mapping and Monitoring)

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