Synthetic Soil Image Research Articles

Assessing soil degradation in Brazilian agriculture by a remote sensing approach to monitor bare soil frequency: impact on soil carbon

In countries with extensive agricultural practices, there is a significant risk of soil degradation, making it essential to develop techniques for understanding and detecting these changes. In this study, we used an earth observation system to identify the temporal bare soil frequency and thus, relate it with soil tillage and its impact on soil carbon degradation. The work was performed in two important agricultural states of Brazil, São Paulo and Paraná. For that, historical field and Remote Sensing (RS) data were analyzed to identify the relation between bare soil areas and their degradation. The frequency of bare soil was detected by Landsat images, in the last 36 years using the Geospatial Soil Sensing System (GEOS3). Historical soil surface temperature data was produced using the same images. In addition, legacy pedological and crops (i.e., soil cover) maps were used. Finally, soil texture information was spatialized based on a synthetic soil image (SISY). A total of 28,000 sites with topsoil organic carbon (SOC) were used as the reference for degradation. The soils of the state of Paraná presented significantly lower bare soil areas when compared to the state of São Paulo, mainly due to the wide use of the No-Tillage system. The advancement of sugarcane harvesting technologies together with the "boom" of the commodities after 2000s was responsible for the considerable increases in soil cover conservation. It was noticed that the more exposed the soil remains, the less carbon it has, having a negative correlation (r≈ −0.5). Sandy soils in both states proved to be the ones that were subject to the highest exposure rates and thus, more degraded. This fact is of concern, given that sandy soils are more susceptible to degradation factors, such as erosion. We observed important historical public policies related to the temporal tillage systems adopted by agro-community, which had a significant impact on carbon dynamics. The remote technique was able to infer how the soil has been managed. This information is crucial as it provides a solid basis for developing future public policies aimed at sustainable production.

Sand subfractions by proximal and satellite sensing: Optimizing agricultural expansion in tropical sandy soils

Sandy soils, which expressly cover 7% of the Earth’s land surface, are known for their management complexity and their significant influence on the proportion of sand subfractions in terms of physical, chemical, and physical-hydric properties. Proximal and remote sensing techniques offer cost-effective ways to improve soil evaluation. To address this gap, this study evaluates the potential of different sensing techniques, including laboratory-based analysis using the FieldSpec 3 spectroradiometer equipment and satellite imagery, to characterize and estimate sandy soil texture fractions and subfractions. We first defined 216 samplings in Mato Grosso State, Brazil considering the pedology, geology, synthetic soil image (exposed soil), curvature and slope of the terrain. This sampling location include sandy soils with different proportions of sand subfractions and mineralogy within a region of agricultural expansion. The sensing data used in the study were composed of total element concentrations obtained by pXRF, Vis-NIR-SWIR, and MIR spectra. The satellite data were obtained from exposed soil images of Landsat 5 and Sentinel 2, and also from simulations of Landsat 5, Sentinel 2, and Terra (ASTER). The proximal and satellite data were descriptively analysed and used to estimate the levels of clay, total sand (TS), very coarse sand (VCS), coarse sand (CS), medium sand (MS), fine sand (FS) and very fine sand (VFS). It was observed, at both the proximal and the satellite levels, that the reflectance intensity of sandy soils is inversely proportional to the particle diameter of the predominant sand subfraction. Proximal level models were slightly more accurate in predicting the texture fractions of sandy soils compared to models based on satellite data (mean validation R2 0,45 and 0,40, respectively). In both models, SWIR and MIR stand out as key predictor variables. The results obtained in this study can be implemented to optimise the expansion of agricultural frontiers in sandy soils in other areas.

Minimizing vegetation influence on soil salinity mapping with novel bare soil pixels from multi-temporal images

Optical remote sensing satellites provide rapid access to regional topsoil salinization mapping. However, mapping topsoil salinization based on spectral reflectance is always affected by background material like vegetation cover, straw mulching and soil types. In light of these challenges, this study investigates the potential of image fusion, where images of original and bare soil pixels were combined, to minimize the impact of vegetation cover on topsoil salinity mapping. A case study was presented for the typical vegetation cover area using synchronized Sentinel-2 MSI image (named original image) and 255 ground-truth data collected in October 2020, aligning with periods of vegetation cover and salt return. Furthermore, to obtain novel bare soil pixels, multi-temporal Sentinel-2 MSI images were acquired during two distinct intervals: March to May and September to November, spanning the years from 2018 to 2021. The synthetic soil image (SYSI) was obtained by extracting bare soil pixels from multi-temporal images. Two images (original, SYSI) were fused with non-negative matrix factorization (NMF) method, named SYSIfused. Then, the stacking machine algorithm was used for soil salinity mapping under different soil types, with evaluating the impact of SYSIfused on the accuracy of soil salinity prediction. The results showed the SYSIfused outperformed the original image (the R2 of the best models increased by 0.054–0.242, RMSE and MAE decreased by 0.049–0.780 and 0.012–0.546, respectively). Based on the SYSIfused, the order of the effect of soil types was coastal bog solonchaks > alluvial soil > cinnamon soil > coral saline soil > overall samples, and their roles in improving the R2 of the model were 0.141, 0.085, 0.022, 0.012, respectively. Besides, stacking models with the SYSIfused provided the best prediction performances (R2 = 0.742, RMSE = 0.377, MAE = 0.362). This study introduces the concept of merging original images with SYSI, resulting in a significant improvement in the accuracy of soil salinity mapping in areas covered by vegetation.

Remote sensing imagery detects hydromorphic soils hidden under agriculture system

The pressure for food production has expanded agriculture frontiers worldwide, posing a threat to water resources. For instance, placing crop systems over hydromorphic soils (HS), have a direct impact on groundwater and influence the recharge of riverine ecosystems. Environmental regulations improved over the past decades, but it is difficult to detect and protect these soils. To overcome this issue, we applied a temporal remote sensing strategy to generate a synthetic soil image (SYSI) associated with random forest (RF) to map HS in an 735,953.8 km2 area in Brazil. HS presented different spectral patterns from other soils, allowing the detection by satellite sensors. Slope and SYSI contributed the most for the prediction model using RF with cross validation (accuracy of 0.92). The assessments showed that 14.5% of the study area represented HS, mostly located inside agricultural areas. Soybean and pasture areas had up to 14.9% while sugar cane had just 3%. Here we present an advanced remote sensing technique that may improve the identification of HS under agriculture and assist public policies for their conservation.

Spectral Mixture Modeling of an ASTER Bare Soil Synthetic Image Using a Representative Spectral Library to Map Soils in Central-Brazil

Pedological maps in suitable scales are scarce in most countries due to the high costs involved in soil surveying. Therefore, methods for surveying and mapping must be developed to overpass the cartographic material obtention. In this sense, this work aims at assessing a digital soil map (DSM) built by multispectral data extrapolation from a source area to a target area using the ASTER time series modeling technique. For that process, eight representative toposequences were established in two contiguous micro-watersheds, with a total of 42 soil profiles for analyses and classification. We found Ferralsols, Plinthosols, Regosols, and a few Cambisols, Arenosols, Gleisols, and Histosols, typical of tropical regions. In the laboratory, surface soil samples were submitted to spectral readings from 0.40 µm to 2.50 µm. The soil spectra were morphologically interpreted, identifying shapes and main features typical of tropical soils. Soil texture grouped the curves by cluster analysis, forming a spectral library (SL). In parallel, an ASTER time series (2001, 2004, and 2006) was processed, generating a bare soil synthetic soil image (SySI) covering 39.7% of the target area. Multiple Endmember Spectral Mixture Analysis modeled the SL on the SySI generating DSM with 73% of Kappa index, in which identified about 77% is covered by rhodic Ferralsols. Besides the overestimation, the DSM represented the study area’s pedodiversity. Given the discussion raised, we consider including subsoil data and other features using other sensors in operations modeled by machine learning algorithms to improve results.

Soil Erosion Satellite-Based Estimation in Cropland for Soil Conservation

Intensive cropland expansion for an increasing population has driven soil degradation worldwide. Modeling how agroecosystems respond to variations in soil attributes, relief and crop management dynamics can guide soil conservation. This research presents a new approach to evaluate soil loss by water erosion in cropland using the RUSLE model and Synthetic Soil Image (spectroscopy technique), which uses time series remotely sensed environmental, agricultural and anthropic variables, in the southeast region of São Paulo State, Brazil. The availability of the open-access satellite images of Tropical Rainfall Measuring Mission (TRMM) and Landsat satellite images provided ten years of rainfall data and 35 years of exposed soil surface. The bare soil surface and agricultural land use were extracted, and the multi-temporal rainfall erosivity was assessed. We predict soil maps’ attributes (texture and organic matter) through innovative soil spectroscopy techniques to assess the soil erodibility and soil loss tolerance. The erosivity, erodibility, and topography obtained by the Earth observations were adopted to estimate soil erosion in four scenarios of sugarcane (Saccharum spp.) residue coverage (0%, 50%, 75%, and 100%) in five years of the sugarcane cycle: the first year of sugarcane harvest and four subsequent harvesting years from 2013 to 2017. Soil loss tolerance means 4.3 Mg ha−1 exceeds the minimum rate in 40% of the region, resulting in a total soil loss of ~6 million Mg yr−1 under total coverage management (7 Mg ha−1). Our findings suggest that sugarcane straw production has not been sufficient to protect the soil loss against water erosion. Thus, straw removal is unfeasible unless alternative conservation practices are adopted, such as minimum soil tillage, contour lines, terracing and other techniques that favor increases in organic matter content and soil flocculating cations. This research also identifies a spatiotemporal erosion-prone area that requests an immediately sustainable land development guide to restore and rehabilitate the vulnerable ecosystem service. The high-resolution spatially distribution method provided can identify soil degradation-prone areas and the cropland expansion frequency. This information may guide farms and the policymakers for a better request of conservation practices according to site-specific management variation.

Detection of bare soils in sugarcane areas by temporal satellite images: A monitoring technique for soil security

Bare soil triggers several undesirable processes for its quality and remote sensing can be a powerful tool to monitoring its occurrence. This work aims to apply multi-temporal satellite image techniques to detect bare soil areas under sugarcane cultivation and relate with soil security. The study was carried out in an area of 2,574 km² located in Brazil. The MapBiomas land use and cover collection was used to know the sugarcane area changes from 1985 to 2019. A collection of Landsat images over 35 years (1985 to 2019) were used to create Synthetic Soil Images (SYSIs) and the Bare Soil Frequency Images (BSF) of the area. SYSIs were generated annually, in the rainy and dry season. BSFs was generated in the total period and every five years by dry and rainy season. Thus, the land use changes and bare soil occurrence were compared to categorical maps of soil types, surface clay classes and slope, and also with economic, social and political changes in the period. In general, the bare soil increased from 1985 to 2006, and began to decline thereafter because of “Agro-environmental Protocol’ that anticipated the end of pre-harvest burning in sugarcane crop. BSF in the rainy season decreased over the period motivated by knowledge of farmers and changes in management. Despite this, many prone to erosion soils classes (Arenosols, Lixisols/Acrisols) remain under conventional tillage in the rainy season. We concluded that the use of multi-temporal satellite images is an important approach to monitoring soil management contributing to soil security.

Regional soil organic matter mapping models based on the optimal time window, feature selection algorithm and Google Earth Engine

The spatial distribution of soil organic matter (SOM) is highly significant to the assessment of the regional carbon balance, food security and cultivated land quality. Due to climate change and the increasing food demand, the intensity of cultivated land development in the Northeast China black soil region is increasing, and it is urgent to accurately map the SOM content in this region. Remote sensing technology has been widely applied in the field of soil mapping, but large-scale and high-precision soil mapping remains a significant challenge. In this study, the Google Earth Engine (GEE) platform is adopted to generate synthetic soil images based on Landsat-8 and Sentinel-2 images capturing bare soil periods at 20-d intervals. Then, the spectral index and band are adopted as input variables to evaluate the prediction accuracy of these synthetic images depicting different periods using random forest (RF) regression. Finally, two feature selection methods (Boruta and recursive feature elimination (RFE)) are employed to evaluate the performance of these two methods. The results indicate that 1) the optimal time window for SOM prediction is day of year (DOY) 120–140 for the Songnen Plain; 2) the performance of SOM prediction based on Landsat-8 synthetic images is better than that based on Sentinel-2 synthetic images; and 3) both feature selection methods improve the SOM prediction accuracy, but RFE has the highest accuracy(Landsat-8 with Coefficient of Determination (R2) of 0.702, Root Mean Square Error (RMSE) of 0.681%; Sentinel-2 with R2 of 0.5963, RMSE of 0.793%). This study provides a new model for large-scale and high-spatial resolution SOM prediction and verifies the importance of the time window to the SOM prediction accuracy.

Ground Observations and Environmental Covariates Integration for Mapping of Soil Salinity: A Machine Learning-Based Approach

Soil salinization is a severe danger to agricultural activity in arid and semi-arid areas, reducing crop production and contributing to land destruction. This investigation aimed to utilize machine learning algorithms to predict spatial soil salinity (dS m−1) by combining environmental covariates derived from remotely sensed (RS) data, a digital elevation model (DEM), and proximal sensing (PS). The study is located in an arid region, southern Iran (52°51′–53°02′E; 28°16′–28°29′N), in which we collected 300 surface soil samples and acquired the spectral data with RS (Sentinel-2) and PS (electromagnetic induction instrument (EMI) and portable X-ray fluorescence (pXRF)). Afterward, we analyzed the data using five machine learning methods as follows: random forest—RF, k-nearest neighbors—kNN, support vector machines—SVM, partial least squares regression—PLSR, artificial neural networks—ANN, and the ensemble of individual models. To estimate the electrical conductivity of the saturated paste extract (ECe), we built three scenarios, including Scenario (1): Synthetic Soil Image (SySI) bands and salinity indices derived from it; Scenario (2): RS data, PS data, topographic attributes, and geology and geomorphology maps; and Scenario (3): the combination of Scenarios (1) and (2). The best prediction accuracy was obtained for the RF model in Scenario (3) (R2 = 0.48 and RMSE = 2.49), followed by Scenario (2) (RF model, R2 = 0.47 and RMSE = 2.50) and Scenario (1) for the SVM model (R2 = 0.26 and RMSE = 2.97). According to ensemble modeling, a combined strategy with the five models exceeded the performance of all the single ones and predicted soil salinity in all scenarios. The results revealed that the ensemble modeling method had higher reliability and more accurate predictive soil salinity than the individual approach. Relative improvement (RI%) showed that the R2 index in the ensemble model improved compared to the most precise prediction for the Scenarios (1), (2), and (3) with 120.95%, 56.82%, and 66.71%, respectively. We applied the best model in each scenario for mapping the soil salinity in the selected area, which indicated that ECe tended to increase from the northwestern to south and southeastern regions. The area with high ECe was located in the regions that mainly had low elevations and playa. The areas with low ECe were located in the higher elevations with steeper slopes and alluvial fans, and thus, relief had great importance. This study provides a precise, cost-effective, and scientific base prediction for decision-making purposes to map soil salinity in arid regions.

Obtaining high-resolution synthetic soil imagery for topsoil mapping

ABSTRACT Detailed soil maps contribute to the understanding of agricultural and food value chains. PlanetScope satellite constellations have high spatial and temporal resolution and could be used to develop Synthetic Soil Image (SYSI) at specific dates of field crops calendar. This work aimed to derive a SYSI from PlanetScope time series and estimate soil clay content, an important attribute related to several soil functions. The normalization process was applied to make the radiometries of the different Doves’ images compatible and, subsequently, land cover classification was performed to select the bare soil class, obtaining the SYSI-Planet. The clay content was predicted for 2337 topsoil samples using the SYSI-Planet and Random Forest algorithm. The normalization of PlanetScope images were based on reference images, which mitigated the radiometric differences from acquisition dates and different radiometric calibrations. SYSI-Planet resulted in an excellent clay content prediction, resulting in a coefficient of determination (R 2) equal to 0.93 and Root-Mean-Square Error (RMSE) equal to 70.90. The predicted clay mapped revealed spatial patterns with a high spatial resolution (~3 m), which can enhance precise agricultural management.

Spatial prediction of soil surface properties in an arid region using synthetic soil image and machine learning

Evaluation of spatial variability and mapping of soil properties is critical for sustainable agricultural production in arid lands. The main objectives of the present study were to spatialize soil organic carbon (SOC), soil particle size distribution(clay, sand, and silt contents), and calcium carbonate equivalent (CCE) by integrating multisource environmental covariates, including digital elevation model (DEM) and remote sensing data by machine learning (Cubist, Cu and random forest, RF) in an arid region of Iran. Additionally, Synthetic Soil Images (SySI) were achieved from multi-temporal images of bare soil pixels based on Landsats 4, 5, 7, 8, and a DEM. Three hundred topsoil samples (0–30 cm depth) were collected based on the conditioned Latin hypercube sampling (cLHS) approach in Afzar district, Fars province, southern Iran. The models were calibrated and validated by the 10-fold cross-validation approach, and the performance was evaluated using root mean square error (RMSE), the ratio of the performance to interquartile distance (RPIQ), and coefficient of determination (R 2). Also, the prediction accuracy was assessed by the relative RMSE (rRMSE). The performance of the best models based on the RPIQ index showed that the model for predicting clay (1.89) had a good prediction, for sand (1.64), SOC (1.55), and CCE (1.59) had a fair prediction, while the model for silt (1.13) performed poorly. We found that the Cu and RF models had the highest and lowest prediction accuracies for CCE (rRMSE = 14.31%) and SOC (rRMSE = 43.93%), respectively. We discovered that a combination of high-quality RS data (SySI) and variables derived from DEM were reasonably able to predict soil properties. We revealed a strong promise to enhance the accuracy of digital soil mapping, especially in regions with limited soil data. Moreover, the application of RS data can reduce the soil sampling cost and, accordingly, soil mapping.

Soil parent material prediction through satellite multispectral analysis on a regional scale at the Western Paulista Plateau, Brazil

Parent material is the main source for soil textural, mineralogical, and other physical attributes. The knowledge over this factor is explored generally in low scale geology maps, insufficient for most users. Remote sensing can offer assistance in this regard, since it allows the evaluation of soil properties, as largely indicated in literature, being a potential tool to delineate parent material. Thus, we explored a multi temporal Landsat image composition with bare soil reflectance to extract soil properties and distinguish discrepant lithological classes at the western plateau, São Paulo State, Brazil. The area is 247,737 ha large, where 981 soil samples were collected at 0–20 cm depth. We acquired the synthetic soil image and linked the pixel's spectra with soil attributes. We performed a digital soil mapping procedure to generate maps of attributes related to parent material. The soil maps offered a great input on identifying the transitions between sandstone and basalt as soils from these formations have significant differences in clay, sand, Fe2O3 and TiO2 contents. Therefore, the use of remote sensing coupled with digital soil mapping is a strong alternative to conventional methods to improve low scale PM maps to enhance detail on regional and local scales.

Integration of multispectral and hyperspectral data to map magnetic susceptibility and soil attributes at depth: A novel framework

The understanding of attributes and magnetic susceptibility (χ) at soil surface, mainly subsurface, is crucial due to their role to identify climate changes, soil degradation, soil classification systems, soil fertility, and pedogenesis. The integration of proximal sensing (PS) and remote sensing (RS) data sources could increase the efficiency of Digital Soil Mapping. Nevertheless, products of this integration need to be evaluated in hybrid, stochastic, and deterministic models to predict soil attributes and χ at surface and subsurface. This study investigates the PS and RS integration by applying four deterministic (e.g. Bayesian Regularised Neural Network, Generalised Linear Model, Random Forest and Cubist) and hybrid models (e.g. Regression Kriging of residuals of the best-fitted model) to create a new environmental variable, the Best Synthetic Soil Image (BSSI), at three soil depths (e.g. 0 – 20, 40 – 60 and 80 – 100 cm) that quantitatively represent the soil spectral signature. We also used the BSSI in a comparison with bare soil surface (e.g. SYSI - Synthetic Soil Image) to predict soil attributes and χ by performing the deterministic and hybrid models. We hypothesize that the BSSI, which integrates PS and RS data, enhances soil modelling predictions at subsurface by selecting the best model approach. The BSSI demonstrated original and valuable contribution to increase the predictive model power at deeper layers, while SYSI was effective at upper layers. The PS and RS integration helped to identify the main soil patterns horizontally and vertically, which traditional soil surveys have not been capable of representing.

Soil variability and quantification based on Sentinel-2 and Landsat-8 bare soil images: A comparison

There is a worldwide need for detailed spatial information to support soil mapping, mainly in the tropics, where main agricultural areas are concentrated. In this line, satellite images are useful tools that can assist in obtaining soil information from a synoptic point of view. This study aimed at evaluating how satellite images at different resolutions (spatial, spectral and temporal) can influence the representation of soil variability over time, the percentage of bare soil areas and spatial predictions of soil properties in southeastern Brazil. We used single-date and multi-temporal images (SYSI, Synthetic Soil Images) of bare soil pixels from the Sentinel2-MultiSpectral Instrument (S2-MSI) and the Landsat-8 Operational Land Imager (L8-OLI) to conduct this research. Two SYSIs were obtained from images acquired in four years (2016–2019) for each satellite (SYSI S2-MSI and SYSI L8-OLI) and a third SYSI, named SYSI Combined, was obtained by combining the images from both satellites. The single-date images for each satellite was acquired in September, when the influence of clouds was low and bare soil pixels was predominant. Single-date images and SYSIs were compared by means of their spectral patterns and ability to predict topsoil properties (clay, sand, silt, and organic matter contents and soil color) using the Cubist algorithm. We found that the SYSIs outperformed single-date images and that the SYSI Combined and SYSI L8-OLI provided the best prediction performances. The SYSIs also had the highest percentage of areas with bare soil pixels (~30–50%) when compared to the single-date images (~20%). Our results suggest that bare soil images obtained by combining Landsat-8 and Sentinel-2 images are more important for soil mapping than spatial or spectral resolutions. Soil maps obtained via satellite images are important tools for soil survey, land planning, management and precision agriculture.

To what extent can multifractal measures provide an accurate model of the porosity of soils?

AbstractOver the last decades, several authors have suggested that multifractal measures, that is, self‐similar measures defined on fractal or non‐fractal objects, could be useful to describe soil properties, to model soil processes, and to deal with their extreme microscale heterogeneity. In this context, a key question relates to the extent to which multifractal measures can indeed fulfill all the expectations they have generated. To address this question, we discuss the possibility of generating a synthetic soil image exhibiting multifractal porosity. To this end, a simple geometrical multifractal model in 2D is developed, which helps us to better understand the concept of multifractality and to generate images. We show that it is possible to generate synthetic binary images over a limited range of scales, but that a pure multifractal model for the distribution of the solid or pore mass cannot be developed due to physical constraints. Moreover, in the generated images mimicking multifractal solid space, a higher degree of multifractality corresponds to a larger porosity, rendering it difficult to tune model parameters to match actual soil properties. In addition, simple statistics relying on power‐law fits appear insufficient to characterize soil architecture even if they may capture some key multiscale indicators of observed spatial heterogeneity. We argue that the same conclusions would be reached in a three‐dimensional space, as well as for grey‐scales images.

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

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.

Scaling properties of binary and greyscale images in the context of X-ray soil tomography

Characterization of the complex soil structure is one the cornerstones of soil science and pore space detection is a crucial step in this process. Synthetic soil image construction has been proved to be an efficient resource for validating different binarization methods given that, unlike in real world, ground truth information is known. In this work, we introduce an improved Truncated Multifractal Method (TMM), to better simulate synthetic computed tomography (CT) soil images and then we generate 150 synthetic images with three different porosities (7%, 12% and 17%), both in greyscale and in binary scale (pore spaces). Synthetic images are then compared with two sets of 260 slides of real CT soil images, in order to validate the goodness of the method. All images are subjected to multifractal analysis where we show a detailed comparative analysis of parameters such as lacunarity, characteristic length and multifractal spectrum, that are calculated both for the whole set of synthetic (greyscale and binary) and for the sets of real CT soil images. With respect to lacunarity, a not previously reported inverse relationship between binary and grey lacunarity is found for this range of porosities. Moreover, we have also reported a new relationship between lacunarity and characteristic length. Similar multifractal results, that we obtain for real CT and synthetic soil images, prove that TMM is a reasonable solution to create simulated CT soil images. Finally, a segmentation test was carried out, using TMM synthetic greyscale soil images and its binary counterpart as ground-truth information, evaluating global (Otsu) and local (Combining Singularity-CA) binarization methods, where we report better performance for the last.

Pedology and soil class mapping from proximal and remote sensed data

Pedological assessment and mapping by hyperspectral proximal and multispectral remote sensing comes up as an important alternative for large extent areas with high soil variability. Strategies indicating how to integrate these multi-source data for digital soil mapping are emerging. In this paper, we used proximal and remote sensed data to perform pedological assessments and to support a detailed soil class mapping by MESMA. The study area is located in Southeast of Federal District, Brazil. Six toposequences were defined according to pedomorphogeological assessments and 34 sites were collected for laboratory analysis (texture and chemical) and VIS-NIR-SWIR (350–2500 nm) spectroscopy reflectance analysis. We assessed soil mineralogy based on derivative analysis of topsoil reflectance and we grouped observations to recognize topsoil spectral patterns by clustering method. Topsoil patterns (mean spectral curve for each cluster) were convolved using a Gaussian function of Landsat 5-TM spectral bands to obtain endmembers. Then, we used a Landsat 5 TM time series to produce a bare soil composite denominated Synthetic Soil Image (SYSI). Endmembers and SYSI were used as input data for Multiple Endmember Spectral Mixture Analysis (MESMA) to map the soil classes. Topsoil spectra clustered soil samples that were similar in texture, mineralogy and color, and identified 13 topsoil patterns (endmembers). SYSI retrieved 74% of bare soil area coverage and presented very similar spectral patterns to endmembers. The RGB 543 composite highlighted the mineralogy (i.g. sesquioxides, kaolinite and quartz), texture and color of the soils. MESMA modeled almost 100% of SYSI from endmembers, with low global RMSE of 0.86% and high global fraction of 62%. Soil classes were mapped using topsoil reflectance patterns and satellite images by MESMA method. We validated the digital soil map by using independent field-visited sites, which reached a Kappa coefficient of 0.73. We efficiently assessed soil mineralogy and recognized patterns from laboratory topsoil spectra. We successfully mapped soil classes using topsoil reflectance patterns and a bare soil composite image by MESMA method.

Improvement of Clay and Sand Quantification Based on a Novel Approach with a Focus on Multispectral Satellite Images

Soil mapping demands large-scale surveys that are costly and time consuming. It is necessary to identify strategies with reduced costs to obtain detailed information for soil mapping. We aimed to compare multispectral satellite image and relief parameters for the quantification and mapping of clay and sand contents. The Temporal Synthetic Spectral (TESS) reflectance and Synthetic Soil Image (SYSI) approaches were used to identify and characterize texture spectral signatures at the image level. Soil samples were collected (0–20 cm depth, 919 points) from an area of 14,614 km2 in Brazil for reference and model calibration. We compared different prediction approaches: (a) TESS and SYSI; (b) Relief-Derived Covariates (RDC); and (c) SYSI plus RDC. The TESS method produced highly similar behavior to the laboratory convolved data. The sandy textural class showed a greater increase in average spectral reflectance from Band 1 to 7 compared with the clayey class. The prediction using SYSI produced a better result for clay (R2 = 0.83; RMSE = 65.0 g kg−1) and sand (R2 = 0.86; RMSE = 79.9 g kg−1). Multispectral satellite images were more stable for the identification of soil properties than relief parameters.

Geospatial Soil Sensing System (GEOS3): A powerful data mining procedure to retrieve soil spectral reflectance from satellite images

Soil mapping has been identified as key to environmental issues. The determination of soil attributes to achieve the best decision making on land use planning is crucial. The use of remote sensing (satellite images) can improve understanding of the surface, since it collects a spectral reflectance fingerprint related to soil properties. However, methodologies still gather spatially fragmented information on bare soil in a single image; thus, there is still room to improve information as a continuous surface. This work has the purpose of developing a procedure using multi-temporal satellite image information, aiming to construct a single synthetic image which would represent soils. The work was carried out in the state of São Paulo, Brazil, on a site covering 14,614 km2. The procedure, designated as Geospatial Soil Sensing System (GEOS3), is based on the following steps: a) creation of a database with Landsat 5 legacy data.; b) filtering of the database to provide images only from the dry season in the region; c) insertion of a set of rules into the system to filter other objects besides soils; d) Each bare soil occurrence for each location along the time-series was used to calculate a Temporal Synthetic Spectral Reflectance (TESS) of the soil surface; e) aggregation of all TESS composes the Synthetic Soil Image (SYSI); f) quantitative and qualitative validation of the SYSI through the correlation between laboratory and TESS, soil line assessment and the principal component analysis (PCA). GEOS3 was able to provide the best representative reflectance of soils for each band during the historical period. Thus, TESS is not the ‘true’ but a synthetic spectral reflectance. The canonical correlation between laboratory and satellite data reached 0.93. A value of up to 0.88 in the Pearson's correlation between laboratory and TESS was also achieved. In a single scene, only 0.5% of area was available as isolated bare soil for spatial analysis. However, SYSI reached 68%. Considering only the sugarcane agricultural areas, a value of 92% was achieved. Our study indicates that a multi-temporal data mining procedure can retrieve soil surface representation. The key to the results was calculating the median spectral reflectance from the bare soil pixels along the period of the time series. GEOS3 products can aid soil evaluation by assisting in digital soil mapping, soil security, precision agriculture, soil attribute quantification, soil conservation, environment monitoring and soil sample allocation, among others.