Is it possible to map subsurface soil attributes by satellite spectral transfer models?

Mendes, Wanderson de Sousa; Neto, Luiz G. Medeiros; Demattê, José Alexandre Melo; Gallo, Bruna Cristina; Rizzo, Rodnei; Safanelli, José Lucas; Fongaro, Caio Troula

doi:10.1016/j.geoderma.2019.01.025

Cited by 47 publications

(26 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For our study, SySI represents the soils surface of agriculture areas and other natural surfaces with low vegetation cover and rock outcrops, when the vegetation was absent or almost absent, typical for savanas. The GEOS3 has also been implemented in different regions in Brazil for mapping soil variables [11,12,47]. Similar approaches were developed to produce bare soil composites based on Landsat data and accurately employed for soil mapping and management in Germany [9] and the Swiss Plateau and Europe [8].…”

Section: Landsat-derived Datamentioning

confidence: 99%

“…The better model performance in lower layers are related to soil conditions at such depths. A possible factor impacting surface-subsurface predictions are the agricultural practices, where soil management could be increasing the system's complexity [12]. While the chemical and physical weathering are more intense and active in surface, alterations in depth tend to be less intense [59].…”

Section: Machine Learningmentioning

confidence: 99%

See 1 more Smart Citation

Mapping at 30 m Resolution of Soil Attributes at Multiple Depths in Midwest Brazil

et al. 2019

Self Cite

View full text Add to dashboard Cite

The Midwest region in Brazil has the largest and most recent agricultural frontier in the country where there is no currently detailed soil information to support the agricultural intensification. Producing large-extent digital soil maps demands a huge volume of data and high computing capacity. This paper proposed mapping surface and subsurface key soil attributes with 30 m-resolution in a large area of Midwest Brazil. These soil maps at multiple depth increments will provide adequate information to guide land use throughout the region. The study area comprises about 851,000 km2 in the Cerrado biome (savannah) in the Brazilian Midwest. We used soil data from 7908 sites of the Brazilian Soil Spectral Library and 231 of the Free Brazilian Repository for Open Soil Data. We selected nine key soil attributes for mapping and aggregated them into three depth intervals: 0–20, 20–60 and 60–100 cm. A total of 33 soil predictors were prepared using Google Earth Engine (GEE), such as climate and geologic features with 1 km-resolution, terrain and two new covariates with 30 m-resolution, based on satellite measurements of the topsoil reflectance and the seasonal variability in vegetation spectra. The scorpan model was adopted for mapping of soil variables using random forest regression (RF). We used the model-based optimization by tuning RF hyperparameters and calculated the scaled permutation importance of covariates in R software. Our results were promising, with a satisfactory model performance for physical and chemical attributes at all depth intervals. Elevation, climate and topsoil reflectance were the most important covariates in predicting sand, clay and silt. In general, for predicting soil chemical attributes, climatic variables, elevation and vegetation reflectance provided to be the most important of predictive components, while for organic matter it was a combination of climatic dynamics and reflectance bands from vegetation and topsoil. The multiple depth maps showed that soil attributes largely varied across the study area, from clayey to sandy, suggesting that less than 44% of the studied soils had good natural fertility. We concluded that key soil attributes from multiple depth increments can be mapped using Earth observations data and machine learning methods with good performance.

show abstract

Section: Landsat-derived Datamentioning

confidence: 99%

Section: Machine Learningmentioning

confidence: 99%

Mapping at 30 m Resolution of Soil Attributes at Multiple Depths in Midwest Brazil

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…The soil spectroscopy mainly encompasses the visible (Vis, 350-700 nm) and near-infrared (NIR, 700-2500 nm) ranges of the electromagnetic spectrum. In remote sensing, the NIR is usually split into two ranges because of the satellite sensors (Ogen et al, 2017;Crucil et al, 2019;Mendes et al, 2019) as NIR (700-1000 nm) and shortwave infrared (SWIR, 1000-2500 nm) (López-Maestresalas et al, 2016;Wilczyński et al, 2016). The composition presented in this study fixes in the SWIR region because our sensor only captures this range of the electromagnetic spectrum.…”

Section: Introductionmentioning

confidence: 99%

Soil spectral library of Piauí State using machine learning for laboratory analysis in Northeastern Brazil

Mendes¹,

Boechat²,

Gualberto³

et al. 2021

Revista Brasileira De Ciência Do Solo

View full text Add to dashboard Cite

Soil chemical and physical analyses are the major sources of data for agriculture. However, traditional soil analyses are time-consuming, not cost-efficient, and not environmentally friendly. An alternative to traditional soil analyses is soil spectroscopy. This technique is a low-cost and quick analytical method, which can be implemented in a laboratory and/or in-situ. Nevertheless, some spectrometers are expensive and do not contemplate the entire spectrum. Despite this limitation, the main objective of the study was to create a soil spectral library of the Piauí State using only the 1000-2500 nm range. In this sense, it was evaluated and standardized the soil spectral library by accessing the combination of smoothing, standard normal variate, continuum removal, and Savitzky-Golay derivative spectral preprocessing procedures with partial least squares, random forest, and cubist machine learning algorithms. It was collected 262 geo-referenced soil samples at the layer of 0.00-0.20 m across the entire Piauí State, representing most of its soil variability. The soil properties evaluated were pH(H 2 O), sand, clay, and soil organic carbon (SOC) contents. This study demonstrated that the Standard Normal Variate was one of the most promising preprocessing procedures to improve model predictions for pH(H 2 O), sand, and clay. For SOC and pH, the best overall results were without preprocessing the soil spectra. Moreover, the cubist model was the most accurate in predicting soil properties. Finally, our study showed evidence of the potential and feasibility of using this soil spectral library to estimate soil properties such as pH(H 2 O), sand, clay, and SOC.

show abstract

“…Soils of tropical Midwest Brazil usually present high weathering degree and tend to have relatively homogenous profiles [5,7]. Some studies has been shown that topsoil spectral patterns are related to the subsoil pattern variations and dynamic processes which occur within the soil profile [35][36][37]. In addition, bare topsoil reflectance composites produced from Landsat time series [38][39][40] were considered as reliable proxies of topsoil spatial patterns, which can be integrated with other datasets by machine learning to better capture information from deeper layers of the Earth's crust [30].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

Self Cite

View full text Add to dashboard Cite

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

show abstract

Is it possible to map subsurface soil attributes by satellite spectral transfer models?

Cited by 47 publications

References 52 publications

Mapping at 30 m Resolution of Soil Attributes at Multiple Depths in Midwest Brazil

Mapping at 30 m Resolution of Soil Attributes at Multiple Depths in Midwest Brazil

Soil spectral library of Piauí State using machine learning for laboratory analysis in Northeastern Brazil

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

Contact Info

Product

Resources

About