Geophysical sensors combined with machine learning algorithms were used to understand the pedosphere system and landscape processes and to model soil attributes. In this research, we used parent material, terrain attributes, and data from geophysical sensors in different combinations to test and compare different and novel machine learning algorithms to model soil attributes. We also analyzed the importance of pedoenvironmental variables in predictive models. For that, we collected soil physicochemical and geophysical data (gamma-ray emission from uranium, thorium, and potassium; magnetic susceptibility and apparent electric conductivity) by three sensors (gamma-ray spectrometer, RS 230; susceptibilimeter KT10, Terraplus; and conductivimeter, EM38 Geonics) at 75 points and analyzed the data. The models with the best performance (R² 0.48, 0.36, 0.44, 0.36, 0.25, and 0.31) varied for clay, sand, Fe₂O₃, TiO₂, SiO₂, and cation exchange capacity prediction, respectively. Modeling with the selection of covariates at three phases (variance close to zero, removal by correction, and removal by importance) was adequate to increase the parsimony. The results were validated using the method “nested leave-one-out cross-validation”. The prediction of soil attributes by machine learning algorithms yielded adequate values for field-collected data, without any sample preparation, for most of the tested predictors (R² values ranging from 0.20 to 0.50). Also, the use of four regression algorithms proved to be important since at least one of the predictors used one of the tested algorithms. The performance values of the best algorithms for each predictor were higher than those obtained with the use of a mean value for the entire area comparing the values of root mean square error (RMSE) and mean absolute error (MAE). The best combination of sensors that reached the highest model performance was that of the gamma-ray spectrometer and the susceptibilimeter. The most important variables for most predictions were parent material, digital elevation, standardized height, and magnetic susceptibility. We concluded that soil attributes can be efficiently modeled by geophysical data using machine learning techniques and geophysical sensor combinations. This approach can facilitate future soil mapping in a more time-efficient and environmentally friendly manner.

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.

Soil apparent electrical conductivity (ECa) is related to various soil attributes and processes. This research aimed to understand the relationship between ECa and paedogenesis, lithology and attributes. The study area is located in São Paulo State, Brazil. Soil samples were collected for physical-chemical analysis from 79 locations (0–20 cm layer). A geophysical field-portable equipment (Geonics EM38-MK2 conductivity meter) was used to measure soil ECa. For that, four toposequences were selected on landscape. Sixteen soil profiles were allocated in the landscape for soil characterization and classification. The ECa values were measured across toposequences, soil types and lithology variations, at the same 79 locations used for physico-chemical characterization. The statistical analysis and Kruskal–Wallis rank sum test and Cluster analysis by k-means and Wilcoxon–Mann–Whitney test was performed to verify differences in ECa values over lithology, relief and soil types. The values of ECa measurements were related to soil attributes variation. The results showed that lithology strongly affects ECa values, mainly in diabase-derived soils, followed by metamorphosed siltstone. In fluvial sediments, the ECa exhibits different behaviours. In less evolved soils, the lithology contributed more to ECa than paedogenesis, as the opposite occurs in more evolved ones. The ECa decreases downslope and correlates with lithology alteration and soil types. This study demonstrates that soil ECa can be a tool to differentiate between various soil types and lithological transitions across the landscape, supporting the soil survey and mapping at finer scales. Graphical Abstract

Despite occupying a large area of the globe and being the next agricultural frontier, sandy soils are seldom explored in scientific studies. Considering the high capacity of remote sensing in soil characterization, this work aimed to: (i) characterize sandy soils’ profiles from proximal sensing; (ii) assess the ability of visible, near, and short-wave infrared (Vis-NIR-SWIR) as well as mid-infrared (MIR) spectroscopy to distinguish soil classes of highly sandy content; (iii) quantify physical and chemical attributes of sandy soil profiles from Vis-NIR-SWIR and MIR spectroscopy as well as X-ray fluorescence (pXRF). Samples were described and collected from 29 sandy soil profiles. The 127 samples went under Vis-NIR-SWIR and MIR spectroscopy, X-ray fluorescence, and chemical and physical analyses. The spectra were analyzed based on “Morphological Interpretation of Reflectance Spectrum” (MIRS), Principal Components Analysis (PCA), and cluster methodology to characterize soils. The integration of different information obtained by remote sensors, such as Vis-NIR-SWIR, MIR, and Portable X-ray Fluorescence (pXRF), allows for pedologically complex characterizations and conclusions in a short period and with low investment in analysis and reagents. The application of MIRS concepts in the VNS spectra of sandy soils showed high potential for distinguishing pedological classes of sandy soils. The MIR spectra did not show distinct patterns in the general shapes of the curves and reflectance intensities between sandy soil classes. However, even so, this region showed potential for identifying mineralogical constitution, texture, and OM contents, assuming high importance for the complementation of soil pedometric characterizations using VNS spectroscopy. The VNS and MIR data, combined or isolated, showed excellent predictive performance for the estimation of sandy soil attributes (R² > 0.8). Sandy soil color indices, which are very important for soil classification, can be predicted with excellent accuracy (R² from 0.74 to 0.99) using VNS spectroscopy or the combination of VNS + MIR.

This study analyzed the role of soil health (SH) and ecosystem services (ESs) in global mangrove research articles from 1958 to 2024. The SH approach is vital for evaluating mangroves’ ability to provide ES. However, most studies made no reference to these topics, an important gap that must be addressed. We performed a systematic literature review of the Scopus database using the following prompts: Level 1: “mangrove*” and “soil” or “sediment”; Level 2: “mangrove*” and “soil health” or “soil quality”; and Level 3: “mangrove*” and “soil health” or “soil quality” and “ecosystem service*” or “ecologic* service*”. A total of 8289 scientific articles were published that explored mangrove soils or sediments, of which 321 included a discussion of SH, and 39 discussed SH and ES. There is a historical preference for the term “sediment” in marine sciences. Carbon is the most studied topic. Six of the fifteen most productive countries are also among the fifteen with the largest mangrove areas. There is a scientific gap regarding studies that link mangrove soil studies with SH and ES. We recommend the development of a soil health index fully adapted to mangroves, considering their physical and geochemical dynamics, climate conditions, and anthropic relevance.