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The objective of the present study was to evaluate the efficiency of soil magnetic parameters for assessment of soil drainage classes in Juneqan district, Charmahal and Bakhtiari province, western Iran. Four soil drainage classes including well drained (WD), moderately well drained (MWD), intermittent poor drained (IPD) and poorly drainage (PD) were selected. A total of 89 soil pedons were described and soil samples were collected within the moisture control section. Magnetic susceptibility (MS) at high (χhf) and low (χlf) frequencies and frequency-dependent MS (χfd) were evaluated in the laboratory. Poorly crystalline iron (Feo) and pedogenic iron (Fed) values of all soil samples were also measured. The results revealed that among the four drainage classes, PD class showed the lowest χlf and χhf values, greatest of Feo and Feo/Fed, lowest contents of Fed, as well as the highest average increase of χlf on heating (at 500 °C). However, all mentioned features showed an inverse trend in the WD class as compared to PD. The results of discriminant analysis demonstrated that magnetic measures could prosperously discriminate between the selected drainage classes in this study area (average accuracy = 83.1%). Therefore, it can be concluded that MS technique could be used as a powerful, nondestructive and fast technique for separation of soil drainage classes in the present case.

Soil loss is a major cause of land degradation worldwide, especially in large areas of arid and semi-arid regions. With advent of new software and technologies such as remote sensing (RS) and GIS, there is a necessity to integrate them to achieve important information in a faster manner. The aims of present study were to evaluate soil erodibility (K-factor) using standard plots under natural rainfall and prediction of soil loss by integrating RUSLE, GIS and RS in Fars Iran. The RUSLE factors were evaluated as following: the R-factor was calculated using modified Fournier index; K-factor was measured in the field using erosion plots and estimated by the USLE equation; the C-factor map was created using the NDVI; the LS-factor map was generated from digital elevation model with 10 m resolution, and the P-factor map was assumed as 1. Spatial distribution of annual soil loss in the Simakan watershed was obtained by multiplying these factors in GIS. The average of the measured K was 0.014 th MJ− 1 mm− 1 and 2.08 times less than the average of the estimated K (0.030 th MJ− 1 mm− 1). The performance of RUSLE was highly influenced by the K, because the annual soil loss predicted using estimated K (11.0 th− 1 ya− 1) was about twice as much as the measured K (5.7 th− 1 ya− 1). The spatial distribution of soil loss classes predicted was: 73.64% very low, 14.79% low, 10.19% moderate and 1.25% severe. Areas of severe soil loss are situated in the northern portion of the study area, which needs suitable conservation practices.

Determinations of soil carbon and nitrogen stocks are important to evaluate land fertility and agricultural potential and because of their influence on the global climate. Spectroscopic determinations are faster, cheaper and less pollutant than traditional methods. The potential use of spectroscopy in the visible (Vis), near infrared (NIR) and mid-infrared (MIR) regions and their combination to estimate total C and N concentrations were evaluated using seven different pre-processing and two regression models and comparing to the concentrations determined by dry combustion of 701 soil samples from different soil classes and land uses in Northeast Brazil. Better C and N concentration predictions were obtained with the MIR region than with the Vis-NIR region and no significant improvement occurred when the two spectra were combined. The support vector machine (SVM) and the partial least squares (PLSR) models had similar performances both for C and N. The multiplicative scatter correction pre-processing is recommended for C and the standard normal transformation technique for N. Equations to estimate soil C and N concentrations of the predominant soil classes in the region and of the set of all classes are provided. Their high accuracy confirm the potential of reflectance spectroscopy as a useful and rapid tool to quantify C and N concentrations in different tropical soils and under different land uses.

The land surface temperature (LST) provides important information about energy exchange processes, which are influenced by land use/land cover (LULC). Thus, our objective was to evaluate LST patterns driven by LULC changes, detected over a time series of Landsat images. The study area of 2990 km2 is located in the Piracicaba region, state of Sao Paulo, Brazil. We acquired Landsat images from 1985 to 2019, in dry and moist seasons. Six LULC classes (agriculture, bare soil, straw, forest, water, and pasture) were identified by maximum-likelihood supervised classification every five years and then LST was estimated using the inversion of Planck’s function in the thermal band. Spectral indices representing vegetation, water, bare soil, and straw were calculated and correlated to LST in specific years. Bare soil images and their respective LST in both seasons were used annually to approach the influence of bare soil areas on the LST, considering soil class, time and rainfall. LULC alterations over 1985–2015 were an important factor on the LST change, which varied on average from 21.46 °C to 41.31 °C in the moist season and 17.05 °C to 31.67 °C in the dry one. Water bodies and vegetation had the lowest LST values, whereas bare soil and straw had the highest ones. The correlation between LST and spectral indices somewhat agreed with such patterns. Arenosols presented the highest LST mean values in both seasons and differed from Acrisols in the dry season, which is probably related to their texture and mineralogical composition. In the moist season, LST was negatively correlated to rainfall, suggesting the influence of soil moisture content on its surface temperature. In the dry season, the LST of bare soil areas increased by an average of 0.13 °C per year, indicating a warming trend. In general, LST increased in the studied period, probably due to the increase of anthropic activity, such as the expansion of agricultural areas. These findings can assist future studies on the influence of soils and land use on climate alterations.

Quantification of soil organic carbon (SOC) is a low-cost and necessary practice to meet increasing agricultural demands. Studies show that remote sensing (RS) is important for SOC prediction and its use has become crucial in agricultural management. In this study, a Multiple Linear Regression (MLR) model was constructed to predict SOC in a site in Piracicaba, Sao Paulo, Brazil. As predictor variables, we used the optical-satellite data of OLI/Landsat-8 sensor (bands 5 and 7, specifically), clay concentration, and the Normalized Difference Vegetation Index (NDVI). We collected 218 samples at the sampling points in the field to quantify clay and SOC in the laboratory as a calibration procedure. An Exposed Soil Mask (ESM) was created using the method GEOS3 technology, which showed pixels with greater variability of bare soil. The pixels were evaluated with their respective surface reflectance values obtained by the satellite sensor and their respective NDVI index values. We evaluated the model predictive performance based on the adjusted coefficient of determination (R2), the Root Mean-Squared Error (RMSE), and the Ratio of Performance to Interquartile Range (RPIQ) obtained in data validation. The MLR model presented R2 values 0.79 and 0.81 for calibration and validation, respectively. We obtained important RMSE and RPIQ values, 0.14 and 2.32, respectively. The high RPIQ indicated significative sampling distribution around the trendline. After construction, the model was applied to the C spatial distribution using the predictive variables as layers, predominant concentrations of 0.65 to 0.79 g. Kg−1 in 51 (23.4%) soil samples. The analysis presented here offer possibilities for SOC prediction using Geographic Information Systems (GIS) tools.