Accurate and detailed spatial soil information is essential for environmental modelling, risk assessment and decision making. The use of Remote Sensing data as secondary sources of information in digital soil mapping has been found to be cost effective and less time consuming compared to traditional soil mapping approaches. But the potentials of Remote Sensing data in improving knowledge of local scale soil information in West Africa have not been fully explored. This study investigated the use of high spatial resolution satellite data (RapidEye and Landsat), terrain/climatic data and laboratory analysed soil samples to map the spatial distribution of six soil properties–sand, silt, clay, cation exchange capacity (CEC), soil organic carbon (SOC) and nitrogen–in a 580 km2 agricultural watershed in south-western Burkina Faso. Four statistical prediction models–multiple linear regression (MLR), random forest regression (RFR), support vector machine (SVM), stochastic gradient boosting (SGB)–were tested and compared. Internal validation was conducted by cross validation while the predictions were validated against an independent set of soil samples considering the modelling area and an extrapolation area. Model performance statistics revealed that the machine learning techniques performed marginally better than the MLR, with the RFR providing in most cases the highest accuracy. The inability of MLR to handle non-linear relationships between dependent and independent variables was found to be a limitation in accurately predicting soil properties at unsampled locations. Satellite data acquired during ploughing or early crop development stages (e.g. May, June) were found to be the most important spectral predictors while elevation, temperature and precipitation came up as prominent terrain/climatic variables in predicting soil properties. The results further showed that shortwave infrared and near infrared channels of Landsat8 as well as soil specific indices of redness, coloration and saturation were prominent predictors in digital soil mapping. Considering the increased availability of freely available Remote Sensing data (e.g. Landsat, SRTM, Sentinels), soil information at local and regional scales in data poor regions such as West Africa can be improved with relatively little financial and human resources.

/pdf/high-resolution-mapping-of-soil-properties-using-remote-2tbn5zrniy.pdf

High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models.

Status of the World’s Soil Resources Main report

High resolution mapping of soil organic carbon stocks using remote sensing variables in the semi-arid rangelands of eastern Australia

High-resolution digital mapping of soil organic carbon and soil total nitrogen using DEM derivatives, Sentinel-1 and Sentinel-2 data based on machine learning algorithms.

Estimating soil organic carbon stocks using different modelling techniques in the semi-arid rangelands of eastern Australia

Research both nationally and internationally has indicated that no-till (NT) management used in combination with stubble retention has the potential to increase soil organic carbon (SOC) stocks in cropping soils relative to conventional tillage (CT) However, rates of SOC increase can vary depending on cropping system, climate, and soil type, making the quantification of carbon change difficult on a regional level Various long-term trials and commercial sites throughout Queensland were used to compare rates of SOC change under CT and NT management in cropping soils, and to determine how climate and soil type interact to influence rates of change It was observed that NT management was not capable of increasing SOC stocks under the crop–fallow rotation systems practised throughout Queensland, and was unlikely even to hold SOC stocks steady under current management practices However, SOC losses under NT systems did appear to be slower than under CT, indicating that NT may slow SOC loss following a period of organic carbon input, for example, from a pasture ley On a regional scale, biomass production (estimated through remote sensing), climate (specifically the vapour pressure deficit), and soil sand content could be used to adequately predict SOC stocks on commercial sites, indicating the importance of considering these factors when assessing SOC stocks following management change across the region

Organic carbon stocks in cropping soils of Queensland, Australia, as affected by tillage management, climate, and soil characteristics

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Organic carbon stocks in cropping soils of Queensland, Australia, as affected by tillage management, climate, and soil characteristics