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DETECTION OF ORGANIC CARBON CONTENT IN THE SOILS OF NORTHERN KAZAKHSTAN BASED ON REMOTE SENSING DATA
Abstract
This study explores the possibility of creating detailed maps of organic carbon content in the arable lands of northern Kazakhstan. The technology is based on filtering satellite images with convolutional neural networks and calculating the coefficients of the multi-temporal soil line using remote sensing data with a resolution of 30 meters. Calculating the coefficient of the multi-temporal soil line allowed us to create a map of the bare soil surface, highlighting areas of soil cover heterogeneity. The resulting bare soil surface map was used to plan a ground survey route, during which the condition of the soil, humus horizon, and humus content in the arable layer were assessed. The main soil types in the area were identified as southern and ordinary chernozems. Based on the results, we were able to spectrally distinguish areas with different humus content. Ultimately, we created a detailed map of organic carbon content in the soil for the study area. Understanding the humus content in soil has high practical significance, as humus content is often considered an integral indicator of soil fertility. The maps enable the creation of precise prescriptions maps for fertilizer application, ensuring that agricultural inputs are used efficiently and effectively.
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