首页|New Research on Machine Learning from Satbayev University Summarized (Using Pseu do-Color Maps and Machine Learning Methods to Estimate Long-Term Salinity of Soi ls)
New Research on Machine Learning from Satbayev University Summarized (Using Pseu do-Color Maps and Machine Learning Methods to Estimate Long-Term Salinity of Soi ls)
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A new study on artificial intelligence is now available. According to news reporting out of Almaty, Kazakhstan, by New sRx editors, research stated, "Soil salinity assessment methods based on remote sensing data are a common topic of scientific research. However, the developed m ethods, as a rule, estimate relatively small areas of the land surface at certai n moments of the season, tied to the timing of ground surveys." Funders for this research include Committee of Science of The Ministry of Scienc e And Higher Education of The Republic of Kazakhstan. Our news correspondents obtained a quote from the research from Satbayev Univers ity: "Considerable variability of weather conditions and the state of the earth surface makes it difficult to assess the salinity level with the help of remote sensing data and to verify it within a year. At the same time, the assessment of salinity on the basis of multiyear data allows reducing the level of seasonal f luctuations to a considerable extent and revealing the statistically stable char acteristics of cultivated areas of land surface. Such an approach allows, in our opinion, the processes of mapping the salinity of large areas of cultivated lan ds to be automated considerably. The authors propose an approach to assess the s alinization of cultivated and non-cultivated soils of arid zones on the basis of long-term averaged values of vegetation indices and salinity indices. This appr oach allows revealing the consistent relationships between the characteristics o f spectral indices and salinization parameters. Based on this approach, this pap er presents a mapping method including the use of multiyear data and machine lea rning algorithms to classify soil salinity levels in one of the regions of South Kazakhstan."
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