首页|China Agricultural University Reports Findings in Machine Learning (Inversion of soil organic carbon content based on the two-point machine learning method)
China Agricultural University Reports Findings in Machine Learning (Inversion of soil organic carbon content based on the two-point machine learning method)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting from Beijing, People's Republ ic of China, by NewsRx journalists, research stated, "Soil organic carbon (SOC) is vital for the global carbon cycle and environmentally sustainable development . Meanwhile, the fast, convenient remote sensing technology has become one of th e notable means to monitor SOC content." The news correspondents obtained a quote from the research from China Agricultur al University, "Nowadays, limitations are found in the inversion of SOC content with high-precision and complex spatial relationships based on scarce ground sam ple points. It is restrained by the spatial difference in the relationship betwe en SOC content and remote sensing spectra due to the problem of different spectr a for the same substance and the influence of topographic and environment (e.g. vegetation and climate). In this regard, the two-point machine learning (TPML) m ethod, which can overcome above problems and deal with complex spatial heterogen eity of relationships between SOC and remote sensing spectra, was used to invert the SOC content in Hailun County, Heilongjiang Province, combined with derived variables from Sentinel-1, Sentinel-2, topography and environment. Based on 10-f old cross-validation and t-test, results indicated that the TPML method boasts t he highest inversion accuracy, followed by random forest, gradient boosting regr ession tree, partial least squares regression and support vector machine. The av erage r, MAE, RMSE, and RPD of TPML were 0.854, 0.384 %, 0.558 % , and 1.918. Further, the TPML method has been proven to be equal to evaluating the uncertainty of inversion results, by comparing the actual and theoretical er ror of the inversion result in one subset. The spatial inversion result of SOC c ontent with 10 m resolution by TPML is smoother and has more real details than o ther models, which are consistent with the distribution of SOC content in differ ent land use types."
BeijingPeople's Republic of ChinaAsi aCyborgsEmerging TechnologiesMachine LearningRemote Sensing
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