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Copyright of World Nuclear Geoscience is the property of World Nuclear Geoscience Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of World Nuclear Geoscience is the property of World Nuclear Geoscience Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Osteoporosis is the property of Chinese Journal of Osteoporosis and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Geomechanics is the property of Journal of Geomechanics Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of World Nuclear Geoscience is the property of World Nuclear Geoscience Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to improve the efficiency and accuracy of the quantitative prediction of soil nutrient content in black soil of Heilongjiang province, in this paper, we utilized statistical theory and spectral analysis method, researched the relationship of three kinds of soil nutrient content and soil spectrum to established hyperspectral inversion model of soil total nitrogen, available phosphorus, available kalium content. We acquired the aerial hyperspectral data by using CASI-1500 and SASI-600 linear array push-broom imaging spectrometers. Preprocessing of calibration and atmospheric radiation correction of Airborne Hyperspectral raw radiation data was studied. 96 samples were evenly sampled. In order to increase the representativeness of samples, 96 groups of samples were collected from 3-5 samples collected from 15 meters around the sampling point, and 1.5 kg was retained after mixing. After air-drying, mixing and grindingetc, it is used for the contents of total nitrogen, available phosphorus and available kalium were obtained through laboratory tests. The content of total nitrogen, available phosphorus and available kalium was determined by NaOH diffusion method, NaHCO3 extraction-molybdenum blue colorimetry and NH4OAC extraction-flame photometry. Referring to Kennard-Stone method, 72 groups of representative samples were selected as model samples for nutrient content prediction, and 24 groups were model prediction samples. 96 black soil samples were sorted according to nutrient content, and the spectral transformation in the visible near red range was analyzed. The change rule of total nitrogen is that the reflectance decreases with the increase of content. The first order differential spectra at 580 nm were significantly correlated with total nitrogen and available phosphorus content, with a correlation coefficient of -0.43 and -0.36, respectively. The first-order differential spectra at 1730-2200 nm were significantly correlated with K2O, and the maximum correlation coefficient was -0.31. Compared with the original spectral waveform, the correlation coefficient between the first derivative and three nutrient contents fluctuated sharply, and the positive and negative cross-sections were relatively sharp, with more peak coefficients. After spectral contrast analysis and correlation coefficient calculation, 86 bands with higher correlation coefficient were selected for the study under the first order differential variation. On black soil airborne hyperspectral data processing, the application of partial least squares regression (PLSR) and BP neural network method respectively establish soil nutrient content of high spectral quantitative inversion model. The results showed that RPIQ values (Difference between the third and the first quartile of sample observations ratio to RMSE) of total nitrogen PLSR and BP neural network prediction model were 2.42 and 2.80, respectively. The RPIQ values of effective phosphorus PLSR and BP neural network model were 0.83 and 1.67 respectively. The RPIQ values of the available kalium PLSR and BP neural network models were 2.00 and 2.33 respectively. Experiments showed that the spectral quantitative prediction model of soil total nitrogen and available kalium has good accuracy and prediction ability. Nitrogen, phosphorus and potassium, and the spatial distribution of nutrient content in black soil were obtained. However, the prediction effect of effective Phosphorus was not particularly ideal, which could only meet the requirements of approximate quantitative prediction. At the same time, the BP neural network modeling has better accuracy and prediction ability than the partial least square modeling, and the prediction accuracy increased by 6.5%, 10.1% and 6.6% respectively. Due to the limitation of soil samples and other conditions, more samples are needed to verify the universality of the model. More data mining methods are expected to establish more robust prediction models, which will provide more reliable information for the prediction and evaluation of black soil quality information. [ABSTRACT FROM AUTHOR]

In order to improve the precision and stability of the soil nutrient content inversion model in black soil area, taking Jiansanjiang area in Heilongjiang province as the study area, and the airborne hyperspectral imaging system CASI-1500 (380-1 050 nm) as the analysis data, the influence of different spectral transformation methods on the accuracy was researched. 60 samples were evenly sampled, and the contents of organic matter, total nitrogen, total phosphorus and total potassium were obtained through laboratory tests. The content of organic matter was determined by potassium dichromate capacity external heating method. The content of total nitrogen, total phosphorus and total potassium was determined by Kjeldahl method, NaOH alkali antimony colorimetric method and potassium flame atomic absorption spectrophotometry. 60 black soil samples were sorted according to nutrient content, and the spectral transformation in the visible near red range was analyzed. The change rule of organic matter is that the reflectance decreases with the increase of content. The change rule of nitrogen is similar to the spectral curve of organic matter. With the increase of nitrogen content, the reflectance decreases. The transformation of phosphorus and potassium in the visible near red spectrum is not significant. The nutrient correlation coefficients of 60 samples at different sampling points were calculated by spectral reflectance. The results show that the correlation coefficient of each band is the highest, the mean value is 0.39, the correlation coefficients of nitrogen and phosphorus are close to 0.28 and 0.30, and the correlation coefficient of potassium is the lowest, which is 0.05. The first 5 bands with high correlation coefficient are selected as modeling bands, that of organic matter is 933.6, 914.5, 905, 866.8 and 943.1 nm, and that of nitrogen is 933.6, 866.8, 876.3, 847.7 and 914.5 nm. The content of organic matter and support vector machine were used to model nitrogen, phosphorus and potassium contents. The extraction accuracies of 5 spectral transformations which are resampling (RE), logarithmic reciprocal (LR), first order derivative (FD), continuum removal (CR) and multivariate scatter correction (MSC) transformation are compared. The most accurate methods for the spectral transformation of organic matter, nitrogen, phosphorus and potassium are MSC, MSC, LR and RE, respectively. Five spectral transformation methods are used to calculate the R² of each model, and the order of modeling accuracy for soil organic matter prediction is MSC (0.922) > RE (0.529) > LR (0.432) > CR (0.414) > FD (0.018). The modeling accuracy of multiple scattering correction transformation is significantly higher than that of the other four methods. The order of prediction accuracy or total phosphorus is MSC (0.872) > CR (0.387) > RE (0.256) > LR (0.029) > FD (0.012), and the prediction accuracy of the multivariate scattering correction transformation is also the highest. The highest prediction accuracies of total phosphorus and total potassium are LR (0.621) and RE (0.423). In turn, the MSC, MSC, LR and RE spectral transformation methods with high coefficient of determination are applied to the combined operation of the characteristics of organic matter, nitrogen, phosphorus and potassium, and the spatial distribution of nutrient content in black soil is obtained. The results show that the spectral transformation methods of MSC, MSC, LR and RE are applied to calculate soil organic matter, nitrogen, phosphorus and potassium, respectively, the spatial distribution accuracy of nutrient content in black soil is the highest, and the determination coefficients of predicted samples are 0.748, 0.673, 0.631 and 0.420, respectively. [ABSTRACT FROM AUTHOR]

In order to improve the precision and stability of the soil nutrient content inversion model in black soil area, taking Jiansanjiang area in Heilongjiang province as the study area, and the airborne hyperspectral imaging system CASI-1500 (380-1 050 nm) as the analysis data, the influence of different spectral transformation methods on the accuracy was researched. 60 samples were evenly sampled, and the contents of organic matter, total nitrogen, total phosphorus and total potassium were obtained through laboratory tests. The content of organic matter was determined by potassium dichromate capacity external heating method. The content of total nitrogen, total phosphorus and total potassium was determined by Kjeldahl method, NaOH alkali antimony colorimetric method and potassium flame atomic absorption spectrophotometry. 60 black soil samples were sorted according to nutrient content, and the spectral transformation in the visible near red range was analyzed. The change rule of organic matter is that the reflectance decreases with the increase of content. The change rule of nitrogen is similar to the spectral curve of organic matter. With the increase of nitrogen content, the reflectance decreases. The transformation of phosphorus and potassium in the visible near red spectrum is not significant. The nutrient correlation coefficients of 60 samples at different sampling points were calculated by spectral reflectance. The results show that the correlation coefficient of each band is the highest, the mean value is 0.39, the correlation coefficients of nitrogen and phosphorus are close to 0.28 and 0.30, and the correlation coefficient of potassium is the lowest, which is 0.05. The first 5 bands with high correlation coefficient are selected as modeling bands, that of organic matter is 933.6, 914.5, 905, 866.8 and 943.1 nm, and that of nitrogen is 933.6, 866.8, 876.3, 847.7 and 914.5 nm. The content of organic matter and support vector machine were used to model nitrogen, phosphorus and potassium contents. The extraction accuracies of 5 spectral transformations which are resampling (RE), logarithmic reciprocal (LR), first order derivative (FD), continuum removal (CR) and multivariate scatter correction (MSC) transformation are compared. The most accurate methods for the spectral transformation of organic matter, nitrogen, phosphorus and potassium are MSC, MSC, LR and RE, respectively. Five spectral transformation methods are used to calculate the R2 of each model, and the order of modeling accuracy for soil organic matter prediction is MSC (0.922) > RE (0.529) > LR (0.432) > CR (0.414) > FD (0.018). The modeling accuracy of multiple scattering correction transformation is significantly higher than that of the other four methods. The order of prediction accuracy or total phosphorus is MSC (0.872) > CR (0.387) > RE (0.256) > LR (0.029) > FD (0.012), and the prediction accuracy of the multivariate scattering correction transformation is also the highest. The highest prediction accuracies of total phosphorus and total potassium are LR (0.621) and RE (0.423). In turn, the MSC, MSC, LR and RE spectral transformation methods with high coefficient of determination are applied to the combined operation of the characteristics of organic matter, nitrogen, phosphorus and potassium, and the spatial distribution of nutrient content in black soil is obtained. The results show that the spectral transformation methods of MSC, MSC, LR and RE are applied to calculate soil organic matter, nitrogen, phosphorus and potassium, respectively, the spatial distribution accuracy of nutrient content in black soil is the highest, and the determination coefficients of predicted samples are 0.748, 0.673, 0.631 and 0.420, respectively. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)