Prediction of black soil nutrient content based on airborne hyperspectral remote sensing

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 1 730-2 200 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.