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Northern Xinjiang is a significant base of agricultural production in China. Finding out the cropping structure of main crops is important for food security. Accurate acquisition of field crop planting information is the basis of agricultural production and the key to accurate estimation of yield. This paper selected some farmlands of the 8th Division of Xinjiang Production and Construction Corps as the different experimental area and verification area. In August 2017, the CW-20 fixed-wing drone was equipped with a Parrot Sequoia camera, which captured the visible light image. The red, green and blue bands were employed to color space conversion and texture filtering, the texture and structural features of the image were deeply explored, and the automatic extraction of crop planting information was realized. Firstly, color space transformation and gray level co-occurrence matrix texture filtering were used to obtain 27 texture features of hue, saturation, and brightness. Secondly, classification weights and correlation coefficients of 24 texture features and 3 low-pass features were calculated by ReliefF-Pearson feature reduction method. The redundant features with weak classification ability and high correlation were rejected. Hue convolution low pass, hue entropy, intensity correlation, hue homogeneity, intensity convolution low pass, intensity entropy, intensity homogeneity, intensity dissimilarity, saturation convolution low pass, saturation correlation, and intensity variance were the optimal features for visible waveband image classification. Based on the optimal classification features, K-nearest neighbor (KNN), support vector machine (SVM), Naive Bayes (NB), artificial neural network (ANN), and decision tree (C5.0) classification algorithms were used to train model parameters and assess the classification effect. The overall classification of all methods is quite satisfactory, and the classification accuracy rate is over 80%. Among them, the SVM method has the highest classification accuracy, and the accuracy of test area classification is 83.77%, followed by the ANN and KNN methods. Finally, the SVM, KNN, and ANN classification methods were used to perform pixel-level supervised classification of images in the test and verification areas, and the classification effects were evaluated based on visual interpretation maps. The results show that the SVM, KNN, and ANN classification methods are satisfactory in the classification of crop category, and the boundary of the field block is relatively complete, but the classification effect of the non-crop category is unsatisfactory. From the confusion matrix, the SVM method has the highest classification accuracy. Its overall accuracy is 80.74% and Kappa coefficient is 0.75. The classification accuracies of cotton, corn, marrow, and alfalfa crops are 82.78%, 85.49%, 92.65% and 80.84%, respectively, while that of the trees and other categories are only 64.58% and 59.28%, respectively. The overall accuracies of KNN and ANN methods are above 74%. In summary, based on the optimal color-texture features selected by ReliefF-Pearson, the SVM classification method is the most stable and reliable in the crop category classification and can be utilized to classify the main crops in northern Xinjiang. However, all classification methods are built on the pixel level. There are some differences in the canopy structure of the same crop, and some internal areas may be misclassified as others. Using object-oriented segmentation can improve the classification accuracy. In addition, there are differences in the growth period of different kinds of crops, and the difference in the optimal identification time differs greatly in the accuracy of each category. Using crop phenology information combined with the time-series images can get the expected classification effect. The method of this paper has a certain applicability for the UAV (unmanned aerial vehicle) visible waveband images to obtain planting information of the main crops in northern Xinjiang and provides a methodological reference for a large-scale crop planting information survey. [ABSTRACT FROM AUTHOR]

Temperature and humidity prediction plays an important role in various fields of the national economy. In view of the poor predictions of temperature and humidity and the failure to achieve multimodal prediction, this paper uses the Gaussian mixture process (GPM) model to predict the multimodal temperature and humidity. GPM model was developed from the Gaussian process (GP) model. It was proposed by Tresp in 2000. The main idea of GPM is to fit the samples in various input regions with different GP models in order to fit different patterns in these regions. However, the existing GPM models are rather complicated and thus the learning algorithms involve various approximation schemes, which can lead to much computation or rough prediction. So, in order to improve the model learning efficiency, a variable-hidden posterior hard-cut iterative training algorithm is proposed for the GPM model. This algorithm improves the EM (expectation maximization) learning with a new approximation strategy, which achieves the optimal grouping of samples through iterative learning and using the maximum a posteriori estimation. Prior to the experiment, the autocorrelation function and partial autocorrelation function were used to verify the nonlinear and non-stationary property of humidity and temperature sequences. Largest Lyapunov, saturation correlation dimension and recursive graph were used to verify the chaotic characteristic of humidity and temperature sequences. Finally, the GPM proposed in this paper is compared with the kernel regression (K-R), the minimax probability machine regression (MPMR), the linear regression (L-R), and GP models. K-R is a kind of prediction model based on kernel function. By adjusting the optimal window width, K-R gradually gets the corresponding prediction result. K-R has been the basis of predictive model because it can predict the linear and non-linear time series simultaneously. MPMR does not need to make specific assumptions about the model distribution. It only needs to know the mean and covariance matrix of the data distribution. This model improves the neural network easy to fall into the minimum and over-fitting. L-R is a very traditional model, using the quantitative relationship between variables to obtain a linear expression and then predict the test sample. The results show that not only GPM can perform multimodal prediction, but also the accuracy of the model is significantly better than other traditional models. In the humidity sequence prediction, the minimum of RMSE can reach 0.0620 and the maximum of R2 can reach 0.9362. In the temperature sequence prediction, the minimum of RMSE can reach 0.0426 and the maximum of R2 can reach 0.9666. In the prediction time consumed, GPM is a little more than the other 4 traditional models. The main reason is that GPM requires iterative learning to achieve the optimal grouping of samples. Finally, humidity prediction needs 113.4175 s and temperature prediction needs 90.0049 s. The research in this paper not only can promote atmospheric temperature and humidity forecasting, but also has some reference value for indoor and solid surface temperature and humidity forecasting because GPM is the prediction model without environment factors input. [ABSTRACT FROM AUTHOR]

The rapid acquisition of soil water content (SWC) in field crop root zone is significant for drought supervision and precision irrigation. The UAV multispectral remote sensing system has the advantages of obtaining high spatial-temporal resolution of crop phenotype data, and has a wide application prospect in soil moisture monitoring. In order to obtain SWC accurately and timely at a farm scale, in this paper, the field maize with different water treatments is taken as the research object, and the multispectral remote sensing monitoring of summer maize is carried out by using the UAV remote sensing platform, and the soil water content of different soil depth in maize root zone is collected synchronously. Based on the UAV multispectral remote sensing image data sets of jointing stage, tasseling-silking stage and milky-maturity stage of summer maize in 2018, the soil background is removed by support vector machine classification, the spectral reflection of maize canopy is extracted, and the 10 vegetation indices are calculated, then the sensitivity analysis of soil water content in different depth is carried out by using full subset screening method for different wave bands and vegetation indices, and the soil water content in different depth is analyzed respectively, ridge regression and extreme learning machine are used to construct quantitative estimation models of soil water content at 0-20, 20-45 and 45-60 cm soil depth after full subset selection. The test area is located in Zhaojun Town, Dalate Banner, Ordos, Inner Mongolia, China(40°26'0.29" N, 109°36'25.99" E, elevation 1 010 m). The sowing time of maize is on May 11, 2018, the emergence time is on May 18, and the harvest time is on September 8, 2018. The total growth period is 114 days. The UAV multispectral remote sensing images and ground data collection dates are July 8, July 12, July 17, July 21, July 26, August 2, August 28 and September 7, 2018. It is collected once a day and tested 8 times in the whole growth period. July 8-21 is the jointing stage, July 26-August 2 is the tasseling-silking stage, August 28-september 7 is the milk-maturity stage. The flight altitude of the UAV is 70 m, and the flight time is 11:00-13:00 local time (11:44-13:44 Beijing time). Firstly, the UAV multispectral canopy images of field maize with 5 different irrigation treatments (TRTs) are acquired through the six-rotor UAV equipped with a RedEdge multispectral camera ( MicaSense, USA), and the multispectral images of diffuse reflector (reflectivity 58%, size 3×3 m) are collected at the same height to perform radiometric correction in the meantime, and then the spectral reflectances of the field maize are acquired. Secondly, the support vector machine (SVM) is used to eliminate the multispectral image of soil background in ENVI and ArcGIS software, then the maize canopy spectral reflectance is extracted and 10 vegetation indices (VIs), such as Normalized Difference Vegetation Index (NDVI), Normalized Green Difference Vegetation Index (GNDVI) and Transformed Chlorophyll Absorption In Reflectance Index(TCARI), etc, are calculated. Finally, the full subset selection method based on Bayesian Information Criterion (BIC) is used to analyze the sensitivity of SWC at different depths for different spectra bands and vegetation indices in R3.5.1 software, and then Ridge Regression (RR) as well as Extreme Learning Machine (ELM) are used to construct a quantitative estimation model of SWC at soil depths of 0-20, 20-45 and 45-60 cm at different growth stages, respectively. The results show that the full subset selection method based on BIC can effectively select the optimal spectral subset, and the selected variables generally pass the significance test and the independent variables number is small; the effects of the ELM model outperformed the RR model almost under all the same conditions; the optimal monitoring soil depth of maize at jointing stage, tasseling-silking stage is 0-20 cm, and the optimal monitoring soil depth of milk-maturity stage is 20-45 cm; the ELM inversion model at 20-45 cm soil depth at milk-maturity stage has the best effect, the decision coefficients of modeling set and verification set are 0.825 and 0.750, respectively, the root mean square error are 1.00% and 1.32%, respectively, and the normalized root mean square error are 10.85% and 13.55%, respectively. The combination of full subset selection method and machine learning can improve the inversion accuracy and robustness of SWC. This study provides a new way for rapid and accurate monitoring of SWC in farmland and precise irrigation. [ABSTRACT FROM AUTHOR]

Estimating soil salinity is imperative for scheduling irrigation and remediating saline soil but difficult at large scales. Remote sensing can bridge this gap because of its advantages in low cost and large-area coverage; it has become an efficient method for assessing soil salinization in field. One issue in use of remote sensing to assess saline soil is the presence of plastic film mulch and bare soil because of their difference in reflecting waves in the spectral bands. In order to investigate the effect of plastic film mulch on soil salinity inversion using UAV multispectral remote sensing, we studied four plots with plastic film mulch at the Shahaoqu Irrigation area in the Hetao Irrigation District, Inner Mongolia of China. From each plot, we took soil samples and measured their salt contents from May to July. We also flew a drone to simultaneously take multispectral images of the sampling sites and extracted the spectral reflectance to calculate the spectral indices. Correlation analysis found that the S4, S6, SI1, SI2, SI3 and BI indices can be used to calculate soil salinity. Six-band spectral reflectances and six spectral indices obtained from different datasets were used as independent variables to calculate the salt content with the support vector machine (SVM), the back propagation neural network (BPNN) and the extreme learning machine (ELM), respectively, before and after the mulch film was removed. We compared the three models based on their determination coefficient (R²), root mean squared error (RMSE) and relative error (RE). The results showed that plastic film mulch did impact on soil salinity inversion. Although all three models could adequately estimate the soil salt contents before and after the film removal, they worked better after the film removal than before the film removal. Models based on the spectral indices were more accurate than those based on the spectral reflectances, and the accuracy of the inversely calculated salt content varied with sampling time and treatment. The inversion results based on monthly data differed from those based on by pooling all data. After the film was removed in June, the salt content estimated using the model was most accurate, with its associated R² and RMSE being 0.695 and 0.182 respectively for the spectral reflectance-based method, and 0.663 and 0.191respectively for the spectral indices-based method. The salt content estimated by BPNN was least accurate in May, with its associated R² and RMSE being 0.766 and 0.161 respectively for the spectral reflectance-based method, and 0.769, 0.162 respectively for the spectral indices-based method. Comparison of the three models revealed that ELM was most accurate, followed by SVM and BPNN, although their errors were within the tolerable range. In summary, this paper provides an effective method to inversely calculate soil salinization at large mulched farmland using UAV multispectral remote sensing. [ABSTRACT FROM AUTHOR]

Soil salinization is the main problem of land degradation in the Yellow River Delta of China. Remote sensing technology can gain soil salinization information quickly and accurately, which is of great significance to the sustainable development of agriculture. In this paper, a typical salinization area in Kenli County of the Yellow River Delta was chosen as the study area. In order to retrieve soil salinity from hyperspectral imagery with high accuracy, image fusion and machine learning were used in this study. HSI (hyperspectral imaging radiometer) hyperspectral imagery of HJ-1A satellite of China and OLI (operational oand imager) multispectral imagery of Landsat 8 of USA (United States of America) were preprocessed, including radiometric calibration, atmospheric correction and image registration. After that, the 2 kinds of images were fused with the hyperspherical color space resolution merge algorithm. This algorithm was designed for 8‑band data of Worldview‑2 sensor, and it works with any multispectral data containing 3 bands or more. The fused image has 30 m spatial resolution and 4.32 nm spectral resolution, in which saline soil can be identified better than that in the original image. The feature bands were selected according to spectral analysis of different levels of saline soil and the PLSR (partial least squares regression) regression coefficients between soil salinity and imagery bands. Two types of models, i.e. statistical model and machine learning model, were built. The statistical model includes multi linear regression model and PLSR model, while the machine learning model includes BP (back propagation) neural network model, support vector machine (SVM) model and random forest (RF) model. These models were built with soil salinity data as retrieval target and feature bands of images as input variables. In this process, natural logarithm function was adopted for soil salinity data to obey the normal distribution. The research gained the following results. Firstly, the retrieval model based on fused images is overall better than HSI images, and the latter is better than OLI multispectral images, which shows that both spatial and spectral resolution have important effects on the retrieval results. The retrieval accuracy of fused image is obviously better than that of HSI and OLI images. The main reason is that the fused image not only has both high spatial and spectral resolution but also has fewer mixed pixels. Secondly, with regard to performance of the models, the machine learning models are superior to the classic statistical models. This is because classic statistics often require sufficient samples, while machine learning is designed for small sample data and has more advantages over retrieval problems. In general, BP neural network model is better than SVM model and RF model. For the retrieval of fused images, the correlation coefficients of the 3 models are all higher than 0.82, and thus all of them achieve desirable results. Thirdly, the results also indicate that the accuracy of the models can be improved to some extent by proper preprocessing of the data, such as natural logarithm function which can let the data obey normal distribution. Either the classic statistical analysis method or the new machine learning method is based on the training data to explore the relationship between the retrieval target and the input variables. We then conclude that: 1) Despite the differences both in time and in wavelength between OLI and HSI images, image fusion can significantly improve the accuracy of remote sensing retrieval of soil salinity; 2) Machine learning model is better than traditional statistical model for soil salinization retrieval; 3) The main factors that affect the retrieval accuracy in our study include the number of measured samples, the quality of remote sensing data, the data preprocessing, fusion and modeling methods, and so on. Therefore, this study provides useful results and has positive theoretical and practical significance to the soil salinity retrieval in the typical area of the Yellow River Delta with remote sensing method. [ABSTRACT FROM AUTHOR]