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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]

Nitrogen is a key plant nutrient and its deficiency or surplus could inhibit plant growth and reduce crop yield. Over the past decade, remote sensing has been increasingly used to diagnosis nitrogen deficiency in crop. Taking rice grown in cold region in northeast China as an example, this paper studies the relationship between nitrogen content in japonica rice and the difference between spectral reflectance based on data measured from field. This relationship was used to inversely estimate nitrogen deficiency in the rice based on hyperspectral images. In our analysis, the nitrogen content producing the highest yield was defined as standard nitrogen content and its associated spectral reflectance was defied as standard spectral reflectance. The difference between real nitrogen content and the standard nitrogen content, as well as the difference between the real spectral reflectance the standard spectral reflectance, were calculated respectively. The difference in spectral reflectance was dimensionally reduced using the discrete wavelet multi-scale decomposition, continuous projection method (successive projections algorithm, SPA) and vegetation index construction. The characteristic bands screened by SPA were 459、460、475、 671、723、874 和 996 nm. Analysis showed that when the discrete wavelet multi-scale decomposition was used to reduce the dimension, the Sym8 wavelet mother function worked best when it was decomposed at the seventh layer. Comparing DVI, NDVI and RVI vegetation index found that the determination coefficient of the DVI index and nitrogen deficiency was significantly higher than that of NDVI and RVI index. The three indexes were used as input to the partial least squares (PLSR), the extreme learning machine (ELM) and genetic algorithm optimization extreme learning machine (GA-ELM). The GA-ELM model was most accurate with the R² being 0.7062 for the training set and 0.7594 for the verification set; their associated RMSE was 0.5099mg/g and 0.4276mg/g respectively. The GA-ELM model based on the optimal vegetation index was least accurate, with the R² for the training set and the verifying set being 0.6615 and 0.6509 respectively; their associated RMSE was 0.4415mg/g and 0.5312mg/g. Overall, GA-ELM improved stability and predictability of the model compared with PLSR and ELM. It can thus be used as a new method to detect nitrogen content in rice leaf, and has important implication in precision fertilization. [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]