Li Yanling, Zhao Gengxing, Chang Chunyan, Wang Zhuoran, Wang Ling, Zheng Jiarong. Soil salinity retrieval model based on OLI and HSI image fusion[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2017, 33(21): 173-180. DOI: 10.11975/j.issn.1002-6819.2017.21.020
    Citation: Li Yanling, Zhao Gengxing, Chang Chunyan, Wang Zhuoran, Wang Ling, Zheng Jiarong. Soil salinity retrieval model based on OLI and HSI image fusion[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2017, 33(21): 173-180. DOI: 10.11975/j.issn.1002-6819.2017.21.020

    Soil salinity retrieval model based on OLI and HSI image fusion

    • Abstract: 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.
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