Remote sensing images accumulate over time, but there are still some missing situations. The existing remote sensing images can be used to reverse the missing remote sensing images, supplement the database and provide information for related research. The characteristics of remote sensing image data are analyzed, and the methods and ideas for deducting remote sensing images are envisaged. The existing algorithms and existing cases are used to study and think about related algorithms, which provides reference and ideas for further research.With the development of aerospace technology and the continuous breakthrough of sensor levels, the acquisition of remote sensing image data has become easier, and the image quality has gradually increased. Remote sensing image data has accumulated over the years. For a research area, there are often references to remote sensing image data from a variety of sensor sources, and the shooting time is usually more than one. These multi-temporal multi-source remote sensing image data have important reference value for the study of natural phenomena, environmental changes, human activities, and topography. However, due to the limitation of shooting, such as the period of the satellite, the influence of the weather, the uncertainty of the aviation platform, etc., there are still omissions in the long-term accumulation of remote sensing image data, and it is difficult to achieve continuous sensing of multiple sensors in a certain area without interruption. There will be a lack of remote sensing data at a certain time in the study area. When analyzing the research area, the remote sensing data corresponding to the determined time is the most important research data, and it can be used to obtain accurate information. The lack of direct remote sensing image data may lead to inaccurate research results or follow-up The processing and analysis are interrupted, and there is an urgent need for a method to make this data available for restoration and retrieval. However, time has passed, and accurate remote sensing data at that time will never be available. Based on the above problems, this paper proposes a concept of remote sensing image data acquisition, which can supplement the database and provide a reference for remote sensing data for scholars who need this information. And think and expand. The main idea is to simulate and deduct some type of remote sensing image data that has not been acquired at certain time points through existing remote sensing image data and other data. At the same time, this paper also proposes some new ideas to solve related problems.

To achieve the rapid identification of Torreya grandis kernels (T. grandis kernels) with different storage times, the near infrared spectra of 300 T. grandis kernels with storage times of 4~9 months were collected. The collected spectral data were modeled, analyzed, and compared using unsupervised and supervised classification methods to determine the optimal rapid identification model for T. grandis kernels with different storage times. The results indicated that principal component analysis (PCA) after derivative processing enabled the visualization of spectral differences and achieved basic detection of samples with different storage times under unsupervised classification. However, it was unable to differentiate samples with storage times of 4~5 and 8~9 months. For supervised classification, the classification accuracy of support vector machine (SVM) modeling was found to be 97.33%. However, it still could not detect the samples with a storage time of 8~9 months. The classification accuracy of linear discriminant analysis after principal component analysis (PCA-DA) was found to be 99.33%, which enabled the detection of T. grandis kernels with different storage times. This research showed that near-infrared spectroscopy technology could be used to achieve the rapid detection of T. grandis kernels with different storage times.

Water content is an important parameter of Torreya grandis (T. grandis) kernels that affects their quality, processing and storage. The traditional drying method for water content determination is time-consuming and laborious. Water content detection based on modern analytical techniques such as spectroscopy is accomplished in a fast, accurate, nondestructive, and sustainable way. The aim of this study was to realize the rapid detection of the water content in T. grandis kernels using near-infrared spectroscopy. The water content of T. grandis kernels was measured by the traditional drying method. Meanwhile, the corresponding near-infrared spectra of these samples were collected. A quantitative water content model of T. grandis kernels was established using the full spectrum after 10 outlier samples were removed by the Mahalanobis distance method and concentration residual analysis. The results showed that the prediction model developed from the partial least squares regression (PLS) method after the spectra were pretreated by the standard normal variate transform (SNV) achieved optimal performance. The correlation coefficient of the calibration set (R2c) and the cross-validation set (R2cv) were 0.9879 and 0.9782, respectively, and the root mean square error of the calibration set (RMSEC) and the root mean square error of the cross-validation set (RMSECV) were 0.0029 and 0.0039, respectively. Thus, near-infrared spectroscopy is feasible for the rapid nondestructive detection of the water content in T. grandis seeds. Detecting the water content of agricultural and forestry products in such an environmentally friendly manner is conducive to the sustainable development of agriculture.

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