During the processing and planting of soybeans, it is greatly significant that a reliable, rapid, and accurate technique is used to detect soybean varieties. Traditional chemical analysis methods of soybean variety sampling (e.g., mass spectrometry and high-performance liquid chromatography) are destructive and time-consuming. In this paper, a robust and accurate method for nondestructive soybean classification is developed through hyperspectral imaging and ensemble machine learning algorithms. Image acquisition, preprocessing, and feature selection are used to obtain different types of soybean hyperspectral features. Based on these features, one of ensemble classifiers-random subspace linear discriminant (RSLD) algorithm is used to classify soybean seeds. Compared with the linear discrimination (LD) and linear support vector machine (LSVM) methods, the results show that the RSLD algorithm in this paper is more stable and reliable. In classifying soybeans in 10, 15, 20, and 25 categories, the RSLD method achieves the highest classification accuracy. When 155 features are used to classify 15 types of soybeans, the classification accuracy of the RSLD method reaches 99.2%, while the classification accuracies of the LD and LSVM methods are only 98.6% and 69.7%, respectively. Therefore, the ensemble classification algorithm RSLD can maintain high classification accuracy when different types and different classification features are used.

Snow cover plays an important role in climate, hydrology, and ecosystem. At present, passive microwave remote sensing is the most effective method for monitoring global and regional snow depth (SD). The traditional SD inversion algorithms use empirical or semiempirical methods to establish a fixed relationship between the SD and brightness temperature difference, given snow particle size and snow density. However, the snow characteristics present large temporal heterogeneity in Northeast China, and it leads to the inadaptability of the SD retrieval algorithm; using a fixed empirical coefficient will lead to large errors in SD inversion. In this study, a novel dynamic method was proposed to retrieve SD based on AMSR2 brightness temperature data. A snow survey experiment was designed to collect snow characteristics in different periods in Northeast China, and the microwave emission model of layered snowpacks was applied to simulate brightness temperature with varying snow characteristics to determine the dynamic coefficients in the SD retrieval algorithm. The validation results at 98 meteorological stations demonstrate that the novel dynamic SD inversion algorithm achieved better stability in the long-term sequence, its RMSE, bias, and R are 7.79 cm, 1.07 cm, and 0.61, respectively. Furthermore, compared with Che SD products, Chang algorithm, and AMSR2 SD products, the novel algorithm can obtain specific dynamic coefficients considering the snow metamorphism and has a higher accuracy of SD inversion in the whole winter. In conclusion, this novel SD inversion algorithm is more applicable and accurate than the existing SD inversion products in Northeast China.

Snow cover plays a highly critical role in the global water cycle and energy exchange. Accurate snow depth (SD) data are important for research on hydrologic processes, climate change, and natural disaster prediction. However, existing passive microwave (PMW) SD products have high uncertainty in Northeast China owing to their coarse spatial resolution. Surface environment parameters should also be considered to reduce errors in existing SD products. Otherwise, it is difficult to accurately capture snow spatiotemporal variations, especially in a complex environment (e.g., mountain or forests areas). To improve the inversion accuracy and spatial resolution of existing SD products in Northeast China, a multifactor SD downscaling model was developed by combining PMW SD data from the AMSR2 sensor, optical snow cover extent data, and surface environmental parameters to produce fine scale (500 m × 500 m) and high precision SD data. Validations at 98 ground meteorological stations show that the developed model greatly improved the spatial resolution and inversion accuracy of the raw AMSR2 SD product; its root-mean-square error (RMSE) reduced from 26.15 cm of the raw product to 7.58 cm, and the correlation coefficient (R) increased from 0.39 to 0.53. For other SD products (WESTDC and FY), the multifactor SD downscaling model still has good applicability, it could further improve the performance of the WESTDC and FY SD products in time and space and achieve better inversion accuracy than raw SD products. Furthermore, the proposed model exhibited good agreement with the observed SD data in a field quadrat (3 km × 2 km) within the fine scale, with an error ranging between −2 and 2 cm. Compared with the existing downscaling methods, the proposed model presented the best performance.

At present, passive microwave remote sensing is the most efficient method to estimate snow depth (SD) at global and regional scales. Farmland covers 46% of Northeast China and accurate SD retrieval throughout the whole snow season has great significance for the agriculture management field. Based on the results of the statistical analysis of snow properties in Northeast China from December 2017 to January 2018, conducted by the China snow investigation project, snow characteristics such as snow grain size (SGS), snow density, snow thickness, and temperature of the layered snowpack were measured and analyzed in detail. These characteristics were input to the microwave emission model of layered snowpacks (MEMLS) to simulate the brightness temperature (TB) time series of snow-covered farmland in the periods of snow accumulation, stabilization, and ablation. Considering the larger SGS of the thick depth hoar layer that resulted in a rapid decrease of simulated TBs, effective SGS was proposed to minimize the simulation errors and ensure that the MEMLS can be correctly applied to satellite data simulation. Statistical lookup tables (LUTs) for MWRI and AMSR2 data were generated to represent the relationship between SD and the brightness temperature difference (TBD) at 18 and 36 GHz. The SD retrieval results based on the LUT were compared with the actual SD and the SD retrieved by Chang’s algorithm, Foster’s algorithm, the standard MWRI algorithm, and the standard AMSR2 algorithm. The results demonstrated that the proposed algorithm based on the statistical LUT achieved better accuracy than the other algorithms due to its incorporation of the variation in snow characteristics with the age of snow cover. The average root mean squared error of the SD for the whole snow season was approximately 3.97 and 4.22 cm for MWRI and AMSR2, respectively. The research results are beneficial for monitoring SD in the farmland of Northeast China.

As one of the major components in the hydrological system, seasonal snow cover in Northeast China has drawn much attention recently. Because of the coarse spatial resolution of the passive microwave (PMW), heterogeneity of snowpack, and forest cover, it is difficult for existing snow products to achieve high precision snow parameters [e.g., snow depth (SD) or snow water equivalent (SWE)] assessment and hydrological research in fine scale. In this study, a novel SD retrieval algorithm that considered both the spatiotemporal dynamic of snow characteristics and forest attenuation was developed by combining the Calibrated Enhanced-Resolution Brightness Temperature (TB) data and other auxiliary information and produced a fine resolution (i.e., 6.25 km $\times6.25$ km) and high accuracy SD data in Northeast China. Instead of complex physical models, the machine learning was used to untangle the nonlinear complex relationship between SD and the enhanced-resolution TB, forest fraction (FF), and snow characteristics. The verification results at ground weather stations showed that the retrieved SD by the proposed algorithm had high consistency with the observed SD, its RMSE, bias, and correlation coefficient ( $R$ ) of 6.32 cm, −0.23 cm, and 0.63, respectively. Compared with the existing SD products (WESTDC and AMSR2), the developed model greatly improved both spatial resolution and retrieval accuracy. In general, the fine-resolution SD inversion model achieved satisfactory accuracy and stability, and it will be used to generate a long-term SD dataset service for climate change and hydrological research in the future.

Satellite passive microwave remote sensing has been extensively used to estimate snow depth (SD) and snow water equivalent (SWE) across both regional and continental scales. However, the presence of forests causes significant uncertainties in the estimations of snow parameters. Forest transmissivity is one of the most important parameters for describing the microwave radiation and scattering characteristics of forest canopies. Although many researchers have constructed models for the functional relationship between forest transmissivity and forest vegetation parameters (e.g., stand growth and accumulation), such relationships are strongly limited by the inversion accuracy of vegetation parameters, forest distribution types, and scale-transformation effects in terms of regional or global scale applications. In this research, we propose a pixel-wise forest transmissivity estimation model (Pixel-wise γ Model) based on long-term series satellite brightness temperature (TB) data for the satellite remote sensing inversion of snow parameters. The model performance is evaluated and applied in SD inversion. The results show that the SD inversion errors RMSE and Bias are 9.8 cm and −1.5 cm, respectively; the SD inversion results are improved by 41% and 84% after using the Pixel-wise γ Model, compared with the forest transmissivity model applied in the GlobSnow v3.0 product. The proposed forest transmissivity model does not depend on forest cover parameters and other ground measurement parameters, which greatly improves its application scope and simplicity.