Although efforts and progress have been made in crop classification using optical remote sensing images, it is still necessary to make full use of the high spatial, temporal, and spectral resolutions of remote sensing images. However, with the increasing volume of remote sensing data, a key emerging issue in the field of crop classification is how to find useful information from massive data to balance classification accuracy and processing time. To address this challenge, we developed a novel crop classification method, combining optimal feature selection (OFSM) with hybrid convolutional neural network-random forest (CNN-RF) networks for multi-temporal optical remote sensing images. This research used 234 features including spectral, segmentation, color, and texture features from three scenes of Sentinel-2 images to identify crop types in the Jilin province of northeast China. To effectively extract the effective features of remote sensing data with lower time requirements, the use of OFSM was proposed with the results compared with two traditional feature selection methods (TFSM): random forest feature importance selection (RF-FI) and random forest recursive feature elimination (RF-RFE). Although the time required for OFSM was 26.05 s, which was between RF-FI with 1.97 s and RF-RFE with 132.54 s, OFSM outperformed RF-FI and RF-RFE in terms of the overall accuracy (OA) of crop classification by 4% and 0.3%, respectively. On the basis of obtaining effective feature information, to further improve the accuracy of crop classification we designed two hybrid CNN-RF networks to leverage the advantages of one-dimensional convolution (Conv1D) and Visual Geometry Group (VGG) with random forest (RF), respectively. Based on the selected optimal features using OFSM, four networks were tested for comparison: Conv1D-RF, VGG-RF, Conv1D, and VGG. Conv1D-RF achieved the highest OA at 94.27% as compared with VGG-RF (93.23%), Conv1D (92.59%), and VGG (91.89%), indicating that the Conv1D-RF method with optimal feature input provides an effective and efficient method of time series representation for multi-temporal crop-type classification.

Clarifying the evolution pattern of groundwater storage (GWS) is crucial for exploring the amount of available water resources at a regional or basin scale. Currently, the groundwater resources of Northeast China have been extensively exploited, but only limited studies have assessed the extent of GWS depletion and its driving mechanisms. In this study, the groundwater storage anomaly (GWSA) in the black soil region of Northeast China was explored based on the Gravity Recovery and Climate Experiment (GRACE) satellite combined with the Global Land Data Assimilation System (GLDAS) hydrological model. The results show that from 2002 to 2021, the overall GWSA decreased (−0.4204 cm/a), and specifically, the average rates of decrease in Heilongjiang, Jilin, and Liaoning Provinces were −0.2786, −0.5923, and −0.6694 cm/a, respectively, with the eastern, southern, and central parts of Heilongjiang, Jilin, and Liaoning Provinces losing seriously. Especially the GWSA deficit trend can reach −0.7471 cm/a in southern Jilin Province. The GWSA deficits in the three provinces from April to September were greater than 0.40 cm/a, while the deficit values from January to March and from October to December were less than 0.40 cm/a. This study is the first to quantitatively analyze the GWSA and its influencing factors in Northeast China for 2002–2021. The results of the study help clarify the differences in the spatial and temporal distribution of groundwater resources and their driving mechanisms in the northeastern black soil regions and provide a reference for the conservation and sustainable utilization of groundwater resources in the black soil region.

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.

Spring soil moisture (SM) is of great importance for monitoring agricultural drought and waterlogging in farmland areas. While winter snow cover has an important impact on spring SM, relatively little research has examined the correlation between winter snow cover and spring SM in great detail. To understand the effects of snow cover on SM over farmland, the relationship between winter snow cover parameters (maximum snow depth (MSD) and average snow depth (ASD)) and spring SM in Northeast China was examined based on 30 year passive microwave snow depth (SD) and SM remote-sensing products. Linear regression models based on winter snow cover were established to predict spring SM. Moreover, 4 year SD and SM data were applied to validate the performance of the linear regression models. Additionally, the effects of meteorological factors on spring SM also were analyzed using multiparameter linear regression models. Finally, as a specific application, the best-performing model was used to predict the probability of spring drought and waterlogging in farmland in Northeast China. Our results illustrated the positive effects of winter snow cover on spring SM. The average correlation coefficient (R) of winter snow cover and spring SM was above 0.5 (significant at a 95% confidence level) over farmland. The performance of the relationship between snow cover and SM in April was better than that in May. Compared to the multiparameter linear regression models in terms of fitting coefficient, MSD can be used as an important snow parameter to predict spring drought and waterlogging probability in April. Specifically, if the relative SM threshold is 50% when spring drought occurs in April, the prediction probability of the linear regression model concerning snow cover and spring SM can reach 74%. This study improved our understanding of the effects of winter snow cover on spring SM and will be beneficial for further studies on the prediction of spring drought.

Soil moisture (SM) retrieval from SMOS (the Soil Moisture and Ocean Salinity mission) and SMAP (the Soil Moisture Active/Passive mission) passive microwave data over forested areas with required accuracy is of great significance and poses some challenges. In this paper, we used Ground Wireless Sensor Network (GWSN) SM measurements from 9 September to 5 November 2015 to validate SMOS and SMAP Level 3 (L3) SM products over forested areas in northeastern China. Our results found that neither SMOS nor SMAP L3 SM products were ideal, with respective RMSE (root mean square error) values of 0.31 cm³/cm³ and 0.17 cm³/cm³. Nevertheless, some improvements in SM retrieval might be achievable through refinements of the soil dielectric model with respect to high percentage of soil organic matter (SOM) in the forested area. To that end, the potential of the semi-empirical soil dielectric model proposed by Jun Liu (Liu’s model) in improving SM retrieval results over forested areas was investigated. Introducing Liu’s model into the retrieval algorithms of both SMOS and SMAP missions produced promising results. For SMAP, the RMSE of L3 SM products improved from 0.16 cm³/cm³ to 0.07 cm³/cm³ for AM (local solar time around 06:00 am) data, and from 0.17 cm³/cm³ to 0.05 cm³/cm³ for PM (local solar time around 06:00 pm) data. For SMOS ascending orbit products, the accuracy was improved by 56%, while descending orbit products improved by 45%.

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.