Using remote sensing images to identify crop plots and estimate crop planting area is an important part of agricultural remote sensing monitoring. High-resolution remote sensing images can provide rich information regarding texture, tone, shape, and spectrum of ground objects. With the advancement of sensor and information technologies, it is now possible to categorize crops with pinpoint accuracy. This study defines crop mapping as a semantic segmentation problem; therefore, a deep learning method is proposed to identify the distribution of corn and soybean using the differences in the spatial and spectral features of crops. The study area is located in the southwest of the Great Lakes in the United States, where corn and soybean cultivation is concentrated. The proposed attention mechanism deep learning model, A₂SegNet, was trained and evaluated using three years of Sentinel-2 data, collected between 2019 and 2021. The experimental results show that this method is able to fully extract the spatial and spectral characteristics of crops, and its classification effect is significantly better than that of the baseline method, and it has better classification performance than other deep learning models. We cross verified the trained model on the test sets of different years through transfer learning in both spatiotemporal and spatial dimensions. Proving the effectiveness of the attention mechanism in the process of knowledge transfer, A₂SegNet showed better adaptability.

Optical remote sensing data are widely used for constructing soil organic carbon (SOC) mapping models. However, it is challenging to map SOC in forested areas because atmospheric water vapor affects the results derived from optical remote sensing data. To address this issue, we utilized Sentinel-1, Sentinel-2, and digital elevation model (DEM) data to obtain a comprehensive feature set (including S1-based textural indices, S2-based spectral indices, and DEM-derived indices) to map the SOC content in forested areas. The features set were the predictor variables, and the measured SOC content was the dependent variable. The random forest algorithm was used to establish the SOC model. The ratio of performance to inter-quartile range (RPIQ) was 2.92 when the S2-based spectral indices were used as predictor variables. When the comprehensive feature set was utilized as the model input, the model achieved an RPIQ of 4.13 (R² = 0.91, root mean square error (RMSE) = 9.18), representing a 41.44% improvement in model accuracy. The average SOC content in the Greater Khingan Mountains was 43.75 g kg⁻¹. The northern and southwestern parts had higher SOC contents (>54.93 g kg⁻¹), while the southeastern and northwestern parts had lower contents (<39.83 g kg⁻¹). This discrepancy was primarily attributed to agricultural activities. The results indicate that using a comprehensive feature set and the random forest algorithm is a reliable approach for estimating the spatial distribution of the SOC content in forested areas and is suitable for forest ecology and carbon management studies.

We evaluated the hourly dynamics of ground-dwelling invertebrate communities in farmland using infrared camera traps between August and September 2022. No significant variations within 24 h nor between any two time points of each day were observed in the taxonomic richness and abundance of the entire community. However, the periods from 4:00 to 7:00 and 13:00 to 20:00 showed relatively high taxonomic richness, while those from 2:00 to 6:00 and 16:00 to 21:00 showed relatively high abundance. Millipede abundance varied significantly in a 24 h period, with higher abundance from 3:00 to 4:00 and 1:00 to 2:00. Additionally, slug, beetle, and grasshopper abundances were significantly higher from 22:00 to 23:00, 17:00 to 18:00, and 23:00 to 24:00, respectively. The abundance of other taxa did not show significant variations between any two time points of a day. Predominant generalist predators showed positive correlation in their activity times. These results suggest that significant variations within each 24 h period are uncommon at either community or taxa (except for millipedes) levels in farmland ground-dwelling invertebrates. Further, while most taxa had significantly preferred active hours, the total community did not. Therefore, hourly dynamics should be considered to understand biodiversity maintenance.

Snow depth is a critical parameter for characterizing snow dynamics and water resources, and its accurate inversion is essential for hydrological processes, climate studies, and disaster prevention in cold regions. Based on long-term daily ground meteorological observation data from the hydrological years 1961 to 2015 at two meteorological stations in Mohe and Mishan, Heilongjiang Province, China, this study integrates physical parameters of snow density and snow albedo from the ERA5-Land reanalysis data to systematically compare the performance of three machine learning and three deep learning models in retrieving daily snow depth. Four feature combination schemes were designed to evaluate the contributions of meteorological factors, lagged snow depth terms, and snow physical parameters. The results indicate that, for both machine learning and deep learning models, the first-order lagged value of snow depth is the most important variable determining prediction accuracy. In terms of model performance, machine learning methods excelled, with XGBoost performing particularly outstandingly, achieving optimal prediction accuracy and stability under the best feature combination (coefficient of determination, R², reaching 0.989; root mean square error, RMSE, of 1.19 cm). Among deep learning methods, 1D CNN demonstrated strong local feature extraction capabilities, achieving prediction accuracy comparable to the best-performing machine learning model (R² of 0.9878, RMSE of 1.26 cm). Notably, models specifically designed for time-series data, such as LSTM (R² of 0.9848, RMSE of 1.41 cm), and the more complex 1D CNN-LSTM hybrid model (R² of 0.9803, RMSE of 1.60 cm) did not show significant advantages in this study. This indicates that model complexity and predictive performance are not simply positively correlated. Through comprehensive analysis of data from both stations, this study demonstrates that a prediction framework centered on historical snow depth as the core driving factor, combined with key meteorological elements, is highly robust. Although the inclusion of ERA5-Land snow physical parameters did not significantly improve model accuracy, it provides important insights for the future development of hybrid models that integrate physical mechanisms with data-driven approaches. The findings offer an effective solution for reconstructing long-term snow depth time series and hold significant application value for simulating cryospheric hydrological processes and studying climate change.

The recent increase in the frequency of extreme weather events and declining soil biodiversity in global agricultural ecosystems make it crucial to assess the daily dynamics of soil communities in fields. To elucidate the daily dynamics of ground-dwelling invertebrate communities, their communities were monitored temporally using infrared camera traps in field farmland during and following an extremely high-temperature (EHT) event in summer 2022 in Ningbo City, China. Nine taxa and 1,147 individuals of the ground-dwelling invertebrate community were photographed in the 176,256 images. There were no significant differences in the taxonomic richness and abundance of the total ground-dwelling invertebrate communities during and following the EHT event. The abundance of ants was significantly decreased following the EHT event, whereas the abundance of other taxa was not. Significantly daily dynamics and obvious differences between each day in taxonomic richness, abundance of ground-dwelling invertebrate community, and abundance of each taxon were not observed during and following the EHT event. The results of this study showed that the daily dynamics of richness and abundance of the ground-dwelling invertebrate community and the abundance of each taxon were not significant during and following the EHT event. Overall, this study provides a useful monitoring method to observe the daily dynamics of ground-dwelling invertebrates in field farmlands and suggests that the daily dynamics of soil fauna communities should be further studied when assessing the effects of climate change on soil biodiversity.