Soil analysis using near-infrared spectroscopy has shown great potential to be an alternative to traditional laboratory analysis, and there is continuously increasing interest in building large-scale soil spectral libraries (SSLs). However, due to issues such as high non-linearity in soil spectral data and complexity in soil spatial variation, the establishment of robust prediction models for soil spectral libraries remains a challenge. This study aimed to investigate the performance of deep learning algorithms, including long short-term memory (LSTM) and LSTM–convolutional neural networks (LSTM–CNN) integrated models, to predict the soil organic matter (SOM) of a provincial-scale SSL, and compare it to the normally used local weighted regression (LWR) model. The Hebei soil spectral library (HSSL) contains 425 topsoil samples (0–20 cm), of which every 3 soil samples were collected from dry land, irrigated land, and paddy fields, respectively, in different counties of Hebei Province, China. The results show that the accuracy of the validation dataset rank as follows: LSTM–CNN (R²_p = 0.96, RMSE_p = 1.66 g/kg) > LSTM (R²_p = 0.83, RMSE_p = 3.42 g/kg) > LWR (R²_p = 0.82, RMSE_p = 3.79 g/kg). The LSTM–CNN model performed the best, mainly due to its comprehensive ability to effectively extract spatial and temporal features. Meanwhile, the LSTM model achieved higher accuracy than the LWR model, owing to its built-in memory unit and its advantage of faster feature band extraction. Thus, it was suggested to use deep learning algorithms for SOM predictions in SSLs. However, their performance on larger-scale SSLs such as continental/global SSLs still needs to be further investigated.

Sustainable agricultural production systems are important for ensuring food security. However, they are severely threatened by soil degradation and carbon emissions resulting from traditional farming practices. A cooperative-dominated conservation tillage model attempts to mitigate these issues, yet it is not clear how this model has been implemented and how well it performs in practice. This study takes Lishu County in Jilin Province in Northeast China as a case study to explore the implementation of a cooperative-dominated conservation tillage (CDCT) model and its practical effectiveness. In contrast to the traditional production model, this model uses cooperatives as the direct managers of cultivated land and promotes the construction of new production units and large-scale and mechanized operations to standardize the application of conservation tillage technology in agricultural production. Scientific research institutes, governments, and enterprises are supporters of cooperatives, empowering them in terms of technology, capital, products, and services. The evaluation results show that, unlike the traditional production model, which caused a decrease in the soil organic carbon content, the organic carbon content of the topsoil of cultivated land under this model increased by an average of 6.17% after 9 years of conservation tillage application. Furthermore, the soil structural stability index of the cultivated land increased from 3.35% to 3.69%, indicating that the degree of soil structural degradation was alleviated to a certain extent. The CDCT model effectively enhanced the operational efficiency and fertilizer use efficiency, and the carbon footprint of maize production was also reduced by 15.65% compared to the traditional production model. In addition, the total production cost was reduced by 1449 CNY/ha and profit increased by 2599 CNY/ha on average, indicating higher economic returns under the CDCT model due to increased yields and lower input costs. Farmers who are freed from agricultural production activities by transferring their farmland can also gain two types of income—land revenue and labor wagesi—thus mproving their living conditions. The CDCT model can deliver multigoal benefits and be of great value in its extension to other regions. This study may provide lessons for the sustainable use of cultivated land in China and other developing countries, contributing to agricultural development with lower environmental costs.

Remote sensing technology provides an efficient and low-cost approach for acquiring large-scale soil information, offering notable advantages for soil organic matter (SOM) mapping. However, in recent years, the bare soil period of cultivated land in Northeast China has significantly shortened, posing serious challenges to traditional SOM prediction and mapping methods that rely on optical imagery. Meanwhile, current approaches that integrate optical imagery, radar imagery, and environmental covariates have yet to fully exploit the potential of remote sensing data in SOM mapping. To address this, this study focuses on the typical black soil region in Northeastern China, acquiring median synthetic images from different time periods (crop sowing, growing, and harvest stages) along with vegetation and radar indices. Six data groups were created by integrating environmental covariate data. Four machine learning models—XGBoost, BRT, ET, and RF—were used to analyze the SOM prediction accuracy of different groups. The group and model with the highest prediction accuracy were selected for SOM mapping in cultivated land. The results show that: (1) in the same model, incorporating radar images and their related indices significantly improves SOM prediction accuracy; (2) when using four machine learning models for SOM prediction, the RF model, which integrates optical images, radar images, vegetation indices, and radar indices from the crop sowing and growing periods, achieves the highest accuracy (R² = 0.530, RMSE = 6.130, MAE = 4.822); (3) in the optimal SOM prediction model, temperature, precipitation, and elevation are relatively more important, with radar indices showing greater importance than vegetation indices; (4) uncertainty analysis and accuracy verification at the raster scale confirm that the SOM mapping results obtained in this study are highly reliable. This study made significant progress in SOM prediction and mapping by employing a radar–optical image fusion strategy combined with crop growth information. It helped address existing research gaps and provided new approaches and technical solutions for remote sensing-based SOM monitoring in regions with short bare soil periods.