Accurately extracting parcels from satellite images is crucial in precision agriculture. Traditional edge detection fails in complex scenes with difficult post-processing, and deep learning models are time-consuming in terms of sample preparation and less transferable. Based on this, we designed a method combining multi-resolution remote sensing images based on the Segment Anything Model (SAM). Using cropland masking, overlap prediction and post-processing, we achieved 10 m-resolution parcel extraction with SAM, with performance in plain areas comparable to existing deep learning models (P: 0.89, R: 0.91, F1: 0.91, IoU: 0.87). Notably, in hilly regions with fragmented cultivated land, our approach even outperformed these models (P: 0.88, R: 0.76, F1: 0.81, IoU: 0.69). Subsequently, the 10 m parcels results were utilized to crop the high-resolution image. Based on the histogram features and internal edge features of the parcels, used to determine whether to segment downward or not, and at the same time, by setting the adaptive parameters of SAM, sub-meter parcel extraction was finally realized. Farmland boundaries extracted from high-resolution images can more accurately characterize the actual parcels, which is meaningful for farmland production and management. This study extended the application of deep learning large models in remote sensing, and provided a simple and fast method for accurate extraction of parcels boundaries.

The timely and precise estimation of crop aboveground biomass (AGB) is crucial for evaluating crop development and forecasting yields. The objective is to examine the differences, advantages, and limitations between time series parameters and single-time-phase indicators derived from various vegetation indices in AGB estimation. Moreover, we aim to quantitatively investigate and elucidate the impact of the topographic and geographic conditions of the study region on the estimation of highland barley AGB. Results indicate that AGB simulations utilizing time series parameters from vegetation index time series (VI-TS) curves yield satisfactory results for all three VIs, with the exception of the Normalized Difference Vegetation Index (NDVI), which encounters saturation issues. The performance metrics are as follows: the Enhanced Vegetation Index (EVI) (R² = 0.73, RMSE = 20.24 g/m²), the Soil-Adjusted Vegetation Index (SAVI) (R² = 0.67, RMSE = 20.97 g/m²), and the Normalized Difference Mountain Vegetation Index (NDMVI) (R² = 0.54, RMSE = 24.92 g/m²). The inclusion of our quantitative terrain factor improves the simulation accuracies of NDVI, SAVI, and NDMVI. Overall, the terrain factor has a beneficial impact on the highland barley AGB simulation outcomes. This study establishes a foundational framework for the timely and precise estimation of highland barley biomass, crucial for monitoring agricultural production in plateau mountainous regions.

Accurate diagnostics of crop yields are essential for climate-resilient agricultural planning; however, conventional datasets often conflate environmental covariates during model training. Here, we present HHHWheatYield1km, a 1 km resolution winter wheat yield dataset for China’s Huang-Huai-Hai Plain spanning 2000–2019. By integrating climate-independent multi-source remote sensing metrics with a Random Forest model, calibrated against municipal statistical yearbooks, the dataset exhibits strong agreement with county-level records (R = 0.90, RMSE = 542.47 kg/ha, MRE = 9.09%), ensuring independence from climatic influences for robust driver analysis. Using Geodetector, we reveal pronounced spatial heterogeneity in climate–yield interactions, highlighting distinct regional disparities: precipitation variability exerts the strongest constraints on yields in Henan and Anhui, whereas Shandong and Jiangsu exhibit weaker climatic dependencies. In Beijing–Tianjin–Hebei, March temperature emerges as a critical determinant of yield variability. These findings underscore the need for tailored adaptation strategies, such as enhancing water-use efficiency in inland provinces and optimizing agronomic practices in coastal regions. With its dual ability to resolve pixel-scale yield dynamics and disentangle climatic drivers, HHHWheatYield1km represents a resource for precision agriculture and evidence-based policymaking in the face of a changing climate.

Accurate crop density estimation is critical for effective agricultural resource management, yet existing methods face challenges due to data acquisition difficulties and low model usability caused by inconsistencies between optical and radar imagery. This study presents a novel approach to maize density estimation by integrating optical and radar data, addressing these challenges with a unique mapping strategy. The strategy combines available data selection, key feature extraction, and optimization to improve accuracy across diverse growth stages. By identifying critical features for maize density and incorporating machine learning to explore optimal feature combinations, we developed a multi-temporal model that enhances estimation accuracy, particularly during leaf development, stem elongation, and tasseling stages (R² = 0.602, RMSE = 0.094). Our approach improves performance over single-temporal models, and successful maize density maps were generated for the three typical demonstration counties. This work represents an advancement in large-scale crop density estimation, with the potential to expand to other regions and support precision agriculture efforts, offering a foundation for future research on optimizing agricultural resource management.