Accurate estimation of forest aboveground biomass (AGB) and understanding its ecological drivers are vital for carbon monitoring and sustainable forest management. However, AGB estimation using remote sensing is hindered by signal saturation in high-biomass areas and insufficient attention to ecological structural factors. Focusing on Guangdong Province, this study proposes a novel approach that spatially extrapolates airborne LiDAR-derived Forest structural parameters and integrates them with Sentinel-1/2 data to construct an AGB prediction model. Results show that incorporating structural parameters significantly reduces saturation effects, improving prediction accuracy and AGB maximum range in high-AGB regions (R² from 0.724 to 0.811; RMSE = 10.64 Mg/ha; max AGB > 180 Mg/ha). Using multi-scale geographically weighted regression (MGWR), we further examined the spatial influence of forest type, age structure, and species mixture. Forest age showed a strong positive correlation with AGB in over 95% of the area, particularly in mountainous and hilly regions (coefficients up to 1.23). Species mixture had positive effects in 87.7% of the region, especially in the north and parts of the south. Natural forests consistently exhibited higher AGB than plantations, with differences amplifying at later successional stages. Highly mixed natural forests showed faster biomass accumulation and higher steady-state AGB, highlighting the regulatory role of structural complexity and successional maturity. This study not only mitigates remote sensing saturation issues but also deepens understanding of spatial and ecological drivers of AGB, offering theoretical and technical support for targeted carbon stock assessment and forest management strategies.

Accurate collection of crop planting information at large area is essential for estimating agricultural productivity and ensuring food security. Different crops have varying growth cycles and phenological stages, and changes in factors such as topography, soil type, and moisture conditions can lead to diversity in crops growth status, which complicates uniform monitoring. Multiple crops mapping simultaneously with high precision presents a significant challenge due to the high spatial heterogeneity of crops distribution across vast regions. To address these challenges, this paper developed an advanced deep learning crop mapping method, i.e., Phenological Horizon Attention Mechanism-Transformer model (PHAT) to achieve rapid and accurate multiple crops extraction over large areas. Initially, time series data were constructed using the normalized differential vegetation index (NDVI) dataset based on Moderate Resolution Imaging Spectroradiometer (MODIS) product. Subsequently, in the mixed pixel decomposition phase, orthogonal subspace projection (OSP) and vertex component analysis (VCA) were employed to identify crop types and extract endmembers. While the regular changes in the time series NDVI reflect the phenological evolution trend among multiple crops, but the phenological characteristics difference between the same crop is extremely difficult to find. The PHAT model was therefore trained using the phenological features of endmembers to obtain the spatial distribution of crops, and to resolve the issue of varying time series curves for the same crop across large areas. This study selected the North China Plain in 2021 as the research area, utilizing Google Earth data and Landsat 8 images to verify the approach's accuracy. Based on the MODIS NDVI data with a coarse spatial resolution of 250 meters, our method achieved an OA of 90.1% for the synchronous extraction of soybean, spring peanut-summer sesame, winter wheat-summer maize, paddy rice, and rapeseed-cotton, with a RMSE of approximately 12% in 16.6 million mu of cultivated land.

Phenology reflects the life cycle of vegetation, crucial for monitoring global vegetation diversity, ecosystem stability, and agricultural security. However, there is currently no dataset related to phenology. The 24 solar terms (24STs), based on the Sun's annual motion, reflect the changing seasons, temperature fluctuations, and phenological phenomena. They serve as a vital means to characterize vegetation phenology. This study generate a global Normalized Difference Vegetation Index (NDVI) product based on 24STs using Moderate Resolution Imaging Spectroradiometer (MODIS) on the Google Earth Engine (GEE). The 24STs NDVI dataset adopted the maximum value compositing (MVC) to process the NDVI values between two adjacent 24STs. The product has a spatial resolution of 250 m, covering the period from 2001 to 2022. Comparing with the MOD13Q1, good spatiotemporal consistency between the two datasets was observed, confirming the reliability of the 24STs product. However, the 24STs product holds distinct phenological meanings. This product introduces, for the first time, a vegetation index dataset based on the 24STs, enriching the vegetation index dataset and facilitating further research on phenology.

Understanding the spatiotemporal evolution patterns and drivers of cropping structures is crucial for adjusting cropping structure policies, ensuring the sustainability of land resources, and safeguarding food security. However, existing research lacks sub-pixel scale data on planting structure, where planted area data are mainly derived from manual counting results. In this study, remote sensing technology was combined with geostatistical methods to realize the spatiotemporal evolution of crop planting structure at sub-pixel scale. Firstly, the spatial distribution of the multiple cropping structure in Henan Province was extracted based on a mixed-pixel decomposition model, and spatiotemporal evolution of the crop planting structure was analyzed using a combination of Sen’s slope estimator and Mann–Kendall trend analysis, as well as centroid migration. Then, Pearson correlation coefficients were calculated to explore the contribution of driving factors. The results indicate the following: (1) from 2001 to 2022, the cropping structure in Henan Province shows a slightly obvious increase. (2) The centroid of different cropping structures migrates to the main production areas as a whole. (3) Among the driving factors, there was a positive correlation with the labor force and a negative correlation with the urbanization rate. This study provides new insights into the evolution of large-scale crop planting structures and offers significant theoretical and practical value for sustainable agricultural development and the optimization of agricultural planting structures.

Mastering information on cotton yield in large areas has an irreplaceable role in agricultural production and balancing the relationship between supply and demand. Owing to temporal and spatial phenological differences and spectral inconsistencies in large areas, cotton information extraction poses additional challenges. Traditional prediction methods only use a single piece of information and rely on a large number of samples, which are unsuitable for large-scale cotton extraction research because of their poor generalization ability and limited sample availability in large areas. To address these challenges, this study introduces a semi-supervised temporal-spatial-spectral method (TSpaSpe) that leverages Sentinel-2 data. This method integrates temporal-spatial-spectral features along with phenological information to accentuate the distinctions between cotton and other crops and weaken the model dependence on samples. The TSpaSpe method was tested in three renowned high-yield cotton-producing regions (i.e., Aksu in Xinjiang, China; Irving in Texas, the United States; and Querencia, Mato Grosso State, Brazil), and sample datasets were generated using Google Earth high-resolution images. Compared with eight other prediction methods derived from various data configurations, the two methods grounded in the TSpaSpe framework demonstrated superior performance in distinguishing cotton from other crops. The application of the proposed methods to cotton extraction using Sentinel-2 images across the three study areas revealed that the TSpaSpe_CNNDW approach achieved the highest average R2 and overall performance relative to the alternative extraction techniques. Further validation confirmed that TSpaSpe_CNNDW outperformed the other methods in predicting cotton yield across different years within the three regions. These findings suggest that TSpaSpe_CNNDW can effectively extract spatiotemporal distribution patterns and growth information for cotton in diverse environments. Moreover, this technique shows great promise for advancing the field of large-area cotton yield prediction, offering a robust tool for agricultural planning and policy making.