The aim of this study was to explore the differences in the accuracy of winter wheat identification using remote sensing data at different growth stages using the same methods. Part of northern Henan Province, China was taken as the study area, and the winter wheat growth cycle was divided into five periods (seeding-tillering, overwintering, reviving, jointing-heading, and flowering-maturing) based on monitoring data obtained from agrometeorological stations. With the help of the Google Earth Engine (GEE) platform, the separability between winter wheat and other land cover types was analyzed and compared using the Jeffries-Matusita (J-M) distance method. Spectral features, vegetation index, water index, building index, texture features, and terrain features were generated from Sentinel-2 remote sensing images at different growth periods, and then were used to establish a random forest classification and extraction model. A deep U-Net semantic segmentation model based on the red, green, blue, and near-infrared bands of Sentinel-2 imagery was also established. By combining models with field data, the identification of winter wheat was carried out and the difference between the accuracy of the identification in the five growth periods was analyzed. The experimental results show that, using the random forest classification method, the best separability between winter wheat and the other land cover types was achieved during the jointing-heading period: the overall identification accuracy for the winter wheat was then highest at 96.90% and the kappa coefficient was 0.96. Using the deep-learning classification method, it was also found that the semantic segmentation accuracy of winter wheat and the model performance were best during the jointing-heading period: a precision, recall, F1 score, accuracy, and IoU of 0.94, 0.93, 0.93, and 0.88, respectively, were achieved for this period. Based on municipal statistical data for winter wheat, the accuracy of the extraction of the winter wheat area using the two methods was 96.72% and 88.44%, respectively. Both methods show that the jointing-heading period is the best period for identifying winter wheat using remote sensing and that the identification made during this period is reliable. The results of this study provide a scientific basis for accurately obtaining the area planted with winter wheat and for further studies into winter wheat growth monitoring and yield estimation.

Accurately predicting winter wheat yield before harvest could greatly benefit decision-makers when making management decisions. In this study, we utilized weather forecast (WF) data combined with Sentinel-2 data to establish the deep-learning network and achieved an in-season county-scale wheat yield prediction in China’s main wheat-producing areas. We tested a combination of short-term WF data from the China Meteorological Administration to predict in-season yield at different forecast lengths. The results showed that explicitly incorporating WF data can improve the accuracy in crop yield predictions [Root Mean Square Error (RMSE) = 0.517 t/ha] compared to using only remote sensing data (RMSE = 0.624 t/ha). After comparing a series of WF data with different time series lengths, we found that adding 25 days of WF data can achieve the highest yield prediction accuracy. Specifically, the highest accuracy (RMSE = 0.496 t/ha) is achieved when predictions are made on Day of The Year (DOY) 215 (40 days before harvest). Our study established a deep-learning model which can be used for early yield prediction at the county level, and we have proved that weather forecast data can also be applied in data-driven deep-learning yield prediction tasks.

Currently, analyses related the status of soybeans, a major oil crop, as well as the related climate drivers, are based on on-site data and are generally focused on a particular country or region. This study used remote sensing, meteorological, and statistical data products to analyze spatiotemporal variations at the end of the growing season (EOS) for soybeans in the world’s major soybean-growing areas. The ridge regression estimation model calculates the average annual temperature, precipitation, and total radiation contributions to phenological changes. A systematic analysis of the spatiotemporal changes in the EOS and the associated climate drivers since the beginning of the 21st century shows the following: (1) in India, soybean EOS is later than in China and the United States. The main soybean-growing areas in the southern hemisphere are concentrated in South America, where two crops are planted yearly. (2) In most of the world’s soybean-growing regions, the rate change of the EOS is ±2 days/year. In the Mississippi River Valley, India, and South America (the first quarter), the soybean EOS is generally occurring earlier, whereas, in northeast China, it is generally occurring later. (3) The relative contributions of different meteorological factors to the soybean EOS vary between soybean-growing areas; there are also differences within the individual areas. This study provides a solid foundation for understanding the spatiotemporal changes in soybean crops in the world’s major soybean-growing areas and spatiotemporal variations in the effects of climate change on soybean EOS.

Urban forest tree species composition and spatial distribution are essential for refined greenspace management, ecosystem-service assessment, and forest-health monitoring. High-resolution imagery captures crown texture and spatial boundaries, multi-temporal NDVI reflects phenological differences, LiDAR provides canopy height and structural information, and hyperspectral imagery offers detailed spectral discrimination. However, differences in spatial resolution and information representation limit the effectiveness of simple feature stacking. Motivated by the challenges associated with heterogeneous stand structure, similarity among broadleaved species, and insufficient multi-source fusion, this study developed HybridFusion for fine-grained tree species classification in the northern section of Beijing Olympic Forest Park. The model uses 0.5 m high-resolution imagery as its spatial basis and integrates NDVI phenological features from March, April, and August, LiDAR-derived canopy structural features, and hyperspectral–semantic priors through branch encoding, dynamic gated fusion, and reliability-aware hyperspectral modulation. The full-modality model achieved optimal performance (OA: 90.44%; Tree OA: 85.06%; mIoU: 77.88%). Poplar achieved an F1 score of 95.31%, while willow, coniferous trees, and other tree species reached 86.27%, 84.90%, and 85.35%, respectively. Ablation studies confirmed the complementary value of the multi-source data: integrating NDVI and LiDAR increased Tree OA from 67.29% to 81.90%, and the addition of hyperspectral–semantic priors further raised it to 85.06%. These results demonstrate the effectiveness of jointly exploiting phenological, structural, and spectral–semantic information for fine-scale urban forest inventory and management.