How climate change influences lake ice phenology is important in understanding the climate‐lake interactions. This study investigates the response of lake ice phenology to future climate scenarios. Thirty‐five lakes in the Tibetan Plateau were studied. Firstly, we applied a random forest (RF) model to simulate lake ice condition under different representative concentration pathways (RCPs). The results of the virtual experiments show that lake ice freeze‐up start date (FUSD) and break‐up end date (BUED) are well simulated by the RF model, with R²_FUSD > 0.91 under different RCPs, and R²_BUED > 0.84, except in RCP2.6 (R² = 0.74). Secondly, two non‐parametric methods (Mann‐Kendall and Sen's slope estimator) were used for analyzing the trends in lake ice phenology and its response to various emission scenarios. Lake ice phenology was largely affected by temperature changes under different RCPs, and it had a larger inter‐annual variability in the early period (2002–2050) than in the later period (2050–2099). FUSD of 35 lakes delayed by an average of 0.01, 0.04, and 0.04 days/yr from 2002 to 2098 under RCP4.5, RCP6.0, and RCP8.5, whereas BUED advanced by an average of 0.04, 0.11, and 0.21 days/yr from 2003 to 2099. Both delays in FUSD and advances in BUED contributed to shortened average ice duration of 35 lakes, which shortened by 0.05, 0.14, and 0.25 days/yr from 2002 to 2098 under RCP4.5, RCP6.0, and RCP8.5, respectively. These findings could help to tackle the impacts of climate change on the lake systems.

Vegetation activities and stresses are crucial for vegetation health assessment. Changes in an environment such as drought do not always result in vegetation drought stress as vegetation responses to the climate involve complex processes. Satellite-based vegetation indices such as the Normalized Difference Vegetation Index (NDVI) have been widely used to monitor vegetation activities. As satellites only carry information for understanding past and current vegetation conditions, there is a need to model vegetation dynamics to make future predictions. Although many other factors are related, we attempt to predict the vegetation activities and stresses via simulating NDVI, based on only meteorological data and using a deep learning method (bidirectional long short-term memory model, BiLSTM). The BiLSTM is a sequence processing model that can predict NDVI by establishing the relationship between meteorological variables and vegetation activities. Experimental results show that the predicted NDVI is consistent with the reference data (R² = 0.69 ± 0.28). The best accuracy was achieved in the deciduous forest (R² = 0.87 ± 0.16). The vegetation condition index (VCI) calculated from the BiLSTM-predicted NDVI also agreed with the satellite-based ones (R² = 0.70 ± 0.28). Both the monitored and predicted VCI indicated an upward but insignificant trend of vegetation activity in the past decade and increased vegetation stresses in the early growing season over northern China. Based on meteorological data, the deep learning-based solution shows the potential for not only retrospective analysis, but also future prediction of vegetation activities and stresses under varied climate conditions as compared with remote sensing data.

Using change detection technique precisely analyzes remote sensing images, it has a broad range of applications in resource surveys, surveillance systems, and map updating. In recent years, deep learning-based methods have become a focus area owing to their excellent feature extraction and representation ability. The fusion of multi-scale features is the key to improving change detection performance in fully convolutional network-based structural methods, primarily based on an architecture with skip connections or nested and dense skip connections. However, these methods only fuse features on the same scale and lack sufficient information from multiple scales to generate appropriate results. Traditional feature fusion with redundant and unsupervised information leads to poor model fitting. To solve these problems, we proposed a novel attention-guided full-scale feature aggregation network (AFSNet). The proposed method used a Siamese structure as the backbone network to extract features, which were then aggregated using full-scale skip connections, and an attention mechanism to avoid feature redundancy. Finally, to obtain a highly accurate final change map, a multiple side-outputs fusion strategy was used to fuse the change maps at different scales. To check the reliability of AFSNet, we tested it on two public datasets, the LEVIR-CD and SVCD datasets. The F1-Score/IoU scores improved by 0.57%/0.95% and 1.14%/2.07% in the two datasets, respectively, compared to those obtained using the methods that achieved suboptimal values. The results showed that AFSNet outperforms other mainstream methods while maintaining a good balance between computational costs and model parameters.

Satellite-retrieved land surface phenology (LSP) is a first-order control on terrestrial ecosystem productivity, which is critical for monitoring the ecological environment and human and social sustainable development. However, mapping large-scale LSP at a 30 m resolution remains challenging due to the lack of dense time series images with a fine resolution and the difficulty in processing large volumes of data. In this paper, we proposed a framework to extract fine-resolution LSP across the conterminous United States using the supercomputer Tianhe-2. The proposed framework comprised two steps: (1) generation of the dense two-band enhanced vegetation index (EVI2) time series with a fine resolution via the spatiotemporal fusion of MODIS and Landsat images using ESTARFM, and (2) extraction of the long-term and fine-resolution LSP using the fused EVI2 dataset. We obtained six methods (i.e., AT, FOD, SOD, RCR, TOD and CCR) of fine-resolution LSP with the proposed framework, and evaluated its performance at both the site and regional scales. Comparing with PhenoCam-observed phenology, the start of season (SOS) derived from the fusion data using six methods of AT, FOD, SOD, RCR, TOD and CCR obtained r values of 0.43, 0.44, 0.41, 0.29, 0.46 and 0.52, respectively, and RMSE values of 30.9, 28.9, 32.2, 37.9, 37.8 and 33.2, respectively. The satellite-retrieved end of season (EOS) using six methods of AT, FOD, SOD, RCR, TOD and CCR obtained r values of 0.68, 0.58, 0.68, 0.73, 0.65 and 0.56, respectively, and RMSE values of 51.1, 53.6, 50.5, 44.9, 51.8 and 54.6, respectively. Comparing with the MCD12Q2 phenology, the satellite-retrieved 30 m fine-resolution LSP of the proposed framework can obtain more information on the land surface, such as rivers, ridges and valleys, which is valuable for phenology-related studies. The proposed framework can yield robust fine-resolution LSP at a large-scale, and the results have great potential for application into studies addressing problems in the ecological environmental at a large scale.

Desert encroachment significantly threatens the living and activity space of humanity, and undertaking human-directed vegetation restoration is one of the effective ways to prevent desert expansion. In the process of desert vegetation restoration, counting the number of tree saplings for rapidly assessing the survival rate of vegetation (such as Haloxylon ammodendron) is a critical task within the restoration process. However, traditional ground-based statistical methods are resource-intensive and time-consuming. This paper proposed a novel unsupervised fine segmentation framework driven by Grounding DINO prompt generation and optimization segment anything model, termed GDPGO-SAM, designed for the segmentation of desert vegetation from UAV-derived remote sensing imagery, thereby facilitating the rapid inventory of tree saplings counts. The framework combines the Grounding DINO object detector and the pre-trained visual model SAM, employing a task-prior-based prompt optimization mechanism to effectively capture the innate features of desert vegetation. This method achieves zero-sample instance segmentation of desert vegetation with an overall accuracy (OA) of 96.56%, a mean Intersection over Union (mIoU) of 81.50%, and a kappa coefficient (kappa) of 0.782, successfully overcoming the limitations of traditional supervised models that rely on passive memorization rather than true recognition. This research significantly enhances the precision of vegetation extraction and canopy depiction, providing strong support for the management of desert vegetation restoration and combating desert expansion.

Plastic greenhouses (PGs) play a vital role in modern agricultural development by providing a controlled environment for the cultivation of food crops. Their widespread adoption has the potential to revolutionize agriculture and impact the local environment. Accurate mapping and estimation of PG coverage are critical for strategic planning in agriculture. However, the challenge lies in the extraction of small and densely distributed PGs; this is often compounded by issues like irrelevant and redundant features and spectral confusion in high-resolution remote-sensing imagery, such as Gaofen-2 data. This paper proposes an innovative approach that combines the power of a full convolutional network (FC-DenseNet103) with an image enhancement index. The image enhancement index effectively accentuates the boundary features of PGs in Gaofen-2 satellite images, enhancing the unique spectral characteristics of PGs. FC-DenseNet103, known for its robust feature propagation and extensive feature reuse, complements this by addressing challenges related to feature fusion and misclassification at the boundaries of PGs and adjacent features. The results demonstrate the effectiveness of this approach. By incorporating the image enhancement index into the DenseNet103 model, the proposed method successfully eliminates issues related to the fusion and misclassification of PG boundaries and adjacent features. The proposed method, known as DenseNet103 (Index), excels in extracting the integrity of PGs, especially in cases involving small and densely packed plastic sheds. Moreover, it holds the potential for large-scale digital mapping of PG coverage. In conclusion, the proposed method providing a practical and versatile tool for a wide range of applications related to the monitoring and evaluation of PGs, which can help to improve the precision of agricultural management and quantitative environmental assessment.