Vegetation phenology has been viewed as the nature's calendar and an integrative indicator of plant‐climate interactions. The correct representation of vegetation phenology is important for models to accurately simulate the exchange of carbon, water, and energy between the vegetated land surface and the atmosphere. Remote sensing has advanced the monitoring of vegetation phenology by providing spatially and temporally continuous data that together with conventional ground observations offers a unique contribution to our knowledge about the environmental impact on ecosystems as well as the ecological adaptations and feedback to global climate change. Land surface phenology (LSP) is defined as the use of satellites to monitor seasonal dynamics in vegetated land surfaces and to estimate phenological transition dates. LSP, as an interdisciplinary subject among remote sensing, ecology, and biometeorology, has undergone rapid development over the past few decades. Recent advances in sensor technologies, as well as data fusion techniques, have enabled novel phenology retrieval algorithms that refine phenology details at even higher spatiotemporal resolutions, providing new insights into ecosystem dynamics. As such, here we summarize the recent advances in LSP and the associated opportunities for science applications. We focus on the remaining challenges, promising techniques, and emerging topics that together we believe will truly form the very frontier of the global LSP research field.

Understanding the driving mechanisms of vegetation development is critical for maintaining terrestrial ecosystem function in mountain areas, especially under the background of climate change. The Qinba Mountains (QBM), a critical north–south transition zone in China, is an environmentally fragile area that is vulnerable to climate change. It is essential to characterize how its ecological environment has changed. Currently, such a characterization remains unclear in the spatiotemporal patterns of the nonlinear effects and interactions between environmental factors and vegetation changes in the QBM. Here, we utilized the Normalized Difference Vegetation Index (NDVI), obtained from Google Earth Engine (GEE) platform, as an indicator of terrestrial ecosystem conditions. Then, we measured the spatiotemporal heterogeneity for vegetation variation in the QBM from 2003 to 2018. Specifically, the Geodetector method, a new geographically statistical method without linear assumptions, was employed to detect the interaction between vegetation and environmental driving factors. The results indicated that there is a trend of a general increase in vegetation growth amplitude (the average NDVI increased from 0.810 to 0.858). The areas with an NDVI greater than 0.8 are mainly distributed in the Qinling Mountains and the Daba Mountains, which account for more than 76.39% of the QBM area. For the entire region, the global Moran’s index of the NDVI is greater than 0.95, indicating that vegetation is highly concentrated in the spatial domain. The Geodetector identified that landform type was the primary factor in controlling vegetation changes, contributing 24.19% to the total variation, while the explanatory powers of the aridity index and the wetness index for vegetation changes were 22.49% and 21.47%, respectively. Furthermore, the interaction effects between any two factors outperformed the influence of a single environmental variable. The interaction between air temperature and the aridity index was the most significant element, contributing to 47.10% of the vegetation variation. These findings can not only improve our understanding in the interactive effects of environmental forces on vegetation change, but also be a valuable reference for ecosystem management in the QBM area, such as ecological conservation planning and the assessment of ecosystem functions.

With the rapid development of remote sensing technologies, the frequency of observations of the same location is increasing, and many satellites and sensors produced a large amount of time series images. These images make long-term change detection and dynamic characteristic estimation of ground features possible. However, conventional remote sensing image change detection methods mostly rely on manual visual interpretation and supervised or unsupervised computer-aided classification. Traditional methods always face many bottlenecks when processing big and fast-growing datasets, such as low computational efficiency, low level of automation, and different identification standards and accuracies caused by different operators. With the rapid accumulation of remote sensing data, it has become an important but more challenging task to conduct change detection in a more precise, automated and standardized way. The development of geointelligent computing technologies provides a means of solving these problems and improve the accuracy and efficiency of remote sensing image change detection. In this paper, we presented a novel deep learning model called nest network(NestNet) based on a convolutional neural network to improve the accuracy of the automatic change detection task by using remotely sensed time series images. NestNet extracts the respective features of bi-temporal images using an encoding parallel module and subsequently employs absolute different operations to process the features of two images. Compared with change detection method based on U-Shaped network plus plus (UNet++), the parallel module improves the efficiency of NestNet. Finally, a decoding module is used to generate a predicted change image. This paper compares NestNet to traditional methods and state-of-the-art deep learning models on two datasets. The experimental results demonstrate that the accuracy of NestNet is better than that of state-of-the-art methods. It can be concluded that the NestNet model is a potential approach for change detection using high resolution remote sensing images.

With the economic growth and increasing urbanization in the last three decades, the air quality over China has continuously degraded, which poses a great threat to human health. The concentration of fine particulate matter (PM_2.5) directly affects the mortality of people living in the polluted areas where air quality is poor. The Beijing-Tianjin-Hebei (BTH) region, one of the well organized urban regions in northern China, has suffered with poor air quality and atmospheric pollution due to recent growth of the industrial sector and vehicle emissions. In the present study, we used the back propagation neural network model approach to estimate the spatial distribution of PM_2.5 concentration in the BTH region for the period January 2014–December 2016, combining the satellite-derived aerosol optical depth (S-DAOD) and meteorological data. The results were validated using the ground PM_2.5 data. The general method including all PM_2.5 training data and 10-fold cross-method have been used for validation for PM_2.5 estimation (R² = 0.68, RMSE = 20.99 for general validation; R² = 0.54, RMSE = 24.13 for cross-method validation). The study provides a new approach to monitoring the distribution of PM_2.5 concentration. The results discussed in the present paper will be of great help to government agencies in developing and implementing environmental conservation policy.

In the context of global climate change and the increase in drought frequency, monitoring and accurately assessing the impact of hydrological process limitations on vegetation growth is of paramount importance. Our study undertakes a comprehensive evaluation of the efficacy of satellite remote sensing vegetation indices—Normalized Difference Vegetation Index (MODIS NDVI product), kernel NDVI (kNDVI), and Solar-Induced chlorophyll Fluorescence (GOSIF product) in this regard. Initially, we applied the LightGBM-Shapley additive explanation framework to assess the influencing factors on the three vegetation indices. We found that Vapor Pressure Deficit (VPD) is the primary factor affecting vegetation in southern China (18°–30°N). Subsequently, using Gross Primary Productivity (GPP) estimates from flux tower sites as a performance benchmark, we evaluated the ability of these vegetation indices to accurately reflect vegetation GPP changes during drought conditions. Our findings indicate that SIF serves as the most effective surrogate for GPP, capturing the variability of GPP during drought periods with minimal time lag. Additionally, our study reveals that the performance of kNDVI significantly varies depending on the estimation of different kernel parameters. The application of a time-heuristic estimation method could potentially enhance kNDVI’s capacity to capture GPP dynamics more effectively during drought periods. Overall, this study demonstrates that satellite-based SIF data are more adept at monitoring vegetation responses to water stress and accurately tracking GPP anomalies caused by droughts. These findings not only provide critical insights into the selection and optimization of remote sensing vegetation product but also offer a valuable framework for future research aimed at improving our monitoring and understanding of vegetation growth status under climatic changes.

Global climate change has led to significant changes in seasonal rhythm events of vegetation growth, such as spring onset and autumn senescence. Spatiotemporal shifts in these vegetation phenological metrics have been widely reported over the globe. Vegetation growth peak represents plant photosynthesis capacity and responds to climate change. At present, spatiotemporal changes in vegetation growth peak characteristics (timing and maximum growth magnitude) and their underlying governing mechanisms remain unclear at regional scales. In this study, the spatiotemporal dynamics of vegetation growth peak in northeast China (NEC) was investigated using long-term NDVI time series. Then, the effects of climatic factors and spring phenology on vegetation growth peak were examined. Finally, the contribution of growth peak to vegetation production variability was estimated. The results of the phenological analysis indicate that the date of vegetation green up in spring and growth peak in summer generally present a delayed trend, while the amplitude of growth peak shows an increasing trend. There is an underlying cycle of 11 years in the vegetation growth peak of the entire study area. Air temperature and precipitation before the growing season have a small impact on vegetation growth peak amplitude both in its spatial extent and magnitude (mainly over grasslands) but have a significant influence on the date of the growth peak in the forests of the northern area. Spring green-up onset has a more significant impact on growth peak than air temperature and precipitation. Although green-up date plays a more pronounced role in controlling the amplitude of the growth peak in forests and grasslands, it also affects the date of growth peak in croplands. The amplitude of the growth peak has a significant effect on the inter-annual variability of vegetation production. The discrepant patterns of growth peak response to climate and phenology reflect the distinct adaptability of the vegetation growth peak to climate change, and result in different carbon sink patterns over the study area. The study of growth peak could improve our understanding of vegetation photosynthesis activity over various land covers and its contribution to carbon uptake.