Global climate warming has profoundly affected terrestrial ecosystems. The Tibetan Plateau (TP) is an ecologically vulnerable region that emerged as an ideal place for investigating the mechanisms of vegetation response to climate change. In this study, we constructed an annual synthetic NDVI dataset with 500 m resolution based on MOD13A1 products from 2000 to 2021, which were extracted by the Google Earth Engine (GEE) and processed by the Kalman filter. Furthermore, considering topographic and climatic factors, a thorough analysis was conducted to ascertain the causes and effects of the NDVI’s spatiotemporal variations on the TP. The main findings are: (1) The vegetation coverage on the TP has been growing slowly over the past 22 years at a rate of 0.0134/10a, with a notable heterogeneity due to its topography and climate conditions. (2) During the study period, the TP generally showed a “warming and humidification” trend. The influence of human activities on vegetation growth has exhibited a favorable trajectory, with a notable acceleration observed since 2011. (3) The primary factor influencing NDVI in the southeastern and western regions of the TP was the increasing temperature. Conversely, vegetation in the northeastern and central regions was mostly regulated by precipitation. (4) Combined with the principal component analysis, a PCA-CNN-LSTM (PCL) model demonstrated significant superiority in modeling NDVI sequences on the Tibetan Plateau. Understanding the results of this paper is important for the sustainable development and the formulation of ecological policies on the Tibetan Plateau.

Cropland serves as the most vital resource for agricultural production, while its security is primarily threatened by abandonment. Northeast Guangdong Province features a fragmented terrain and faces a significant issue of farmland abandonment. It is crucial to analyze the phenomenon of cropland abandonment to safeguard food security. However, due to limitations in data sources and attribution methods, previous studies struggled to comprehensively characterize the driving mechanisms of abandoned land. Using data from Sentinel time series remote-sensing images, we employed the land use change trajectory method to map cropland abandonment in Jiaoling County from 2019 to 2023. Furthermore, we proposed a novel analytical framework to quantify the influence pathways and interaction effects driving cropland abandonment. The results indicated that: (1) The overall accuracy of the abandoned land extraction was 79.6%. During the study period, the abandonment rate in Jiaoling County showed a trend of a “gradual rise followed by a sharp decline”, and the abandoned area reached its maximum in 2021. The abandonment phenomenon in the southeastern rural areas was serious and stubborn. (2) The slope has the greatest explanatory power for abandonment, followed by the total cultivated area, aggregation index of cropland, and distance to road. Each driving factor has a threshold effect. (3) Topography, location, and agriculture driving factors directly or indirectly affect the abandonment rate, with direct influences of 0.247, 0.255, and −0.256, respectively. The research findings offer valuable scientific guidance for managing abandoned land and deepen our understanding of its formation mechanisms.

The Hubao–Egyu Urban Agglomeration (HBEY) was a crucial ecological barrier in northern China. To accurately assess the impact of climate change on vegetation growth, it is essential to consider the effects of time lag and accumulation. In this study, we used a newly proposed kernel Normalized Difference Vegetation Index (kNDVI) as the metric for vegetation condition, and employed partial correlation analysis to ascertain the lag and accumulation period of vegetation response to climate by considering different scenarios (No/Lag/Acc/LagAcc) and various combinations. Moreover, we further modified the traditional residual analysis model. The results are as follows: (1) From 2000 to 2022, the HBEY experienced extensive and persistent greening, with a kNDVI slope of 0.0163/decade. Precipitation was identified as the dominant climatic factor influencing vegetation dynamics. (2) In HBEY, the lag effect of temperature was most distinct, particularly affecting the vegetation in cropland and grassland. The accumulation effect of precipitation was pronounced in grassland. (3) Incorporating lag and accumulation effects into models increases the explanatory power of climate impacts on vegetation dynamics by 6.95% compared to traditional residual models. Our findings hold essential implications for regional ecological regulation and climate change response research.

Extreme climatic events can alter temperatures and water availability and thereby suppress vegetation productivity. On the Huang–Huai–Hai (HHH) Plain, China’s key agricultural belt, July 2023 featured widespread heat, spatially heterogeneous drying, and limited spatial co-occurrence of heat and root zone soil drought; however, its productivity response and within-season recovery remain unclear. Here, we combine satellite-derived solar-induced chlorophyll fluorescence (SIF), MODIS gross primary productivity (GPP), environmental variables, and vegetation and elevation stratification and use extreme gradient boosting (XGBoost) with spatial block validation and SHapley Additive exPlanations (SHAP) to quantify July productivity anomalies relative to the same months during 2018–2022, identify affected pixels using a standardized SIF anomaly based on the same reference period (z_i ≤ −1.5), track recovery from August to October, and evaluate environmental associations with SIF. In July 2023, 59.6% of vegetated area exceeded its local July 90th-percentile temperature. Area-weighted anomalies were −0.0129 W m⁻² μm⁻¹ sr⁻¹ for SIF, −0.4853 g C m⁻² d⁻¹ for GPP, and −0.0147 m³ m⁻³ for root zone soil moisture (SMrz). Negative anomalies occurred in 57.3% of vegetated pixels for SIF and 73.4% for GPP. Among deciduous broadleaf forest (DBF), grasslands (GRA), and croplands (CRO), GRA had the largest mean SIF loss, whereas CRO had the smallest despite widespread local declines. Losses above 1000 m exceeded those at 0–500 m. Of 2833 affected pixels, 85.6% returned to non-negative SIF anomalies by October, while recovery above 1000 m was 75.6%. Within the July models, SMrz contained the largest individual predictive contribution across classes, although the combined contribution of air temperature, vapor pressure deficit, and downward shortwave radiation was comparable. In the recovery model, recovery stage, initial July loss, and radiation and atmospheric demand contained substantial predictive information. Together, the results characterize spatially uneven productivity loss and recovery and the environmental conditions associated with these differences.