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Ongoing global warming has triggered extreme climate events of increasing magnitude and frequency. Under this effect, a series of extreme climate events such as drought and increased rainfall during the El Nino Southern Oscillation (ENSO) are expected to be amplified in the coming years. Adequate mapping of regions with climate-sensitive vegetation and its associated time lag is required for appropriate mitigation planning to avoid potential negative ecological impacts towards vegetation. In this study, ENSO and climate indicator time series data, for example, Multivariate ENSO Index (MEI) and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) data for rainfall were linked with long-term time series vegetation proxies from remote sensing (RS proxies). ENSO- and rainfall-sensitive areas were identified from each RS proxy using the bivariate Granger test, and the areas identified by multiple RS proxies were taken to identify climate-sensitive regions in Indonesia. Of the biome types in Indonesia, savanna was the most sensitive, with approximately 53% of the total savanna area in Indonesia shown to be sensitive to ENSO and rainfall by two or more RS proxies. Rolling correlation analysis also found that the ENSO effect on the vegetation region after rainfall was positively correlated with the RS proxies with a time lag of +5 months. Therefore, rainfall can be taken as a proxy of the effects of ENSO on the temporal dynamics of sensitive vegetation regions in Indonesia.

Soil moisture is a key parameter for the exchange of substance and energy at the land-air interface, timely and accurate acquisition of soil moisture is of great significance for drought monitoring, water resource management, and crop yield estimation. Synthetic aperture radar (SAR) is sensitive to soil moisture, but the effects of vegetation on SAR signals poses challenges for soil moisture retrieval in areas covered with vegetation. In this study, based on Sentinel-1 SAR and Sentinel-2 optical remote sensing data, a coupling approach was employed to retrieval surface soil moisture over dense vegetated areas. Different vegetation indices were extracted from Sentinel-2 data to establish the vegetation water content (VWC) estimation model, which was integrated with the Water Cloud Model (WCM) to distinguish the contribution of vegetation layer and soil layer to SAR backscattering signals. Subsequently, the Oh model and the Look-Up Table (LUT) algorithm were used for soil moisture retrieval, and the accuracy of the result was compared with the traditional direct retrieval method. The results indicate that, for densely vegetated surfaces, VWC can be better reflected by multiple vegetation indices including NDVI, NDWI2, NDGI and FVI, the R 2 and RMSE of VWC estimation result is 0.709 and 0.30 kg·m -2 . After vegetation correction, the correlation coefficient increased from 0.659 to 0.802 for the VV polarization, and from 0.398 to 0.509 for the VH polarization. Satisfactory accuracy of soil moisture retrieval result was obtained with the Oh model and the LUT algorithm, VV polarization is found to be more suitable for soil moisture retrieval compared to VH polarization, with an R 2 of 0.672 and an RMSE of 0.048m 3 ·m -3 , the accuracy is higher than that of the direct retrieval method. The results of the study preliminarily verified the feasibility of the coupling method in soil moisture retrieval over densely veg etated surfaces.

Solar-induced chlorophyll fluorescence (SIF) is a crucial proxy of photosynthetic processes in vegetation. In recent decades, advancements in remote sensing technology have facilitated long-term global SIF monitoring, significantly enhancing our understanding of vegetation dynamics on a global scale. Despite this progress, current SIF datasets face major challenges, including temporal inconsistencies among various satellite-derived products and a lack of long-term, high-resolution observations. In this study, we developed a \"Long-term Harmonized SIF\" (LHSIF) dataset spanning 1995 to 2024 with a fine spatial resolution of 0.05° by coordinating SIF satellite observations from GOME, SCIAMACHY, GOME-2, and OCO-2. Light use efficiency (LUE)-based spatial downscaling models were employed for each SIF product to generate fine-resolution global SIF maps. The long-term dataset was constructed using temporally corrected GOME-2A SIF (TCSIF) as a benchmark and was combined with a cumulative distribution function (CDF) normalization method for far-red SIF harmonization across satellite sensors from GOME, SCIAMACHY, and OCO-2. The resulting harmonized dataset shows a 49 % reduction in inter-sensor differences compared to the uncorrected data and exhibits a stable interannual increase of 0.31 ± 0.07 % yr.sup.-1 . This result strongly aligns with the growth rate of gross primary production (GPP, 0.47 ± 0.03 % yr.sup.-1) and is consistent with ground-based SIF observations (R0.60). Therefore, the long-term harmonized SIF dataset with a fine 0.05° resolution is valuable for estimating global photosynthesis over extended periods. The LHSIF dataset is available at

Climate change is expected to impact the functioning of the entire Earth system. However, detecting changes in ecosystem dynamics and attributing such change to anthropogenic climate change has proved difficult. Here we analyse the vegetation dynamics of 100 sites representative of the diversity of terrestrial ecosystem types using remote-sensing data spanning the past 40 years and a dynamic model of plant growth, forced by climate reanalysis data. We detect a change in vegetation activity for all ecosystem types and find these changes can be attributed to trends in climate-system parameters. Ecosystems in dry and warm locations responded primarily to changes in soil moisture, whereas ecosystems in cooler locations responded primarily to changes in temperature. We find that the effects of CO2 fertilization on vegetation are limited, potentially due to masking by other environmental drivers. Observed trend switching is widespread and dominated by shifts from greening to browning, suggesting many of the ecosystems studied are accumulating less carbon. Our study reveals a clear fingerprint of climate change in the change exhibited by terrestrial ecosystems over recent decades.An analysis fusing satellite data with a process-based model of plant growth attributes changes in vegetation activity across terrestrial ecosystems to climatic changes.

Understanding long-term vegetation dynamics is essential for assessing ecosystem responses to climate change, particularly in ecologically sensitive regions like the Tibetan Plateau. While numerous studies have analyzed vegetation changes on the Tibetan Plateau from 1982 to 2015 using remote sensing data, most have been limited by insufficient temporal coverage and low-resolution datasets, constraining the accuracy of trend detection and driver analysis. To address this gap, we utilize a high-resolution Normalized Difference Vegetation Index (NDVI) dataset, generated by merging GIMMS and SPOT data via the Extended Observation Time (EOT) algorithm, to investigate vegetation trends, breakpoints, and their climatic drivers over 34 years, with a specific focus on elevation effects. Our results indicate a predominant greening trend, with NDVI increasing in 86% of the area and browning in 14%, and an average greening rate of 0.0012 per decade. However, this trend varies with elevation: greening is most pronounced below 1000 m, followed by 1000–2000 m and 3000–4000 m, while the weakest greening occurs at 2000–3000 m. Breakpoint analysis reveals major shifts around 1998, with 70.1% of vegetation experiencing abrupt changes between 1996 and 2000, and 59.4% showing their first breakpoint in 1998. The highest NDVI breakpoint rate (27%) is observed at 2000–3000 m. Additionally, we find that temperature exerts a stronger influence on NDVI dynamics than precipitation. These findings underscore the complex interactions between vegetation, elevation, and climate, emphasizing the need for enhanced ecological monitoring and conservation efforts. Future research should incorporate additional climatic variables and improved modeling techniques to refine our understanding of vegetation responses in this high-altitude environment.

Climate change and human activities have already caused degradation in a large fraction of vegetation on the Qinghai‐Tibetan Plateau (QTP). Many studies report that climate variability instead of overgrazing has been the primary cause for large‐scale vegetation cover changes on the QTP, for example, Lehnert et al., 2016, https://doi.org/10.1038/srep24367. However, it remains unclear how human activities (mainly livestock grazing) regulate vegetation dynamics under climate change. This paper takes the AVHRR/GIMMS Normalized Difference Vegetation Index (NDVI) as an indicator to analyze the growth status of vegetation zones in the QTP, which has highly sensitive to climate change. The spatiotemporal dynamics of vegetation growth between 1981 and 2015 were analyzed. The dual effects of climate change and human activities were examined by correlation analyses of data from 87 meteorological stations and economic statistical data of the QTP. Results show that: (a) The vegetation in central and southwestern QTP with high altitudes was improving due to the warm‐humid climate trend. An increase in temperature and a reduction in the harsh frigid climate at high altitudes due to global warming has resulted in expansions of the vegetated areas, with the NDVI showing a concordant increase. (b) The degraded areas were mainly confined to the northern and eastern QTP, which have high human and livestock population densities. In comparison to gently changing climate regimes, anthropogenic activities such as chronic concentration of population and livestock in the valleys with a less harsh climate exerts a much stronger pressure on vegetation. The study indicates that the anthropogenic pressures are much more intensive than the impact of climate change and are critical for the conservation and sustainable management of the QTP vegetation. Plain Language Summary Vegetation dynamics and its type are considered to be critical indicators of different climate regimes and have received significant attention from ecologists and climatologists. However, studies on the shift in vegetation toward higher altitudes and higher latitudes with climate warming from the vegetation zone redistribution perspective are relatively scarce. Our results suggest that the degraded areas of vegetation were mainly confined to the northern and eastern Qinghai‐Tibetan Plateau (QTP), which have high human and livestock population densities. In comparison to gently changing climate regimes, anthropogenic activities such as chronic concentration of population and livestock in the relatively less harsh valleys exerts a much stronger pressure on vegetation. Anthropogenic pressures were therefore found to be far more intensive than the impact of climate change and they were the big threats to the sustainability of the QTP. Key Points The vegetation with high altitudes was improving due to the warm‐humid climate trend The degraded vegetation areas were mainly confined to high human and livestock population densities Anthropogenic activities such as chronic concentration of population and livestock exerts a much stronger pressure on vegetation

Leaf area index (LAI) with an explicit biophysical meaning is a critical variable to characterize terrestrial ecosystems. Long-term global datasets of LAI have served as fundamental data support for monitoring vegetation dynamics and exploring its interactions with other Earth components. However, current LAI products face several limitations associated with spatiotemporal consistency. In this study, we employed the back propagation neural network (BPNN) and a data consolidation method to generate a new version of the half-month 1/12∘ Global Inventory Modeling and Mapping Studies (GIMMS) LAI product, i.e., GIMMS LAI4g, for the period 1982–2020. The significance of the GIMMS LAI4g was the use of the latest PKU GIMMS normalized difference vegetation index (NDVI) product and 3.6 million high-quality global Landsat LAI samples to remove the effects of satellite orbital drift and sensor degradation and to develop spatiotemporally consistent BPNN models. The results showed that the GIMMS LAI4g exhibited overall higher accuracy and lower underestimation than its predecessor (GIMMS LAI3g) and two mainstream LAI products (Global LAnd Surface Satellite (GLASS) LAI and Long-term Global Mapping (GLOBMAP) LAI) using field LAI measurements and Landsat LAI samples. Its validation against Landsat LAI samples revealed an R2 of 0.96, root mean square error of 0.32 m2 m−2, mean absolute error of 0.16 m2 m−2, and mean absolute percentage error of 13.6 % which meets the accuracy target proposed by the Global Climate Observation System. It outperformed other LAI products for most vegetation biomes in a majority area of the land. It efficiently eliminated the effects of satellite orbital drift and sensor degradation and presented a better temporal consistency before and after the year 2000. The consolidation with the reprocessed MODIS LAI allows the GIMMS LAI4g to extend the temporal coverage from 2015 to a recent period (2020), producing the LAI trend that maintains high consistency before and after 2000 and aligns with the reprocessed MODIS LAI trend during the MODIS era. The GIMMS LAI4g product could potentially facilitate mitigating the disagreements between studies of the long-term global vegetation changes and could also benefit the model development in earth and environmental sciences. The GIMMS LAI4g product is open access and available under Attribution 4.0 International at https://doi.org/10.5281/zenodo.7649107 (Cao et al., 2023).

This study assesses the relationships between vegetation dynamics and climatic variations in Pakistan from 2000 to 2023. Employing high-resolution Landsat data for Normalized Difference Vegetation Index (NDVI) assessments, integrated with climate variables from CHIRPS and ERA5 datasets, our approach leverages Google Earth Engine (GEE) for efficient processing. It combines statistical methodologies, including linear regression, Mann–Kendall trend tests, Sen's slope estimator, partial correlation, and cross wavelet transform analyses. The findings highlight significant spatial and temporal variations in NDVI, with an annual increase averaging 0.00197 per year (p < 0.0001). This positive trend is coupled with an increase in precipitation by 0.4801 mm/year (p = 0.0016). In contrast, our analysis recorded a slight decrease in temperature (− 0.01011 °C/year, p < 0.05) and a reduction in solar radiation (− 0.27526 W/m 2 /year, p < 0.05). Notably, cross-wavelet transform analysis underscored significant coherence between NDVI and climatic factors, revealing periods of synchronized fluctuations and distinct lagged relationships. This analysis particularly highlighted precipitation as a primary driver of vegetation growth, illustrating its crucial impact across various Pakistani regions. Moreover, the analysis revealed distinct seasonal patterns, indicating that vegetation health is most responsive during the monsoon season, correlating strongly with peaks in seasonal precipitation. Our investigation has revealed Pakistan's complex association between vegetation health and climatic factors, which varies across different regions. Through cross-wavelet analysis, we have identified distinct coherence and phase relationships that highlight the critical influence of climatic drivers on vegetation patterns. These insights are crucial for developing regional climate adaptation strategies and informing sustainable agricultural and environmental management practices in the face of ongoing climatic changes.