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As the largest carbon reservoir within terrestrial ecosystems, forest ecosystems play a major role as carbon sinks in the global carbon cycle. There are still some uncertainties regarding the responses of different carbon fluxes to environmental changes in cold temperate climate forest ecosystems. Here, 14 cold temperate forest flux sites for at least ten years were investigated, including carbon fluxes and environmental variables such as temperature, precipitation, shortwave radiation, and vapor pressure deficit. By calculating the Spearman correlation coefficient, there was a congruence between photosynthetic productivity (i.e., gross primary productivity, GPP) and carbon sequestration (i.e., net ecosystem productivity, NEP) at thirteen forest sites, and at one forest site, GPP and NEP were decoupled. Annual GPP and NEP displayed a consistent trend when temperature and precipitation had significantly opposite trends and when temperature had a significantly positive correlation with VPD. But when VPD was significantly negatively correlated with both temperature and SW in spring and when temperature was negatively correlated with both SW and VPD in summer, a decoupling of GPP and NEP occurred. The impacts of various environmental factors on the annual carbon fluxes were calculated for each year and season using the path analysis method. At forest sites with consistent trends in GPP and NEP, annual, spring, and summer temperatures had significant positive correlations with GPP and ecosystem respiration (RE). While at the decoupled forest site, environmental factors had a stronger effect on RE, which then contributed to the observed decoupling of GPP and NEP. Finally, the Partial Least Squares method was used to analyze the relative contribution of each environmental factor to annual carbon fluxes. The results revealed that temperature and summer precipitation were the key environmental factors affecting forest ecosystems. This study provides important insights into the different responses of carbon fluxes in forest ecosystems undergoing environmental changes. [ABSTRACT FROM AUTHOR]

There are challenges for cloud detection over bright surfaces, especially for Sentinel-2 image without thermal band. In this paper, we developed a new cloud detection algorithm for Sentinel-2 data designed to tackle the challenges of cloud detection on bright targets. Our algorithm (called RF-NDTeI-SNIC) was built through the integration of Random Forest (RF) model, Normalized Difference Temporal Index (NDTeI) cloud index and Simple Non-Iterative Clustering (SNIC) image segmentation algorithm. First, RF and NDTeI were used to derive the initial and second stage's cloud detection results, respectively. Then, the RF- and NDTeI-based results were fused to remove the bright surfaces and land cover changes. Finally, the fusion results were further refined using SNIC for morphological processing. The proposed RF-NDTeI-SNIC algorithm was evaluated using nine challenging cloud-covered Sentinel-2 scenes with substantial bright surfaces. Results indicated that the average overall accuracy of our algorithm was 95.01%, with average commission rate of 10.19% for cloud. In addition, five commonly used Sentinel-2 cloud mask algorithms including s2cloudless, QA, Fmask, CDI and Tmask were selected for comparative analysis. Results suggested that our algorithm outperformed others with overall accuracies of 89.60–94.17% and cloud commission rate of 9.46–37.50%, and have significant advantages in terms of bright surfaces. In summary, the RF-NDTeI-SNIC algorithm we developed was capable of yielding accurate Sentinel-2 cloud masks, and the novel NDTeI cloud index we proposed gave a new and effective approach for improving the separability of clouds and bright surfaces. [ABSTRACT FROM AUTHOR]

Satellite-based solar-induced chlorophyll fluorescence (SIF) serves as a valuable proxy for monitoring the photosynthesis of vegetation globally. The Global Ozone Monitoring Experiment-2A (GOME-2A) SIF product has gained widespread popularity, particularly due to its extensive global coverage since 2007. However, serious temporal degradation of the GOME-2A instrument is a problem, and there is currently a lack of time-consistent GOME-2A SIF products that meet the needs of temporal trend analysis. In this paper, the GOME-2A instrument's temporal degradation was first calibrated using a pseudo-invariant method, which revealed 16.21 % degradation of the GOME-2A radiance at the near-infrared (NIR) band from 2007 to 2021. Based on the calibration results, the temporal degradation of the GOME-2A radiance spectra was successfully corrected by using a fitted quadratic polynomial function whose determination coefficient (R2) was 0.851. Next, a data-driven algorithm was applied for SIF retrieval at the 735–758 nm window. Also, a photosynthetically active radiation (PAR)-based upscaling model was employed to upscale the instantaneous clear-sky observations to monthly average values to compensate for the changes in cloud conditions and atmospheric scattering. Accordingly, a global temporally consistent GOME-2A SIF dataset (TCSIF) for 2007 to 2021 with the correction of temporal degradation was successfully generated, and the spatiotemporal pattern of global SIF was then investigated. Corresponding trend maps of the global temporally consistent GOME-2A SIF showed that 62.91 % of vegetated regions underwent an increase in SIF, and the global annual averaged SIF exhibited a trend of increasing by 0.70 % yr -1 during the 2007–2021 period. The TCSIF dataset is available at https://doi.org/10.5281/zenodo.8242928 (Zou et al., 2023). [ABSTRACT FROM AUTHOR]

Previous studies on global carbon emissions from forest loss have been marked by great discrepancies due to uncertainties regarding the lost area and the densities of different carbon pools. In this study, we employed a new global 30 m land cover dynamic dataset (GLC_FCS30D) to improve the assessment of forest loss areas; then, we combined multi-sourced carbon stock products to enhance the information on carbon density. Afterwards, we estimated the global carbon emissions from forest loss over the period of 1985–2020 based on the method recommended by the Intergovernmental Panel on Climate Change Guidelines (IPCC). The results indicate that global forest loss continued to accelerate over the past 35 years, totaling about 582.17 Mha and leading to total committed carbon emissions of 35.22 ± 9.38 PgC. Tropical zones dominated global carbon emissions (~2/3) due to their higher carbon density and greater forest loss. Furthermore, global emissions more than doubled in the period of 2015–2020 (1.77 ± 0.44 PgC/yr) compared to those in 1985–2000 (0.69 ± 0.21 PgC/yr). Notably, the forest loss at high altitudes (i.e., above 1000 m) more than tripled in mountainous regions, resulting in more pronounced carbon emissions in these areas. Therefore, the accelerating trend of global carbon emissions from forest loss indicates that great challenges still remain for achieving the COP 26 Declaration to halt forest loss by 2030. [ABSTRACT FROM AUTHOR]

Land cover change information plays an indispensable role in environmental monitoring, climate change research, agricultural planning, urban development, biodiversity conservation, and natural disaster risk assessment. Recently, the free access of Landsat imagery and improvement of computation capacity especially supported by Google Earth Engine platform provides great chance in time-series land-cover change monitoring. We used the stratified land-cover monitoring strategy and time-series Landsat imagery to develop a novel global 30 m land-cover dynamic product with fine classification system from 1985 to 2022 (GLC_FCS30D). Firstly, we used the multitemporal classification to generate the time-series impervious surfaces, wetlands and tidal flat products. Then, we proposed to combine the continuous change detection algorithm and local adaptive updating model to capture the land-cover changes, and to generate a new global 30 m land-cover dynamic product (impervious surfaces, wetlands and tidal flat types were excluded in this step). Next, after overlapping the three multitemporal classification products and the time-series dynamical land-cover dataset, the novel GLC_FCS30D was developed, which contained 35 fine land-cover types. Lastly, using the global 84526 validation points in 2020, the GLC_FCS30D was validated to show the great performance with an overall accuracy of 80.88%, and had obvious advantages over other global land-cover products in diversity of land-cover types and mapping accuracy. [ABSTRACT FROM AUTHOR]