Widespread decline in winds delayed autumn foliar senescence over high latitudes.

The high northern latitudes (>50°) experienced a pronounced surface stilling (i.e., decline in winds) with climate change. As a drying factor, the influences of changes in winds on the date of autumn foliar senescence (DFS) remain largely unknown and are potentially important as a mechanism explaining the interannual variability of autumn phenology. Using 183,448 phenological observations at 2,405 sites, long-term site-scale water vapor and carbon dioxide flux measurements, and 34 y of satellite greenness data, here we show that the decline in winds is significantly associated with extended DFS and could have a relative importance comparable with temperature and precipitation effects in contributing to the DFS trends. We further demonstrate that decline in winds reduces evapotranspiration, which results in less soil water losses and consequently more favorable growth conditions in late autumn. In addition, declining winds also lead to less leaf abscission damage which could delay leaf senescence and to a decreased cooling effect and therefore less frost damage. Our results are potentially useful for carbon flux modeling because an improved algorithm based on these findings projected overall widespread earlier DFS than currently expected by the end of this century, contributing potentially to a positive feedback to climate.

Diverse photosynthetic capacity of global ecosystems mapped by satellite chlorophyll fluorescence measurements.

Photosynthetic capacity is often quantified by the Rubisco-limited photosynthetic capacity (i.e. maximum carboxylation rate, Vcmax). It is a key plant functional trait that is widely used in Earth System Models for simulation of the global carbon and water cycles. Measuring Vcmax is time-consuming and laborious; therefore, the spatiotemporal distribution of Vcmax is still poorly understood due to limited measurements of Vcmax. In this study, we used a data assimilation approach to map the spatial variation of Vcmax for global terrestrial ecosystems from a 11-year-long satellite-observed solar-induced chlorophyll fluorescence (SIF) record. In this SIF-derived Vcmax map, the mean Vcmax value for each plant function type (PFT) is found to be comparable to a widely used N-derived Vcmax dataset by Kattge et al. (2009). The gradient of Vcmax along PFTs is clearly revealed even without land cover information as an input. Large seasonal and spatial variations of Vcmax are found within each PFT, especially for diverse crop rotation systems. The distribution of major crop belts, characterized with high Vcmax values, is highlighted in this Vcmax map. Legume plants are characterized with high Vcmax values. This Vcmax map also clearly illustrates the emerging soybean revolution in South America where Vcmax is the highest among the world. The gradient of Vcmax in Amazon is found to follow the transition of soil types with different soil N and P contents. This study suggests that satellite-observed SIF is powerful in deriving the important plant functional trait, i.e. Vcmax, for global climate change studies.

Detection and attribution of vegetation greening trend in China over the last 30 years.

The reliable detection and attribution of changes in vegetation growth is a prerequisite for the development of strategies for the sustainable management of ecosystems. This is an extraordinary challenge. To our knowledge, this study is the first to comprehensively detect and attribute a greening trend in China over the last three decades. We use three different satellite-derived Leaf Area Index (LAI) datasets for detection as well as five different process-based ecosystem models for attribution. Rising atmospheric CO2 concentration and nitrogen deposition are identified as the most likely causes of the greening trend in China, explaining 85% and 41% of the average growing-season LAI trend (LAIGS) estimated by satellite datasets (average trend of 0.0070 yr(-1), ranging from 0.0035 yr(-1) to 0.0127 yr(-1)), respectively. The contribution of nitrogen deposition is more clearly seen in southern China than in the north of the country. Models disagree about the contribution of climate change alone to the trend in LAIGS at the country scale (one model shows a significant increasing trend, whereas two others show significant decreasing trends). However, the models generally agree on the negative impacts of climate change in north China and Inner Mongolia and the positive impact in the Qinghai-Xizang plateau. Provincial forest area change tends to be significantly correlated with the trend of LAIGS (P < 0.05), and marginally significantly (P = 0.07) correlated with the residual of LAIGS trend, calculated as the trend observed by satellite minus that estimated by models through considering the effects of climate change, rising CO2 concentration and nitrogen deposition, across different provinces. This result highlights the important role of China's afforestation program in explaining the spatial patterns of trend in vegetation growth.

Comparison of sampling designs for estimating deforestation from landsat TM and MODIS imagery: a case study in Mato Grosso, Brazil.

Sampling designs are commonly used to estimate deforestation over large areas, but comparisons between different sampling strategies are required. Using PRODES deforestation data as a reference, deforestation in the state of Mato Grosso in Brazil from 2005 to 2006 is evaluated using Landsat imagery and a nearly synchronous MODIS dataset. The MODIS-derived deforestation is used to assist in sampling and extrapolation. Three sampling designs are compared according to the estimated deforestation of the entire study area based on simple extrapolation and linear regression models. The results show that stratified sampling for strata construction and sample allocation using the MODIS-derived deforestation hotspots provided more precise estimations than simple random and systematic sampling. Moreover, the relationship between the MODIS-derived and TM-derived deforestation provides a precise estimate of the total deforestation area as well as the distribution of deforestation in each block.

Validation and refinement of cropland map in southwestern China by harnessing ten contemporary datasets.

Accurate cropland map serves as the cornerstone of effective agricultural monitoring. Despite the continuous enrichment of remotely sensed cropland maps, pervasive inconsistencies have impeded their further application. This issue is particularly evident in areas with limited valid observations, such as southwestern China, which is characterized by its complex topography and fragmented parcels. In this study, we constructed multi-sourced samples independent of the data producers, taking advantage of open-source validation datasets and sampling to rectify the accuracy of ten contemporary cropland maps in southwestern China, decoded their inconsistencies, and generated a refined cropland map (CroplandSyn) by leveraging ten state-of-the-art remotely sensed cropland maps released from 2021 onwards using the self-adaptive threshold method. Validations, conducted at both prefecture and county scales, underscored the superiority of the refined cropland map, aligning more closely with national land survey data. The refined cropland map and samples are publicly available to users. Our study offers valuable insights for improving agricultural practices and land management in under-monitored areas by providing high-quality cropland maps and validation datasets.

Comparative assessment of leaf photosynthetic capacity datasets for estimating terrestrial gross primary productivity.

The maximum Rubisco carboxylation rate normalized to 25 °C (Vcmax25) is a key parameter in terrestrial biosphere models for simulating carbon cycling. Recently, global distributions of Vcmax25 have been derived through various methods and different data, including field measurements, ecological optimality theory (EOT), leaf chlorophyll content (LCC), and solar-induced chlorophyll fluorescence (SIF). However, direct validation poses challenges due to high uncertainty arising from limited ground-based observations. This study conducted an indirect evaluation of four Vcmax25 datasets by assessing the accuracy of gross primary productivity (GPP) simulated using the Biosphere-atmosphere Exchange Process Simulator (BEPS) at both site and global scales. Results indicate that, compared to utilizing Vcmax25 fixed by plant functional types (PFT) derived from field measurements, incorporating Vcmax25 derived from SIF and LCC (SIF + LCC), or solely LCC, into BEPS significantly reduces simulated errors in the annual total GPP, with a 23.2 %-25.1 % decrease in the average absolute bias across 196 FLUXNET2015 sites. Daily GPP for evergreen needleleaf forests, deciduous broadleaf forests, shrublands, grasslands, and croplands shows a 7.8 %-27.6 % decrease in absolute bias, primarily attributed to reduced simulation errors during off-peak seasons of vegetation growth. Conversely, the annual total GPP error simulated using EOT-derived Vcmax25 increases slightly (2.2 %) compared to that simulated using PFT-fixed Vcmax25. This is primarily due to a significant overestimation in evergreen broadleaf forests and underestimation in croplands, despite slight increased accuracy for other PFTs. The global annual GPP simulated using Vcmax25 with seasonal variations (i.e., LCC Vcmax25 and SIF + LCC Vcmax25) yields a 4.3 %-7.3 % decrease compared to that simulated using PFT-fixed Vcmax25. Compared to FLUXCOM and GOSIF GPP products, the GPP simulated based on SIF + LCC Vcmax25 and LCC Vcmax25 demonstrates better consistency (R2 = 0.91-0.93, RMSE = 314.2-376.6 g C m-2 yr-1). This study underscores the importance of accurately characterizing the spatiotemporal variations in Vcmax25 for the accurate simulation of global vegetation productivity.

Assessment of effects of climate change and grazing activity on grassland yield in the Three Rivers Headwaters Region of Qinghai-Tibet Plateau, China.

Inter-annual dynamics of grassland yield of the Three Rivers Headwaters Region of Qinghai-Tibet Plateau of China in 1988-2005 was analyzed using the GLO-PEM model, and the herbage supply function was evaluated. The results indicate that while grassland yield in the region showed marked inter-annual fluctuation there was a trend of increased yield over the 18 years of the study. This increase was especially marked for Alpine Desert and Alpine Steppe and in the west of the region. The inter-annual coefficient of variation of productivity increased from the east to the west of the region and from Marsh, Alpine Meadow, Alpine Steppe, Temperate Steppe to Alpine Desert grasslands. Climate change, particularly increased temperatures in the region during the study period, is suggested to be the main cause of increased grassland yield. However, reduced grazing pressure and changes to the seasonal pattern of grazing could also have influenced the grassland yield trend. These findings indicate the importance of understanding the function of the grassland ecosystems in the region and the effect of climate change on them especially in regard to their use to supply forage for animal production. Reduction of grazing pressure, especially during winter, is indicated to be critical for the restoration and sustainable use of grassland ecosystems in the region.

Continuous monitoring of lake dynamics on the Mongolian Plateau using all available Landsat imagery and Google Earth Engine.

Lakes are important water resources on the Mongolian Plateau (MP) for human's livelihood and production as well as maintaining ecosystem services. Previous studies, based on the Landsat-based analyses at epoch scale and visual interpretation approach, have reported a significant loss in the lake areas and numbers, especially from the late 1990s to 2010. Given the remarkable inter- and intra-annual variations of lakes in the arid and semi-arid region, a comprehensive picture of annual lake dynamics is needed. Here we took advantages of the power of all the available Landsat images and the cloud computing platform Google Earth Engine (GEE) to map water body for each scene, and then extracted lakes by post-processing including raster-to-vector conversion and separation of lakes and rivers. Continuous dynamics of the lakes over 1 km2 was monitored annually on the MP from 1991 to 2017. We found a significant shrinkage in the lake areas and numbers of the MP from 1991 to 2009, then the decreasing lakes on the MP have recovered since circa 2009. Specifically, Inner Mongolia of China experienced more dramatic lake variations than Mongolia. A few administrative regions with huge lakes, including Hulunbuir and Xilin Gol in Inner Mongolia and Ubsa in Mongolia, dominated the lake area variations in the study area, suggesting that the prior treatments on these major lakes would be critical for water management on the MP. The varied drivers of lake variations in different regions showed the complexity of factors impacting lakes. While both natural and anthropogenic factors significantly affected lake dynamics before 2009, precipitation played increasingly important role for the recovery of lakes on the MP after 2009.

Vegetation structural change since 1981 significantly enhanced the terrestrial carbon sink.

Satellite observations show that leaf area index (LAI) has increased globally since 1981, but the impact of this vegetation structural change on the global terrestrial carbon cycle has not been systematically evaluated. Through process-based diagnostic ecosystem modeling, we find that the increase in LAI alone was responsible for 12.4% of the accumulated terrestrial carbon sink (95 ± 5 Pg C) from 1981 to 2016, whereas other drivers of CO2 fertilization, nitrogen deposition, and climate change (temperature, radiation, and precipitation) contributed to 47.0%, 1.1%, and -28.6% of the sink, respectively. The legacy effects of past changes in these drivers prior to 1981 are responsible for the remaining 65.5% of the accumulated sink from 1981 to 2016. These results refine the attribution of the land sink to the various drivers and would help constrain prognostic models that often have large uncertainties in simulating changes in vegetation and their impacts on the global carbon cycle.