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.

Globally shaded leaves contribute to more than a half of the total increase in gross primary production (GPP; 7.6 Pg C) for 1982–2016. During 1982–2016, the fraction of shaded GPP increases by 1.1% (p < 0.01) in tropical forests and decreases by 1.4% (p < 0.01) and 1.8% (p < 0.01) in evergreen needleleaf and deciduous needleleaf boreal forests, respectively, suggesting an ecological niche of certain canopy structure for ecosystems to achieve maximum GPP. Unlike transpiration from sunlit leaves that has a turning point in the trend in 2003, global transpiration from shaded leaves steadily increased at the rate of 34 km³/year (p < 0.0001) during 1982–2016. Our study therefore suggests that shaded leaves have an increasing role in buffering the adverse impact of climate change and extremes. Further studies are still needed to reduce the uncertainties in reported trends arisen from climate forcing data, leaf area index, and land cover and land change products.

The leaf chlorophyll content (LCC) is an important plant physiological trait and is critical for accurate modeling of vegetation photosynthesis over time and space. To date, there is still a lack of a global long time-series dataset of LCC. In this study, we developed an algorithm to retrieve global LCC from Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data from 2000 to 2020. An essential requirement for generating LCC time series is to capture its seasonal dynamics. This issue was addressed by using a matrix system with two pairs of vegetation indices to minimize the impacts of leaf area index and canopy nonphotosynthetic material on LCC estimation in different seasons. The matrix system algorithm was applied to Landsat data and MODIS data, respectively. The validation based on Landsat data and ground measurements reveals that the algorithm has the ability to catch the seasonal variations of LCC in different plant functional types, and the MODIS-derived LCC shows good agreement with Landsat-upscaled LCC ( $R^{2}=0.77$ and RMSE = $6.9 \,\mu \text{g}$ /cm2). The global eight-day LCC data at 500-m resolution in 2000–2020 were generated using the matrix system from MODIS and presented distinct temporal and spatial variations, which provides a new opportunity for analyzing vegetation physiological dynamics in climate change studies.

In the Indian monsoon region, frequent cloud cover in the rainy season results in less valid satellite observations during the vegetation growth period, making it difficult to extract land surface phenology (LSP). Even worse, many valid but humid observations were misidentified as clouds in the MODIS cloud mask, causing severe gaps in the LSP product. Using a refined cloud detection approach to separate clear-sky and cloudy observations, this study found that potentially valid observations during the vegetation growth period could be identified. Furthermore, the varied vegetation growth trajectories cannot be well-fitted by a global curve-fitting approach, but can be modelled by using the locally adjusted cubic-spline capping approach, which performed well for any seasonal patterns. Applying this approach, the start of growing season (SOS) was determined with 9.18% of vegetation growth amplitude between the maximum and minimum NDVI to generate the SOS product (2000–2016). The valid percentage of this regional product largely increased from 29.30% to 69.76% compared with the MCD12Q2 product, and its reliability was approximate to that of deciduous broadleaf forest in North America and Europe. This product could serve as a basis for understanding the response of terrestrial ecosystems to the changing Indian monsoon.

Important Agricultural Heritage Systems (IAHS) are complex adaptive systems that are multidimensional and exhibit strategic values of sustainability. In the context of modernization, IAHS have been increasingly challenged. Thus, the heritage sites must explore dynamic conservation approaches and formulate adaptive management strategies. However, few studies have provided either theoretical methods or practical cases as support. In this study, we put forward an analytical framework for IAHS conservation and development measure formulation based on the Driver –Pressure–State–Impact–Response (DPSIR) model and applied it to the Shexian Dryland Stone Terraced System (SDSTS). With this framework, we analyzed the supportive mechanisms for the stable state of the SDSTS, diagnosed the threats and challenges it faces, and then formulated 34 conservation and development measures from six aspects, namely management mechanism construction, ecological conservation, cultural inheritance, green and organic product development, sustainable tourism development, and capacity building. It is demonstrated that the DPSIR-based analytical framework can provide both theoretical and technical support for the formulation of IAHS conservation and development measures, thus having the potential to become a general tool for the dynamic conservation and sustainable development of IAHS.