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• Both GPP and GPP max in Chinese grasslands showed increases at different space–time scales. • Trend analysis and breakpoint tests were used for the first time to monitor GPP change simultaneously in Chinese grasslands. • GPP max × CUP is a robust indicator to explain annual-scale variation in GPP. At the ecosystem level, Gross Primary Productivity (GPP) was defined as the organic compounds formed by plants that absorb atmospheric CO 2 through photosynthesis and sequester carbon in plant bodies. How to accurately assess the spatiotemporal evolution of ecosystem carbon indicators has become a critical issue to be addressed. Chinese grasslands are located in a typical arid and semi-arid climate zone and are sensitive to global changes, which will inevitably have serious impacts on the function and structure of Chinese grasslands. Based on this, our paper takes Chinese grassland ecosystems as the research area, and used multi-source GPP dataset from terrestrial ecosystem model simulations and remote sensing satellite observations to quantitatively analyze the spatiotemporal evolution patterns of GPP in Chinese grasslands over the past 40 years. We combined trend analysis and breakpoint test, and then to analyze the mechanism components of GPP space–time variability. The main results are as follows: (1) The model and remote sensing estimated GPP results showed that more than 80% of Chinese grasslands show a significant increasing trend over the past 40 years, with growth rates ranging from 0.68 to 3.13 g C/m−2 year−1. (2) GPP max also shows that more than 80% of Chinese grasslands are growing rapidly, with an overall growth rate of more than 0.1 g C/m−2 year−1 in each region. The overall long-term trends and interannual variability of multi-source GPP and GPP max are generally consistent, yet vegetation dynamics in local areas are still uncertain. (3) The breakpoint test showed that 'monotonically increasing' was the largest breakpoint type of GPP in Chinese grasslands (33.09%), and the direction of change of GPP in Chinese grasslands before and after the breakpoint was also increasing. (4) GPP max × CUP explained 91% of the temporal variability of annual-scale GPP in Chinese grasslands from a mechanistic view, and we found that the peak photosynthetic growth and the length of the phenological period synergistically controlled the interannual variability of GPP in Chinese grasslands. Under the scenario of rapid global change, our study accurately assessed the long-term dynamics of GPP and its mechanism-driven of grassland ecosystems in China, which is helpful for estimating and predicting the carbon budget of grassland ecosystems in China, and has important guiding significance for policy formulation to mitigate climate change. [ABSTRACT FROM AUTHOR]

The world's extensive and often remote arid landscapes are receiving increasing attention to maintain their ecological and productive values. Monitoring and management of these lands requires indicators and evidence of ecosystem condition and trend, generally derived from widely distributed and infrequently repeated site-based records. However adequate geographic representation and frequent site revisits are difficult to achieve because of the remoteness and vast extent of these landscapes. Interpreting such sparse ecological indicators is difficult, particularly within landscapes that are highly variable in space and time. To interpret ecological indicator data collected in such environments long-term patterns of natural landscape variability need to be understood. This paper presents a framework of landscape spatio-temporal variability within which to interpret ecological indicator data. This framework is based on long-term patterns of vegetation growth across the Australian arid zone, derived from twenty-five years of high temporal resolution National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) satellite imagery. We present a case study of the extensive Alinytjara Wilurara (AW) Natural Resource Management (NRM) region in far western South Australia to illustrate new insights about landscape function gained from this approach, and their implications for collection and interpretation of ecological indicator data. We illustrate how variability in vegetation response is expressed across the region, and how stratification based on active vegetation response differs from more commonly used biogeographic stratifications in this region. Lastly we demonstrate the unique patterns of long-term vegetation response for the major vegetation response classes. Average amount, seasonality, magnitude, timing and variability of vegetation response over time are used to characterise the natural “envelope” of variability of the new landscape classes. The study region showed low vegetation response in summer and higher response in winter. Onset of growth was earlier in the north and in ecosystems dominated by mallee vegetation. Cyclonic influence from the west was evident at the southern margin of the study region. The study demonstrates the landscape functional response of the study region, and presents a method whereby remote sensing reveals the landscape context within which to better interpret ecological indicator data collected in a highly variable landscape. [ABSTRACT FROM AUTHOR]

[Display omitted] • SIF data that characterizes the growth of sandy vegetation is more sensitive. • SIF can well capture the changes in surface moisture and groundwater. • Vegetation growth has a strong correspondence with SM and a relatively weak correlation with TWS. • Vegetation growth in the late period of the mutation year is generally better than that in the early period. • Similarities and differences of vegetation-moisture relationship in four sandy lands. Hulunbuir, Otindag, Horqin and Mu Us are the four greater sandy lands in China. They are typical ecologically fragile areas with limited ability to resist interference and are susceptible to changes in the external environment. With climate change in recent decades, these areas have experienced severe land desertification and decreased groundwater levels. The ecological environment is at risk of further deterioration, and continuous vegetation restoration and management should be adapted to the local climate and maintain the water balance with natural precipitation. This paper combines multi-source satellite observations to explore the relationship between vegetation growth and water supply. The results show four main points. (1) Although the vegetation growth in Otindag and Horqin had degraded before 2010, it improved after 2010. The vegetation growth and soil moisture (SM) of the four sandy lands both generally increased from 2003 to 2016. The SM of the Horqin increased significantly (84% anomaly > 1.4 × 10−2 cm3/cm3). Other than the Hulunbuir, the other sandy lands show a downward trend in terrestrial water storage (TWS), especially the Mu Us declined the most (97% anomaly < −40 mm). (2) Solar-induced chlorophyll fluorescence (SIF) has specific advantages in characterizing the sandy vegetation productivity and water composition changes compared to vegetation index (EVI) or gross primary productivity (GPP). (3) The regional pattern of the vegetation-moisture relationship is mainly affected by the precipitation-driven SM and depends on the types of underlying vegetation functions. (4) Both SM and TWS have approximately one-month persistence in the four sandy lands, especially the Mu Us has the longest persistence (0.94 months and 1.1 months). The persistence in the early period is generally longer than that in the late period, implying that the resilience of water components after the mutation year is stronger than before it. This study provides a scientific basis for the management and restoration of sandy lands. It is recommended that different vegetation restoration and protection strategies should be developed for specific sandy land moisture conditions in arid/semi-arid regions of northern China, which includes the improvement of water use efficiency to maintain soil moisture and the reduction of excessive groundwater uses. [ABSTRACT FROM AUTHOR]