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ABSTRACTThe Mountain-Oasis-Desert System (MODS) is the fundamental landscape component within the vast arid region of Central Asia. Human activities and natural processes cause surface displacement in the MODS, resulting in geoenvironmental issues and hazards. However, surface displacement and its driving mechanisms in the MODS are not understood. In this study, we used the time-series interferometric synthetic aperture radar (InSAR) method to detect deformation in the MODS in north Tianshan Mountain, Xinjiang and investigated its attribution from the geological conditions, groundwater level changes, and climate variability. The results along the vertical gradient indicate that decorrelation is severe in the high mountain areas with ice-marginal environments (over 3600 m above sea level (a.s.l.)) and the forest-covered mid-mountainous belt (1700–2800 m asl), rendering effective detection unfeasible. Dynamic characteristics are identified in subalpine areas with an elevation of 2800–3600 m asl, with maximum horizontal and vertical displacements of −80.2 mm/year and −58.6 mm/year, respectively. The seasonal acceleration is influenced by the combined effects of temperature and precipitation changes. We observed dense subsidence funnels and ground fissures associated with coal mining in the foreland hills (700–1700 m asl). Ground displacements here exceeded −50 mm/year two years after mining activities had ceased. Subsidence has expanded in the oases with elevations of less than 700 m asl, due to extensive groundwater extraction for agricultural irrigation. The correlation coefficient between groundwater level and displacement was 0.89 and 0.45 at wells in the agricultural and desert areas, respectively. The deformation exhibited seasonal variations associated with groundwater level. The deformation remains relatively stable in the surrounding oasis-affected desert areas, with weak fluctuations influenced by seasonal variations in groundwater level.

The dynamics of Gross Primary Productivity (GPP) is key to understand the global carbon cycle. Multiple GPP products are currently available based on remote sensing, Light Use Efficiency model (LUE) or diagnostic biophysical model. However, little knowledge is available on the spatial patterns of the uncertainty of different GPP products and their potential drivers over the Central Asia (CA), a fragile environment for accurate GPP estimation. This study investigates the sensitivity of the 8-day, monthly and yearly GPP uncertainties based on the three-cornered hat (TCH) method and Shapley additive explanation (SHAP) model in terms of vegetation, energy, water, climate and terrain factors in the dryland ecosystem during the 2003–2015 period. Ten GPP products were examined, including one product (FLUXCOM) from machine learning (ML), six products (EC-LUE, FluxSat, LUEopt, MODIS, MuSyQ and VPM) based on the (LUE), two products (GOSIF and NIRv) from satellite-based direct proxies (Proxies) and one product (PML) from the diagnostic biophysical model. The results indicate that the spatial distribution of the ten GPP products in CA showed similar patterns at different time scales, but with values varied at different products and time scales. According to the eddy covariance (EC) observations and the TCH-based uncertainties, the FLUXCOM product showed smaller relative uncertainties than other products. The attribution analysis denotes that the sources of uncertainty of the GPP varied for each product, and thus the improvement strategies for different products should be implemented separately The FLUXCOM should adapt the vegetation- related module to the dryland environment of CA. The LUE model should optimize the LUE parameters for the dryland ecosystem and incorporate the water related variables in the model. The Proxies model should incorporate the water and energy variables (such as soil moisture and radiation) as input data to improve their performance in CA. The diagnostic model should consider the elevation variable as input data, which may improve the performance of the PML in CA. Our results do not only provide an important basis for the selection of GPP products in the study of the carbon cycle in CA, but also offer a new insight into the GPP model development and improvement for the dryland ecosystem.

Fragmented ecosystems of the desiccated Aral Sea seek answers to the profound local hydrologically- and water-related problems. Particularly, in the Small Aral Sea Basin (SASB), these problems are associated with low precipitation, increased temperature, land use and evapotranspiration (ET) changes. Here, the utility of high-resolution satellite dataset is employed to model the growing season dynamic of near-surface fluxes controlled by the advective effects of desert and oasis ecosystems in the SASB. This study adapted and applied the sensible heat flux calibration mechanism of Surface Energy Balance Algorithm for Land (SEBAL) to 16 clear-sky Landsat 7 ETM+ dataset, following a guided automatic pixels search from surface temperature Ts and Normalized Difference Vegetation Index NDVI ($${T_{{s_{hot/NDVI}}}},{T_{{s_{cold/NDVI}}}}$$). Results were comprehensively validated with flux components and actual ET (ETa) outputs of Eddy Covariance (EC) and Meteorological Station (KZL) observations located in the desert and oasis, respectively. Compared with the original SEBAL, a noteworthy enhancement of flux estimations was achieved as follows: – desert ecosystem ETa $$ \to $$ R2 = 0.94; oasis ecosystem ETa $$ \to $$ R2 = 0.98 (P