Downscaling of passive microwave soil moisture retrievals based on spectral analysis

The retrieval of soil moisture from passive microwave remotesensing data is presently one of the most effective methods for monitoring soil moisture. However, the spatial resolution of passive microwave soil moisture products is generally low; thus, existing soil moisture products should be downscaled in order to obtain more accurate soil moisture data. In this study, we explore the theoretical feasibility of applying the spectral downscaling method to the soil moisture in order to generate high spatial resolution soil moisture based on both Moderate Resolution Imaging Spectroradiometer and Fengyun-3B (FY3B) data. We analyse the spectral characteristics of soil moisture images covering the east-central of the Tibetan Plateau which have different spatial resolutions. The spectral analysis reveals that the spectral downscaling method is reliable in theory for downscaling soil moisture. So, we developed one spectral downscaling method for deriving the high spatial resolution (1 km) soil moister data from the FY3B data (25 km). Our results were compared with the ground truth measurements from 15 selected experimental days in 16 different sites. The average coefficient of determination (R2) of the spectral downscaling increased nearly doubled than that of the original FY3B soil moisture product. The spectral downscaled soil moister data were successfully applied to examine the water exchange between the land and atmosphere in the study regions. The spectral downscaling approach could be an efficient and effective method to improve the spatial resolution of current microwave soil moisture images.This work was supported by the National Natural Science Foundation of China (NSFC): [Grant Number 41401426]

Modelling the spatial variation of hydrology in volta river basin of west Africa under climate change

Spatial variability in Volta basin's climate coupled with climate change increases unpredictability and unreliability of rain-fed agriculture, putting livelihoods of the inhabitants under severe risk. Though there have been numerous studies on the hydrological response of the basin to climate change, only a few have dealt into its spatial variation. To fill up the existing gap, the spatial variation of hydrology of Volta basin under projected impacts of climate change is investigated using high resolution (0.33 km) National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR) data as observational data, Global Climate Model HadCM3, IPCC A1B emissions scenario and Soil, and Water Assessment Tool (SWAT). Calibration results from flow stations Dapola (R2 =0.74, NSE=0.72), Nawuni (R2 =0.86, NSE=0.88), and Bamboi (R2 =0.82, NSE=0.80) show reasonable simulation of the basin's hydrology, in general. Overall the simulation indicates higher spatial variability, with variability much higher at the end of the century (2071-2100). There is a greater average increase in rainfall and surface runoff in northern catchments compared to the south with average potential evapotranspiration and evapotranspiration much higher in southern catchments compared to the north. Contrary to projected increase in rainfall in the basin, some sub-basins in north and south show a decrease

Modelling streamflow response to climate change in data-scarce white volta river basin of West Africa using a semi-distributed hydrologic model

This study uses high resolution Climate Forecast System Reanalysis (CFSR), SWAT and two IPCC climate change (CC) scenarios (A1B and B1) combined with two general circulation models (GCMs) (HADCM3 and MPEH5) to evaluate impact of CC on streamflow in the White Volta basin of West Africa. The evaluation criteria (R2 and NSE > 0.70 and PBIAS within ±25%) during calibration and validation showed good simulation of the basin hydrology. Using average streamflow from 1979 to 2008 as a baseline, there were uncertainties over the sign of variation of annual streamflow in the 2020s. Annually, streamflow change is projected to be within 4.00% to þ13.00% in the 2020s and þ3.00% to þ16.00% in the 2050s. Monthly streamflow changes for most months vary between 13.00% and þ32.00%. A shift in monthly maximum streamflow from September to August is projected, while the driest months (December, January and February) show no change in the future. Based on the model results, the White Volta basin will likely experience an increase in streamflow by the mid-21st century. This would call for appropriate investment into cost-effective adaptive water management practices to cater for the likely impact of CC on the future hydrology of the basin

BEZ235 Increases the Sensitivity of Hepatocellular Carcinoma to Sorafenib by Inhibiting PI3K/AKT/mTOR and Inducing Autophagy

Acquired resistance of hepatocellular carcinoma (HCC) to sorafenib (SFB) is the main reason for the failure of SFB treatment of the cancer. Abnormal activation of the PI3K/AKT/mTOR pathway is important in the acquired resistance of SFB. Therefore, we investigated whether BEZ235 (BEZ) could reverse acquired sorafenib resistance by targeting the PI3K/mTOR pathway. A sorafenib-resistant HCC cell line Huh7R was established. MTT assay, clone formation assay, flow cytometry, and immunofluorescence were used to analyze the effects of BEZ235 alone or combined with sorafenib on cell proliferation, cell cycle, apoptosis, and autophagy of Huh7 and Huh7R cells. The antitumor effect was evaluated in animal models of Huh7R xenografts in vivo. Western blot was used to detect protein levels of the PI3K/AKT/mTOR pathway and related effector molecules. In vitro results showed that the Huh7R had a stronger proliferation ability and antiapoptosis effect than did Huh7, and sorafenib had no inhibitory effect on Huh7R. SFB + BEZ inhibited the activation of the PI3K/AKT/mTOR pathway caused by sorafenib. Moreover, SFB + BEZ inhibited the proliferation and cloning ability, blocked the cell cycle in the G0/G1 phase, and promoted apoptosis in the two cell lines. The autophagy level in Huh7R cells was higher than in Huh7 cells, and BEZ or SFB + BEZ further promoted autophagy in the two cell lines. In vivo, SFB + BEZ inhibited tumor growth by inducing apoptosis and autophagy. We concluded that BEZ235 enhanced the sensitivity of sorafenib through suppressing the PI3K/AKT/mTOR pathway and inducing autophagy. These observations may provide the experimental basis for sorafenib combined with BEZ235 in trial treatment of HCC

Reconstruction of spatially continuous time-series land subsidence based on PS-InSAR and improved MLS-SVR in Beijing Plain area

Beijing has undergone severe settlement in recent years. Persistent Scatterers Interferometric Synthetic Aperture Radar (PS-InSAR) technique has been widely used to derive time-series land deformation. However, existing studies have faced two challenges: (1) the nonlinear characteristics of time-series subsidence has not been fully investigated; (2) since PS points are normally distributed in urban areas with high building density, measurement gaps usually exist in nonurban areas. To address the challenges, we presented a new method to reconstruct spatially continuous time-series deformation. First, PS-InSAR was used to retrieve the deformation based on 135 scenes of Envisat ASAR and Radarsat-2 images from 2003 to 2020. Polynomial Curve Fitting (PCF) was then used to model nonlinear time-series deformation for the PS points. In the PS measurement gaps, Iterative Self-Organizing Data Analysis Technique (ISODATA) and Multi-output Least Squares Support Vector Regression (MLS-SVR) were used to estimate the PCF coefficients and then time-series deformation considering 40 features including thickness of the compressible layers, annual groundwater level, etc. The major results showed that (1) compared to linear, quadratic, and quartic models, cubic polynomial model generated better fit for the time-series deformation (R2 ≈0.99), suggesting obvious nonlinear temporal pattern of deformation; (2) the time-series deformation over measurement gaps reconstructed by ISODATA and MLS-SVR had satisfactory accuracy (R2 = 0.92, MAPE < 15%) and yielded higher accuracy (R2 = 0.947) than IDW (R2 = 0.687) and Ordinary Kriging (R2 = 0.688) interpolation methods. The reconstructed results maintain the nonlinear characteristics and ensure the high spatial resolution (120 m) of time-series deformation. Among the 40 predictor variables, ground water level datasets are the most influential predictors of time-series deformation