Search

As the fundamental regulator of energy exchange in the vegetation–soil–atmosphere circulation system, soil moisture is a key parameter for drought monitoring and is indispensable to the land surface hydrological processes. In order to overcome the constraints of the Perpendicular Drought Index, PDI (performs poorly over the fields with dense vegetation and hard to construct the soil line), and the Temperature Vegetation Drought Index, TVDI (requires similar atmospheric forcing and large enough dimension of mapping area), in soil moisture monitoring, a new drought index (Normalized Temperature Drought Index, NTDI) is proposed to explore the spatiotemporal changes of soil moisture by substituting red and near-infrared reflectances with vegetation index and normalized land surface temperature on the basis of the PDI framework. Victoria, Australia, was selected as the study area as it experiences many severe droughts and has been affected for more than ten years. Time series of satellite-based data were applied to evaluate the effectiveness and applicability of the NTDI at the regional scale. Results indicated that the expression of the soil line representing the water condition of the bare soil is easier to obtain in the new trapezoid framework and has good fits with the coefficients of determination (R2) of more than 0.8. Compared with PDI, TVDI and Modified PDI (MPDI) at the cropping sites, NTDI exhibits a relatively better performance in soil moisture monitoring for most days where the R2 achieved can reach to more than 0.7 on DOY 242, 254 and 272. Meanwhile, spatial–temporal mappings of the four drought indices from satellite data were conducted, and the NTDI presented the slightly seasonal variation and effectively described the real spatial characteristics of regional drought. Overall, the NTDI seems to a viable approach and can provide insight into spatial and temporal soil moisture monitoring at different scales. [ABSTRACT FROM AUTHOR]

Future global temperature change will have significant effects on society and ecosystems. Earth system models (ESM) are the primary tools to explore future climate change. However, ESMs have great uncertainty and often run at a coarse spatial resolution (usually about 2°). Accurate high‐spatial‐resolution temperature dataset are needed to improve our understanding of temperature variations and for many other applications. We apply Super resolution (SR) in computer vision using deep learning (DL) to merge 31 ESMs data. The proposed method performs data merging, bias‐correction, and spatial downscaling simultaneously. The CRU TS (Climate Research Unit gridded Time Series) data is used as reference data in the model training process. To find a suitable DL method, we select five SR methodologies with different structures. We compare the performances of the methods based on mean square error (MSE), mean absolute error (MAE) and Pearson correlation coefficient (R). The best method is used to merge the projected monthly data (1850–1900), and monthly future scenarios data (2015–2100), at the high spatial resolution of 0.5°. Results show that the merged data have considerably improved performance compared with individual ESM data and their ensemble mean (EM), both spatially and temporally. The MAE shows significant improvement; the spatial distribution of the MAE widens along the latitudes in the Northern Hemisphere. The MAE of merged data is ranging from 0.60 to 1.50, the South American (SA) has the lowest error and the Europe has the highest error. The merged product has excellent performance when the observation data is smooth with few fluctuations in the time series. This work demonstrates the applicability and effectiveness of the DL methods in data merging, bias‐correction and spatial downscaling when enough training data are available. Data can be accessed at https://doi.org/10.5281/zenodo.5746632. [ABSTRACT FROM AUTHOR]

Correction for 'Time-dependent phosphorescence color of carbon dots in boric acid matrix for dynamic information encryption' by Xiaopeng Wang et al., J. Mater. Chem. C, 2024, 12, 5849–5855, https://doi.org/10.1039/D4TC00431K. [ABSTRACT FROM AUTHOR]

Ecological environmental worsening and water-soil loss occur frequently in mountainous areas in recent years. As an important index of land deterioration and degree of drought, soil moisture can influence vegetation growth, hydrological cycle and water resource distribution in mountainous areas significantly. However, topographic effect of soil moisture under complicated mountainous terrain conditions still remains unknown. In the present study, a quantitative inversion on surface soil moisture content in a typical mountainous area of Dengfeng city, Henan province (China) in 2013 and 2017 was carried out by using temperature vegetation dryness index method based on Landsat-8 OLI (Operational Land Imager) remote sensing images and digital elevation model (DEM). Results were conducive to analyze effects of terrain factors on spatial-temporal distribution of soil moisture and improve ecological environment in mountainous area. Besides, a quantitative analysis on topographic effect (e.g. elevation, slope and slope orientation) on soil moisture in mountainous area was performed based on DEM data. Results demonstrate that topographic factors (e.g. elevation, slope and slope orientation) can cause violent variations of soil moisture in mountainous areas. Soil moisture is positively correlated with elevation and slope. In view of slope orientations, shady slope generally presents the highest soil moisture, followed by semi-shady slope, sunny slope and semi-sunny slope successively. In 2017, the heat island effect in the study area is stronger and the arid area is significantly larger than those in 2013. Additionally, soil moisture in the region with elevation lower than 750 m and slope smaller than 25° is lower in relative to that in 2013, but it is higher in the region with elevation higher than 750 m and slope larger than 25°. This study discloses the influencing law of terrain factors on spatial-temporal distribution of soil moisture in mountainous areas. Research conclusions provide some scientific references to water conservation and ecological environmental protection in mountainous areas. [ABSTRACT FROM AUTHOR]