10 Search Results

Shrub fractional abundance (SFA), the proportion of shrub cover per unit area, serves as a critical indicator of environmental aridity and ecosystem health in arid and semi-arid regions, particularly across the Mongolian steppe. However, large-scale SFA mapping in Mongolian steppe ecosystems remains challenging due to the small crown size of shrubs, their sparse distribution, and spectral overlap with coexisting low vegetation (e.g., grasses and herbs), which hinders accurate detection using coarser-resolution satellite data or traditional field surveys. To address these challenges, we developed a two-step approach that integrates very-high-resolution (VHR) imagery, time-series Sentinel-2 data, and deep learning techniques. First, wemore » generated high-accuracy benchmark maps of individual shrub crowns from 0.5 m VHR imagery by combining manual segmentation with a hybrid deep learning framework (Dino V2 and convolutional neural networks). Second, we used these shrub crown maps as training data to build an XGBoost model for predicting SFA from 20 m Sentinel-2 time-series data, leveraging phenological information to improve estimation. We validated our approach across 70 sites (1km² each) in the Inner Mongolia Autonomous Region, which is representative of Mongolian steppe ecosystems. From VHR imagery, we mapped 1.31 million shrub crowns with an accuracy of R² = 0.92. Scaling up with Sentinel-2 data yielded regional SFA maps with an R² = 0.60. Further SHAP (SHapley Additive exPlanations) analysis on the developed XGBoost model revealed that phenological metrics (particularly observations in early-May, mid-July, and late-September), which distinguish shrub phenology from that of other land cover types (e.g., grasses and bare soil), were the most influential predictors of SFA. Finally, our regional SFA maps uncovered unimodal relationships between shrub distribution and climate variables, peaking at mean annual minimum temperatures near 0 °C and annual precipitation around 200 mm. Collectively, these findings demonstrate how the integration of multi-source remote sensing and machine learning can overcome historical limitations in SFA mapping, enabling accurate, spatially continuous assessments across vast Inner-Mongolian steppe ecosystems. Our framework has the potential to be applied to other steppe ecosystems and dryland ecosystems across the Mongolian steppe and beyond, offering a foundation for improved monitoring and ecological impact assessments in the face of global climate changes.« less

We study the famous example of weakly first-order phase transitions in the (1 + 1)-dimensional quantum Q-state Potts model with Q > 4. We numerically show that these weakly first-order transitions have approximately conformal invariance. Specifically, we find that entanglement entropy on considerably large system sizes fits perfectly with the universal scaling law of this quantity in conformal field theories (CFTs). This supports that the weakly first-order transitions are proximate to complex fixed points, which are described by recent conjectured complex CFTs. Moreover, the central charge extracted from this fitting is close to the real part of the complex centralmore » charge of these complex CFTs. Furthermore, we also study the conformal towers and the drifting behaviors of these conformal data (e.g., central charge and scaling dimensions).« less

The Clouds and the Earth’s Radiant Energy System (CERES) generates one of the few global satellite radiation products. The CERES ARM Validation Experiment (CAVE) has been providing long-term in situ observations for the validation of the CERES products. However, the number of these sites is low and their distribution is globally sparse, and particularly the surface net radiation product has not been rigorously validated yet. Therefore, additional validation efforts are highly required to determine the accuracy of the CERES radiation products. In this study, global land surface measurements were comprehensively collected for use in the validation of the CERES netmore » radiation (R_n) product on a daily (340 sites) and a monthly (260 sites) basis, respectively. The validation results demonstrated that the CERES R_n product was, overall, highly accurate. The daily validations had a Mean Bias Error (MBE) of 3.43 W·m^-2, Root Mean Square Error (RMSE) of 33.56 W·m^-2, and R² of 0.79, and the monthly validations had an MBE of 3.40 W·m^-2, RMSE of 25.57 W·m^-2, and R² of 0.84. The accuracy was slightly lower for the high latitudes. Following the validation, the monthly CERES R_n product, from March 2000 to July 2014, was used for a further analysis. The global spatiotemporal variation of the R_n, which occurred during the measurement period, was analyzed. In addition, two hot spot regions, the southern Great Plains and south-central Africa, were then selected for use in determining the driving factors or attribution of the R_n variation. We determined that R_n over the southern Great Plains decreased by -0.33 W·m^-2 per year, which was mainly driven by changes in surface green vegetation and precipitation. In south-central Africa, R_n decreased at a rate of -0.63 W·m^-2 per year, the major driving factor of which was surface green vegetation« less

Mapping surface all-wave net radiation (R_n) is critically needed for various applications. Several existing R_n products from numerical models and satellite observations have coarse spatial resolutions and their accuracies may not meet the requirements of land applications. In this study, we develop the Global LAnd Surface Satellite (GLASS) daytime R_n product at a 5 km spatial resolution. Its algorithm for converting shortwave radiation to all-wave net radiation using the Multivariate Adaptive Regression Splines (MARS) model is determined after comparison with three other algorithms. The validation of the GLASS R_n product based on high-quality in situ measurements in the United Statesmore » shows a coefficient of determination value of 0.879, an average root mean square error value of 31.61 Wm^-2, and an average bias of 17.59 Wm^-2. Furthermore, we also compare our product/algorithm with another satellite product (CERES-SYN) and two reanalysis products (MERRA and JRA55), and find that the accuracy of the much higher spatial resolution GLASS R_n product is satisfactory. The GLASS R_n product from 2000 to the present is operational and freely available to the public.« less