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Landslide disasters pose significant threats to human life and property; therefore, accurate and effective detection and area extraction methods are crucial in environmental monitoring and disaster management. In our study, we address the critical tasks of landslide detection and area extraction in remote sensing images using advanced deep learning techniques. For landslide detection, we propose an enhanced dual-channel model that leverages EfficientNetB7 for feature extraction and incorporates spatial attention mechanisms (SAMs) to enhance important features. Additionally, we utilize a deep separable convolutional neural network with a Transformers module for feature extraction from digital elevation data (DEM). The extracted features are then fused using a variational autoencoder (VAE) to mine potential features and produce final classification results. Experimental results demonstrate impressive accuracy rates of 98.92% on the Bijie City landslide dataset and 94.70% on the Landslide4Sense dataset. For landslide area extraction, we enhance the traditional Unet++ architecture by incorporating Dilated Convolution to expand the receptive field and enable multi-scale feature extraction. We further integrate the Transformer and Convolutional Block Attention Module to enhance feature focus and introduce multi-task learning, including segmentation and edge detection tasks, to efficiently extract and refine landslide areas. Additionally, conditional random fields (CRFs) are applied for post-processing to refine segmentation boundaries. Comparative analysis demonstrates the superior performance of our proposed model over traditional segmentation models such as Unet, Fully Convolutional Network (FCN), and Segnet, as evidenced by improved metrics: IoU of 0.8631, Dice coefficient of 0.9265, overall accuracy (OA) of 91.53%, and Cohen's kappa coefficient of 0.9185 on the Bijie City landslide dataset; and IoU of 0.8217, Dice coefficient of 0.9021, overall accuracy (OA) of 96.68%, and Cohen's kappa coefficient of 0.8835 on the Landslide4Sense dataset. These findings highlight the effectiveness and robustness of our proposed methodologies in addressing critical challenges in landslide detection and area extraction tasks, with significant implications for enhancing disaster management and risk assessment efforts in remote sensing applications. [ABSTRACT FROM AUTHOR]

Submillimeter wave radiometers are promising remote sensing tools for sounding ice cloud parameters. The Ice Cloud Imager (ICI) aboard the second generation of the EUMETSAT Polar System (EPS−SG) is the first operational submillimeter wave radiometer used for ice cloud remote sensing. Ice clouds simultaneously contain three species of ice hydrometeors—ice, snow, and graupel—the physical distributions and submillimeter wave radiation characteristics of which differ. Therefore, jointly retrieving the mass parameters of the three ice hydrometeors from submillimeter brightness temperatures is very challenging. In this paper, we propose a multiple species of ice hydrometeor parameters retrieval algorithm based on convolutional neural networks (CNNs) that can jointly retrieve the total content and vertical profiles of ice, snow, and graupel particles from submillimeter brightness temperatures. The training dataset is generated by a numerical weather prediction (NWP) model and a submillimeter wave radiative transfer (RT) model. In this study, an end to end ICI simulation experiment involving forward modeling of the brightness temperature and retrieval of ice cloud parameters was conducted to verify the effectiveness of the proposed CNN retrieval algorithm. Compared with the classical Unet, the average relative errors of the improved RCNN–ResUnet are reduced by 11%, 25%, and 18% in GWP, IWP, and SWP retrieval, respectively. Compared with Bayesian Monte Carlo integration algorithm, the average relative error of the total content retrieved by RCNN–ResUnet is reduced by 71%. Compared with BP neural network algorithm, the average relative error of the vertical profiles retrieved by RCNN–ResUnet is reduced by 69%. In addition, this algorithm was applied to actual Advanced Technology Microwave Sounder (ATMS) 183 GHz observed brightness temperatures to retrieve graupel particle parameters with a relative error in the total content of less than 25% and a relative error in the profile of less than 35%. The results show that the proposed CNN algorithm can be applied to future space borne submillimeter wave radiometers to jointly retrieve mass parameters of ice, snow, and graupel. [ABSTRACT FROM AUTHOR]

Lake surface water temperature (LSWT) is a key parameter in understanding the variability of lake thermal conditions and evaporation. The MODIS-derived LSWT is widely used as a reference for lake model validations and process studies in data-scarce regions. In this study, the accuracy of the MODIS LSWT was examined on the Tibetan Plateau (TP). In-situ subsurface temperatures were collected at five large lakes. Although the observation period covers from summer to winter, only the observations during the lake turnover period (from October to freeze-up), when the lakes are well mixed, can be used as ground truth. The MODIS LSWT agrees well with the selected in-situ data for the five large lakes, with root mean square error (RMSE) < 1 °C at nighttime and <2 °C in the daytime, indicating a high accuracy of the MODIS LSWT data. Before the turnover period, the water is thermally stratified and the surface water is warmer than the subsurface water, and thus the in-situ subsurface water temperature data and the MODIS LSWT have different representativeness. In this case, if the observations are used as a validation basis, the MODIS errors could be much magnified. This in turn indicates the importance of period selection for the validation. [ABSTRACT FROM AUTHOR]

Spatial and temporal precipitation data acquisition is highly important for hydro-meteorological applications. Gridded precipitation products (GPPs) offer an opportunity to estimate precipitation at different time and resolution. Though, the products have numerous discrepancies that need to be evaluated against in-situ records. The present study is the first of its kind to highlight the performance evaluation of gauge based (GB) and satellite based (SB) GPPs at annual, winter, and summer monsoon scale by using multiple statistical approach during the period of 1979–2017 and 2003–2017, respectively. The result revealed that the temporal magnitude of all the GPPs was different and deviate up to 100–200 mm with overall spatial pattern of underestimation (GB product) and overestimation (SB product) from north to south gradient. The degree of accuracy of GB products with observed precipitation decreases with the increase in the magnitude of precipitation and vice versa for SB precipitation products. Furthermore, the observed precipitation revealed the positive trend with multiple turning points during the period 1979–2005. However, the gentle increase with no obvious break point has been detected during the period of 2005–2017. The large inter-annual variability and trends slope of the reference data series were well captured by Global Precipitation Climatology Centre (GPCC) and Tropical Rainfall Measuring Mission (TRMM) products and outperformed the relative GPPs in terms of higher R2 values of ≥ 0.90 and lower values of estimated RME ≤ 25% at annual and summer monsoon season. However, Climate Research Unit (CRU) performed better during winter estimates as compared with in-situ records. In view of significant error and discrepancies, regional correction factors for each GPPs were introduced that can be useful for future concerned projects over the study region. The study highlights the importance of evaluation by the careful selection of potential GPPs for the future hydro-climate studies over the similar regions like Punjab Province. [ABSTRACT FROM AUTHOR]

The Global Precipitation Measurement (GPM) mission provides high-resolution precipitation estimates globally. However, their accuracy needs to be accessed for algorithm enhancement and hydro-meteorological applications. This study applies data from 388 gauges in Nepal to evaluate the spatial-temporal patterns presented in recently-developed GPM-Era satellite-based precipitation (SBP) products, i.e., the Integrated Multi-satellite Retrievals for GPM (IMERG), satellite-only (IMERG-UC), the gauge-calibrated IMERG (IMERG-C), the Global Satellite Mapping of Precipitation (GSMaP), satellite-only (GSMaP-MVK), and the gauge-calibrated GSMaP (GSMaP-Gauge). The main results are as follows: (1) GSMaP-Gauge datasets is more reasonable to represent the observed spatial distribution of precipitation, followed by IMERG-UC, GSMaP-MVK, and IMERG-C. (2) The gauge-calibrated datasets are more consistent (in terms of relative root mean square error (RRMSE) and correlation coefficient (R)) than the satellite-only datasets in representing the seasonal dynamic range of precipitation. However, all four datasets can reproduce the seasonal cycle of precipitation, which is predominately governed by the monsoon system. (3) Although all four SBP products underestimate the monsoonal precipitation, the gauge-calibrated IMERG-C yields smaller mean bias than GSMaP-Gauge, while GSMaP-Gauge shows the smaller RRMSE and higher R-value; indicating IMERG-C is more reliable to estimate precipitation amount than GSMaP-Gauge, whereas GSMaP-Gauge presents more reasonable spatial distribution than IMERG-C. Only IMERG-C moderately reproduces the evident elevation-dependent pattern of precipitation revealed by gauge observations, i.e., gradually increasing with elevation up to 2000 m and then decreasing; while GSMaP-Gauge performs much better in representing the gauge observed spatial pattern than others. (4) The GSMaP-Gauge calibrated based on the daily gauge analysis is more consistent with detecting gauge observed precipitation events among the four datasets. The high-intensity related precipitation extremes (95th percentile) are more intense in regions with an elevation below 2500 m; all four SBP datasets have low accuracy (<30%) and mostly underestimated (by >40%) the frequency of extreme events at most of the stations across the country. This work represents the quantification of the new-generation SBP products on the southern slopes of the central Himalayas in Nepal. [ABSTRACT FROM AUTHOR]

The Soil Moisture Active Passive (SMAP) mission was designed to provide a global mapping of soil moisture (SM) measured by L-band passive and active microwave sensors. In this study, we evaluate the newly released SMAP enhanced SM products over the Tibetan Plateau by performing comparisons among SMAP standard products, in-situ observations and Community Land Model (CLM) simulations driven by high-resolution meteorological forcing. At local scales, the enhanced SMAP products, the standard products and CLM simulations all generally compare well with the in-situ observations. The SMAP products show stronger correlations (0.64–0.88) but slightly larger unbiased root mean square errors (ubRMSE, ~0.06) relative to the CLM simulations (0.58–0.79 and 0.037–0.047, for correlation and ubRMSE, respectively). At the regional scale, both SMAP products show similar spatial distributions of SM on the TP (Tibetan Plateau), although, as expected, the enhanced product provides more fine details. The SMAP enhanced product is in good agreement with model simulations with respect to temporal and spatial variations in SM over most of the TP. Regions with low correlation between SMAP enhanced products and model simulations are mainly located in the northwestern TP and regions of complex topography, where meteorological stations are sparse and non-existent or elevation is highly variable. In such remote regions, CLM simulations may be problematic due to inaccurate land cover maps and/or uncertainties in meteorological forcing. The independent, high-resolution observations provided by SMAP could help to constrain the model simulation and, ultimately, improve the skill of models in these problematic regions. [ABSTRACT FROM AUTHOR]