Comment on essd-2022-257

Human thermal comfort measures the combined effects of temperature, humidity, and wind speed, etc., and can be aggravated under the influences of global warming and local human activities. With the most rapid urbanization and the largest population, China is being severely threatened by aggravating human thermal stress. However, the variations of thermal stress in China at a fine scale have not been fully understood. This gap is mainly due to the lack of a high-resolution gridded dataset of human thermal indices. Here, we generate the first high spatial resolution (1 km1 km) dataset of monthly human thermal index collection (HiTIC-Monthly) over China from 2003 to 2020. In this collection, 12 commonly used thermal indicators are generated by the LGBM machine learning algorithm from multi-source gridded data, including MODIS land surface temperature, topography, land cover and land use, population density, and impervious surface fraction. Their accuracies were comprehensively assessed based on observations at 2419 weather stations across the mainland of China. The results show that our dataset has desirable performance, with mean R2, root mean square error, mean absolute error, and bias of 0.996, 0.693 °C, 0.512 °C, and 0.003 °C, respectively, by averaging the 12 indicators. Moreover, the predictions exhibit high agreements with observations across spatial and temporal dimensions, demonstrating the broad applicability of our dataset. The comparison with two existing datasets also suggests that our high-resolution dataset can describe a more explicit spatial distribution of the thermal information, showing great potentials in fine-scale (e.g., intra-urban) study. Further investigation reveals that nearly all indicators exhibit increasing trends in most parts of China during the year 2003~2020. The increase is especially stronger in North China, Southwest China, the Tibetan Plateau, and parts of Northwest China, and in the spring and summer seasons. The HiTIC-Monthly dataset is publicly available via https://zenodo.org/record/6895533 (Zhang et al., 2022a).

Human thermal comfort measures the combined effects of temperature, humidity, and wind speed, etc., and can be aggravated under the influences of global warming and local human activities. With the most rapid urbanization and the largest population, China is being severely threatened by aggravating human thermal stress. However, the variations of thermal stress in China at a fine scale have not been fully understood. This gap is mainly due to the lack of a high-resolution gridded dataset of human thermal indices. Here, we generate the first high spatial resolution (1 km1 km) dataset of monthly human thermal index collection (HiTIC-Monthly) over China from 2003 to 2020. In this collection, 12 commonly used thermal indicators are generated by the LGBM machine learning algorithm from multi-source gridded data, including MODIS land surface temperature, topography, land cover and land use, population density, and impervious surface fraction. Their accuracies were comprehensively assessed based on observations at 2419 weather stations across the mainland of China. The results show that our dataset has desirable performance, with mean R2, root mean square error, mean absolute error, and bias of 0.996, 0.693 °C, 0.512 °C, and 0.003 °C, respectively, by averaging the 12 indicators. Moreover, the predictions exhibit high agreements with observations across spatial and temporal dimensions, demonstrating the broad applicability of our dataset. The comparison with two existing datasets also suggests that our high-resolution dataset can describe a more explicit spatial distribution of the thermal information, showing great potentials in fine-scale (e.g., intra-urban) study. Further investigation reveals that nearly all indicators exhibit increasing trends in most parts of China during the year 2003~2020. The increase is especially stronger in North China, Southwest China, the Tibetan Plateau, and parts of Northwest China, and in the spring and summer seasons. The HiTIC-Monthly dataset is publicly available via https://zenodo.org/record/6895533 (Zhang et al., 2022a).

Human thermal comfort measures the combined effects of temperature, humidity, and wind speed, etc., and can be aggravated under the influences of global warming and local human activities. With the most rapid urbanization and the largest population, China is being severely threatened by aggravating human thermal stress. However, the variations of thermal stress in China at a fine scale have not been fully understood. This gap is mainly due to the lack of a high-resolution gridded dataset of human thermal indices. Here, we generate the first high spatial resolution (1 km1 km) dataset of monthly human thermal index collection (HiTIC-Monthly) over China from 2003 to 2020. In this collection, 12 commonly used thermal indicators are generated by the LGBM machine learning algorithm from multi-source gridded data, including MODIS land surface temperature, topography, land cover and land use, population density, and impervious surface fraction. Their accuracies were comprehensively assessed based on observations at 2419 weather stations across the mainland of China. The results show that our dataset has desirable performance, with mean R2, root mean square error, mean absolute error, and bias of 0.996, 0.693 °C, 0.512 °C, and 0.003 °C, respectively, by averaging the 12 indicators. Moreover, the predictions exhibit high agreements with observations across spatial and temporal dimensions, demonstrating the broad applicability of our dataset. The comparison with two existing datasets also suggests that our high-resolution dataset can describe a more explicit spatial distribution of the thermal information, showing great potentials in fine-scale (e.g., intra-urban) study. Further investigation reveals that nearly all indicators exhibit increasing trends in most parts of China during the year 2003~2020. The increase is especially stronger in North China, Southwest China, the Tibetan Plateau, and parts of Northwest China, and in the spring and summer seasons. The HiTIC-Monthly dataset is publicly available via https://zenodo.org/record/6895533 (Zhang et al., 2022a).

Human thermal comfort measures the combined effects of temperature, humidity, and wind speed, etc., and can be aggravated under the influences of global warming and local human activities. With the most rapid urbanization and the largest population, China is being severely threatened by aggravating human thermal stress. However, the variations of thermal stress in China at a fine scale have not been fully understood. This gap is mainly due to the lack of a high-resolution gridded dataset of human thermal indices. Here, we generate the first high spatial resolution (1 km1 km) dataset of monthly human thermal index collection (HiTIC-Monthly) over China from 2003 to 2020. In this collection, 12 commonly used thermal indicators are generated by the LGBM machine learning algorithm from multi-source gridded data, including MODIS land surface temperature, topography, land cover and land use, population density, and impervious surface fraction. Their accuracies were comprehensively assessed based on observations at 2419 weather stations across the mainland of China. The results show that our dataset has desirable performance, with mean R2, root mean square error, mean absolute error, and bias of 0.996, 0.693 °C, 0.512 °C, and 0.003 °C, respectively, by averaging the 12 indicators. Moreover, the predictions exhibit high agreements with observations across spatial and temporal dimensions, demonstrating the broad applicability of our dataset. The comparison with two existing datasets also suggests that our high-resolution dataset can describe a more explicit spatial distribution of the thermal information, showing great potentials in fine-scale (e.g., intra-urban) study. Further investigation reveals that nearly all indicators exhibit increasing trends in most parts of China during the year 2003~2020. The increase is especially stronger in North China, Southwest China, the Tibetan Plateau, and parts of Northwest China, and in the spring and summer seasons. The HiTIC-Monthly dataset is publicly available via https://zenodo.org/record/6895533 (Zhang et al., 2022a).

Grazing intensity, characterized by high spatial heterogeneity, is a vital parameter to accurately depict human disturbance and its effects on grassland ecosystems. Grazing census data provide useful county-scale information; however, they do not accurately delineate spatial heterogeneity within counties, and a high-resolution dataset is urgently needed. Therefore, we built a methodological framework combining the cross-scale feature extraction method and a random forest model to spatialize census data after fully considering four features affecting grazing, and produced a high-resolution gridded grazing dataset on the Qinghai–Tibet Plateau in 1982–2015. The proposed method (R2 = 0.80) exhibited 35.59% higher accuracy than the traditional method. Our dataset were highly consistent with census data (R2 of spatial accuracy = 0.96, NSE of temporal accuracy = 0.96) and field data (R2 of spatial accuracy = 0.77). Compared with public datasets, our dataset featured a higher temporal resolution (1982–2015) and spatial resolution (over two times higher). Thus, it has the potential to elucidate the spatiotemporal variation in human activities and guide the sustainable management of grassland ecosystem.

Many low- and middle-income countries in Africa face heightened flood risks and significant socio-economic impacts due to climate change, despite contributing minimally to global emissions. However, current flood datasets often lack the necessary resolution (above 250m) and duration to focus effectively on floods in these regions, complicating climate mitigation and adaptation efforts. This study aims to develop long-term, high-resolution spatiotemporal datasets to better characterize flood patterns and their socio-economic impacts across Africa. Using satellite imagery from Landsat and Sentinel-2, we mapped monthly flood inundation extents from 1984 to 2024, producing a flood dataset with a high spatial resolution of 30m to 10m for the entire African continent. We integrated these flood data with socio-economic metrics—population, GDP, and displacement figures—to assess socio-economic vulnerability across African countries. The results show that most African countries have witnessed an increase in affected population by floods over the past 40 years, with Comoros rising by 11.5% of the total population, Madagascar by 6.9%, Liberia by 5.7%, and Congo by 4.3%, identifying these countries as hotspots. In the last decade alone, flood-induced displacements have affected nearly 15 million people, predominantly in low-income countries, while upper-middle-income countries have shown better resilience in flood response. With the growing prevalence of floods and their uneven socio-economic repercussions, these high-resolution datasets are indispensable for shaping effective climate adaptation and mitigation measures, enabling precise and targeted actions. Policies should focus on strengthening flood response capacities and prioritizing support for socio-economically vulnerable regions to minimize flood-related consequences.

Snow cover depletion curves are required for several water management applications of snow hydrology and are often difficult to obtain automatically using optical remote sensing data owing to both frequent cloud cover and temporary snow cover. This study develops a methodology to produce accurate snow cover depletion curves automatically using high temporal resolution optical remote sensing data (e.g. Terra Moderate Resolution Imaging Spectroradiometer (MODIS), Aqua MODIS or National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR)) by snow cover change trajectory analysis. The method consists of four major steps. The first is to reclassify both cloud‐obscured land and snow into more distinct subclasses and to determine their snow cover status (seasonal snow cover or not) based on the snow cover change trajectories over the whole snowmelt season. The second step is to derive rules based on the analysis of snow cover change trajectories. These rules are subsequently used to determine for a given date, the snow cover status of a pixel based on snow cover maps from the beginning of the snowmelt season to that given date. The third step is to apply a decision‐tree‐like processing flow based on these rules to determine the snow cover status of a pixel for a given date and to create daily seasonal snow cover maps. The final step is to produce snow cover depletion curves using these maps. A case study using this method based on Terra MODIS snow cover map products (MOD10A1) was conducted in the lower and middle reaches of the Kaidu River Watershed (19 000 km2) in the Chinese Tien Shan, Xinjiang Uygur Autonomous Region, China. High resolution remote sensing data (charge coupled device (CCD) camera data with 19·5 m resolution of the China and Brazil Environmental and Resources Satellite (CBERS) data (19·5 m resolution), and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data with 15 m resolution of the Terra) were used to validate the results. The study shows that the seasonal snow cover classification was consistent with that determined using a high spatial resolution dataset, with an accuracy of 87–91%. The snow cover depletion curves clearly reflected the impact of the variation of temperature and the appearance of temporary snow cover on seasonal snow cover. The findings from this case study suggest that the approach is successful in generating accurate snow cover depletion curves automatically under conditions of frequent cloud cover and temporary snow cover using high temporal resolution optical remote sensing data. Copyright © 2007 John Wiley & Sons, Ltd.

Coastal ocean areas are very dynamic regions subject to strong anthropogenic pressure (e.g. industry, tourism, renewable energies, population). Satellite data constitute a unique tool to study and monitor these complex areas at high spatial and temporal resolutions. While “traditional” ocean colour satellites like Sentinel-3 provide daily temporal resolution, their sensors do not measure at the spatial resolution needed to correctly resolve complex coastal dynamics. On the other hand, while high spatial resolution sensors, like the MSI onboard Sentinel-2 (10m - 60m resolution), are able to resolve these small scales, their revisit time is far from optimal (2-5 days for the Sentinel-2 A & B tandem). Additionally, both high spatial resolution datasets and traditional ones are hindered by the presence of clouds, resulting in a large amount of missing data. Given the high complementarity of these two measurement strategies (Sentinel-3 and Sentinel-2), we present a methodology to derive gap-filled multi resolution ocean colour products from the synergistic use of Sentinel-2 and Sentinel-3 data. Applying DINEOF (Data Interpolating Empirical Orthogonal Functions), we exploit and combine both the high-resolution spatial variability information contained in Sentinel-2 products as well as the high temporal information of Sentinel-3. Both Sentinel-2 and Sentinel-3 products are generated using the Copernicus Marine High-Resolution processor, which uses automated switching algorithms adapted to the local water conditions (at the pixel level) to retrieve optimal remote sensing spectra and water quality variables. With this approach, we address the high variability of different water types with small scale changes. The combined Sentinel-2 and Sentinel-3 products consist of Chlorophyll-a concentration and turbidity retrieved through a multi-algorithm approach with optimized quality flagging. An analysis of turbidity and chlorophyll daily data at 100m resolution in the northern Adriatic Sea and the Belgian coastal zone will be presented, alongside an analysis of the spatial scales resolved by the original and merged DINEOF datasets.

An object-oriented, multiscale feature extraction approach is proposed for the land-cover classification of high spatial resolution images. The approach provides more discriminative features by considering the spatial context information from different segmentation levels. It consists of three successive substeps: segmentation by mean-shift algorithm, an iteratively merging process controlled by merging cost function and range-of-scale parameter, and feature extraction from linked multilevel image partitions. The mean-shift method is to get boundary-preserved and spectrally homogeneous over-segmentation regions. Then, a family of nested image partitions is constructed by a merging procedure. Meanwhile, every region of the finest scale is linked to image objects of its superlevels. Finally, every region in the finest scale is treated as a basic analysis unit, and the feature vectors are created by stacking statistics from the region and their superlevels. A support vector machine is used as a classifier and the method on two widely used high spatial resolution data sets over Pavia City, Italy, are evaluated. Compared with results reported in many papers, the result indicates superior accuracy.

High spatial resolution (1–5 m) remotely sensed datasets are increasingly being used to map land covers over large geographic areas using supervised machine learning algorithms. Although many studies have compared machine learning classification methods, sample selection methods for acquiring training and validation data for machine learning, and cross-validation techniques for tuning classifier parameters are rarely investigated, particularly on large, high spatial resolution datasets. This work, therefore, examines four sample selection methods—simple random, proportional stratified random, disproportional stratified random, and deliberative sampling—as well as three cross-validation tuning approaches—k-fold, leave-one-out, and Monte Carlo methods. In addition, the effect on the accuracy of localizing sample selections to a small geographic subset of the entire area, an approach that is sometimes used to reduce costs associated with training data collection, is investigated. These methods are investigated in the context of support vector machines (SVM) classification and geographic object-based image analysis (GEOBIA), using high spatial resolution National Agricultural Imagery Program (NAIP) orthoimagery and LIDAR-derived rasters, covering a 2,609 km2 regional-scale area in northeastern West Virginia, USA. Stratified-statistical-based sampling methods were found to generate the highest classification accuracy. Using a small number of training samples collected from only a subset of the study area provided a similar level of overall accuracy to a sample of equivalent size collected in a dispersed manner across the entire regional-scale dataset. There were minimal differences in accuracy for the different cross-validation tuning methods. The processing time for Monte Carlo and leave-one-out cross-validation were high, especially with large training sets. For this reason, k-fold cross-validation appears to be a good choice. Classifications trained with samples collected deliberately (i.e., not randomly) were less accurate than classifiers trained from statistical-based samples. This may be due to the high positive spatial autocorrelation in the deliberative training set. Thus, if possible, samples for training should be selected randomly; deliberative samples should be avoided.

There is an increasing demand for satellite remote sensing data with both high spatial and temporal resolution in many applications. But it still is a challenge to simultaneously improve spatial resolution and temporal frequency due to the technical limits of current satellite observation systems. To this end, much R&D efforts have been ongoing for years and lead to some successes roughly in two aspects, one includes super resolution, pan-sharpen etc. methods which can effectively enhance the spatial resolution and generate good visual effects, but hardly preserve spectral signatures and result in inadequate analytical value, on the other hand, time interpolation is a straight forward method to increase temporal frequency, however it increase little informative contents in fact. In this paper we presented a novel method to simulate high resolution time series data by combing low resolution time series data and a very small number of high resolution data only. Our method starts with a pair of high and low resolution data set, and then a spatial registration is done by introducing LDA model to map high and low resolution pixels correspondingly. Afterwards, temporal change information is captured through a comparison of low resolution time series data, and then projected onto the high resolution data plane and assigned to each high resolution pixel according to the predefined temporal change patterns of each type of ground objects. Finally the simulated high resolution data is generated. A preliminary experiment shows that our method can simulate a high resolution data with a reasonable accuracy. The contribution of our method is to enable timely monitoring of temporal changes through analysis of time sequence of low resolution images only, and usage of costly high resolution data can be reduces as much as possible, and it presents a highly effective way to build up an economically operational monitoring solution for agriculture, forest, land use investigation, environment and etc. applications.

Three-dimensional magnetic resonance imaging (3D MRI) with isotropic high spatial resolution data sets has advanced into a key technology for multiple advanced musculoskeletal (MSK) imaging applications, such as dynamic multiplanar evaluation of small and complex anatomical structures, curved multiplanar reformation of long-winded structures (e.g., nerves and tendons), the realization of efficient joint and whole-body MRI protocols, and advanced MR image reformations, including the creation of cartilage maps, as well as volume rendering and cinematic rendering-type 3D MR images.[1] [2] [3]

Shock-induced H2O masers are important magnetic field tracers at very high density gas. Water masers are found in both high- and low-mass star-forming regions, acting as a powerful tool to compare magnetic field morphologies in both mass regimes. In this paper, we show one of the first magnetic field determinations in the low-mass protostellar core IRAS 16293-2422 at volume densities as high as 10^(8-10) cm^-3. Our goal is to discern if the collapsing regime of this source is controlled by magnetic fields or other factors like turbulence. We used the Very Large Array (VLA) to carry out spectro-polarimetric observations in the 22 GHz Zeeman emission of H2O masers. From the Stokes V line profile, we can estimate the magnetic field strength in the dense regions around the protostar. A blend of at least three maser features can be inferred from our relatively high spatial resolution data set (~ 0.1"), which is reproduced in a clear non-Gaussian line profile. The emission is very stable in polarization fraction and position angle across the channels. The maser spots are aligned with some components of the complex outflow configuration of IRAS 16293-2422, and they are excited in zones of compressed gas produced by shocks. The post-shock particle density is in the range of 1-3 x 10^9 cm^-3, consistent with typical water masers pumping densities. Zeeman emission is produced by a very strong line-of-sight magnetic field (B ~ 113 mG). The magnetic field pressure derived from our data is comparable to the ram pressure of the outflow dynamics. This indicates that the magnetic field is energetically important in the dynamical evolution of IRAS 16293-2422.

Aerosols affect surface solar radiation (SSR) by modifying cloud properties. For solar energy applications, either for photovoltaics (PV) or concentrating solar power (CSP) systems, understanding of spatio-temporal variability of aerosol is essential for short-term forecasts of solar radiation.  This study aims to quantify interday variability of the commonly used proxy for optical properties of aerosols - aerosol optical depth (AOD) at the wavelength of 500 nm and to assess the deviation in direct normal irradiance (DNI) and in global horizontal irradiance (GHI) caused by AOD variation using radiative transfer modeling. The following AOD data are used: AOD forecasts for the next day from the Copernicus Atmosphere Monitoring Service (CAMS); ground-based AOD measurements from the Aerosol Robotic Network (AERONET); ModIs/Terra high spatial resolution data set (MIDAS) which is based on satellite retrievals. We select locations with different aerosol types around the globe based on AERONET stations, where at each site more than 1500 daily values for consecutive days from 2010 to 2020 are available. We adopt the persistence approach using AOD data from AERONET and MIDAS and compare them with CAMS forecasts. The AOD data from AERONET serve as ground truth for validation. Under cloudless conditions, AOD variability informs the deviation of DNI and GHI forecasts from the observation. Furthermore, we attempt a worldwide clustering of aerosol types based on chemical modeling. Interday AOD variability is high in megacities with high aerosol load and in the proximity of highly variable aerosol sources such as desert dust and biomass burning (fires). This work should deepen our knowledge of spatio-temporal variability of aerosol in the context of AOD and SSR prediction, thus contributing to the quality and reliability of solar resource assessment.

Information derived from high spatial resolution remotely sensed data is critical for the effective management of forested ecosystems. However, high spatial resolution data-sets are typically costly to acquire and process and usually provide limited geographic coverage. In contrast, moderate spatial resolution remotely sensed data, while not able to provide the spectral or spatial detail required for certain types of products and applications, offer inexpensive, comprehensive landscape-level coverage. This study assessed using an object-based approach to extrapolate detailed tree species heterogeneity beyond the extent of hyperspectral/LiDAR flightlines to the broader area covered by a Landsat scene. Using image segments, regression trees established ecologically decipherable relationships between tree species heterogeneity and the spectral properties of Landsat segments. The spectral properties of Landsat bands 4 (i.e., NIR: 0.76–0.90 µm), 5 (i.e., SWIR: 1.55–1.75 µm) and 7 (SWIR: 2.08–2.35 µm) were consistently selected as predictor variables, explaining approximately 50% of variance in richness and diversity. Results have important ramifications for ongoing management initiatives in the study area and are applicable to wide range of applications.