Ground Observations Research Articles

Aggravated surface O3 pollution primarily driven by meteorological variations in China during the 2020 COVID-19 pandemic lockdown period

Abstract. Due to the lockdown during the COVID-19 pandemic in China from late January to early April in 2020, a significant reduction in primary air pollutants, as compared to the same time period in 2019, has been identified by satellite and ground observations. However, this reduction is in contrast with the increase of surface ozone (O3) concentration in many parts of China during the same period from 2019 to 2020. The reasons for this contrast are studied here from two perspectives: emission changes and inter-annual meteorological variations. Based on top-down constraints of nitrogen oxide (NOx) emissions from TROPOMI measurements and GEOS-Chem model simulations, our analysis reveals that NOx and volatile organic compound (VOC) emission reductions as well as meteorological variations lead to 8 %, −3 % and 1 % changes in O3 over North China, respectively. In South China, however, we find that meteorological variations cause ∼ 30 % increases in O3, which is much larger than −1 % and 2 % changes due to VOC and NOx emission reductions, respectively, and the overall O3 increase in the simulations is consistent with the surface observations. The higher temperature associated with the increase in solar radiation and the decreased relative humidity are the main reasons that led to the surface O3 increase in South China. Collectively, inter-annual meteorological variations had a larger impact than emission reductions on the aggravated surface O3 pollution in China during the lockdown period of the COVID-19 pandemic.

A comparative study of remotely sensed reservoir monitoring across multiple land cover types

Continuous monitoring of reservoirs and dams is essential for efficient water management. Synthetic Aperture Radar (SAR) imagery offers the potential for continuous monitoring of surface water through all-weather ground observation. The objective of this study is to enhance the accuracy of water body detection and water quantity estimation by applying 64 combinations of speckle filtering and object detection techniques to Sentinel-1 imagery. For speckle filtering, the Median, Gaussian, Lee, and Frost techniques were used with various window sizes (3, 5, 7, and 9). For water body detection, the Otsu, Kittler-Illingworth (KI), Chan-Vese (CV), and K-means methods were employed. The study area included three reservoirs and two dams in Korea, encompassing a variety of water surface sizes and types of land cover. To validate the accuracy of each water body detection combination, manual delineation-based water mask images from Sentinel-2 were employed. Furthermore, a regression equation (y=axb) between water surface area and storage was used to estimate water storage based on SAR imagery, followed by time-series validation using in-situ data. The research results indicate that the optimal detection technique varies significantly depending on the type of surrounding land cover and the size of the water body. The highest performance was observed for the CV technique combination for waterfront pixels, and for the KI technique combination for other land cover pixels. In speckle filtering techniques using a large window size, the false detection rate caused by vegetation and buildings was low; however, the boundaries of water bodies were blurred. Consequently, using smaller window sizes in SAR imagery and leveraging optimal water body detection combinations specific to land cover types, along with post-processing using masking data, would enhance the performance of water surface area and storage estimation.

Weakened connection between spring leaf-out and autumn senescence in the Northern Hemisphere.

Vegetation autumn phenology is critical in regulating the ecosystem carbon cycle and regional climate. However, the dominant drivers of autumn senescence and their temporal shifts under climate change remain poorly understood. Here, we conducted a multi-factor analysis considering both direct climatic controls and biological carryover effects from start-of-season (SOS) and seasonal peak vegetation activities on the end-of-season (EOS) to fill these knowledge gaps. Combining satellite and ground observations across the northern hemisphere, we found that carryover effects from early-to-peak vegetation activities exerted greater influence on EOS than the direct climatic controls on nearly half of the vegetated land. Unexpectedly, the carryover effects from SOS on EOS have significantly weakened over recent decades, accompanied by strengthened climatic controls. Such results indicate the weakened constraint of leaf longevity on senescence due to prolonged growing season in response to climate change. These findings underscore the important role of biological carryover effects in regulating vegetation autumn senescence under climate change, which should be incorporated into the formulation and enhancement of phenology modules utilized in land surface models.

Development of aircraft noise simulation framework J-FRAIN based on component-wise sound source models

In order tomake detailed and accurate predictions of aircraft noise around airports, we have developed “J-FRAIN”, a new aircraft noise simulation framework that can precisely predict the time histories of noises emitted from each major aircraft noise source during the landing approach phase at ground observation points. This article describes the elements of the developed framework—data acquisition, sound source modeling, propagation modeling, and ground noise prediction—and present some application examples. To develop the framework, we first deployed a 30m-diameter microphone array under the final approach path to an international airport to measure acoustic maps of several civil aircraft types in flight. The sound powers of major aircraft noise-emitting components were then estimated quantitatively by domain integration of the deconvolved acoustic maps at five emission angles in the plane of the glideslope, and the directivities of each noise source were determined. Next, component-wise sound source regression models for engines and airframe noise sources were created based on the physical relationships between engine rotation speed, airspeed, and the deployment angle of high-lift devices, and the coefficients in each model were determined to minimize the root-mean-square error between the measured and predicted sound power levels. The phenomena of atmospheric absorption and ground effect during the propagation of radiated sounds were also incorporated into the framework. Actual flight parameter values were used as inputs to the completed framework, and it was confirmed that the predicted time histories of sound pressure levels on the ground agreed with measured data to within 2 dB if ground effect was properly considered. Finally, as sample applications that use the unique features of the proposed J-FRAIN framework, the article discusses the evaluation of the contributions of each aircraft noise source at noise observation points under the final approach path to the airport and the impact assessment of flight operations on noise.

A dataset of downscaled monthly precipitation at 1 km resolution in Guangxi, China and ASEAN regions from 2001 to 2020

Located in the southeastern Asia, Guangxi, China and the ASEAN regions have a subtropical monsoon climate with abundant rainfall and frequent floods, which has a great impact on social and economic activities. The precipitation data with high precision and high spatial and temporal resolution are of great significance for industrial and agricultural production, water conservancy development, drought and flood monitoring and prevention, and ecological environment protection. In this paper, we used the Global Precipitation Measurement Mission precipitation data (GPM IMERG) from 2001 to 2020 as the dependent variable in combination with the enhanced vegetation index (EVI), surface evapotranspiration (ET), land surface temperature (LST) from MODIS data and elevation (ELV) from ASTER data as explanatory variables. Then we introduced a geographically weighted regression (GWR) model to construct an annual scale model, depicting the spatial variation of 10-km satellite precipitation with the influence of geographical environmental conditions. Five types of kernel functions were adopted in our GWR model, including the gaussian, exponential, bisquare, tricube, and boxcar kernel function. The optimal kernel function was selected based on the correlation coefficient, the root means square error and bias. Thus, we established the dataset of downscaled monthly precipitation in Guangxi, China and ASEAN regions from 2001 to 2020 by extrapolating the GWR model with input variables at 1 km resolution. The dataset of 1-km monthly precipitation from 2001 to 2020 was also generated by the proportional coefficient method. Moreover, ground observation data from 2,679 stations during 2001–2020 were used for verification. The correlation coefficient, root mean square error and deviation were 0.792, 74.610mm and -0.122%, respectively. The results show that this dataset can reflect the precipitation spatial and temporal distribution and its variations at 1-km resolution in detail, and could be potentially promising for ecological environment, hydrological management, flood prediction and other relevant fields.

A dataset of gridded phenology of woody plants in Europe from 1951 to 2021

Plant phenology records the timing of plant cyclic growth events, and stand as one of the most important indicators of climate change. The monitoring and research of plant phenology is of great significance for understanding the response of ecosystems to global changes and simulating the material and energy balance of terrestrial ecosystems. Based on the ground observation phenological data of six representative woody plants compiled by the Pan European Phenology Project (PEP725) in the past 70 years, this paper employed three phenology models (Unichill, Unified and TSC) to predict and upscale the phenology data on the continental scale, and prepared a dataset of gridded phenology of woody plants in Europe. This dataset contains the grid data of the first leaf date (FLD) and first flower date (FFD) of six woody plants in Europe (34°57′N-72°3′N，25°3′W-40°3′E) from 1951 to 2021, with a spatial resolution of 0.1 ° and a temporal resolution of one day. The quality assessment of the grid phenology data shows that the average error for FLD and FFD is 7.9 and 7.6 days respectively, which are close to simulation errors of spring phenology observed in other regional-scale studies, revealing the high accuracy of the data. This dataset can better characterize the temporal and spatial patterns of plant phenology at a continental scale in Europe, and provide an effective verification approach for plant phenology products from other sources. Furthermore, it can offer valuable data support for research on global change and terrestrial ecosystem simulation.

A scalable big data approach for remotely tracking rangeland conditions

Rangelands, covering half of the global land area, are critically degraded by unsustainable use and climate change. Despite their extensive presence, global assessments of rangeland condition and sustainability are limited. Here we introduce a novel analytical approach that combines satellite big data and statistical modeling to quantify the likelihood of changes in rangeland conditions. These probabilities are then used to assess the effectiveness of management interventions targeting rangeland sustainability. This approach holds global potential, as demonstrated in Mongolia, where the shift to a capitalist economy has led to increased livestock numbers and grazing intensity. From 1986 to 2020, heavy grazing caused a marked decline in Mongolia’s rangeland condition. Our evaluation of diverse management strategies, corroborated by local ground observations, further substantiates our approach. Leveraging globally available yet locally detailed satellite data, our proposed condition tracking approach provides a rapid, cost-effective tool for sustainable rangeland management.

Matching Spring Phenology Indicators in Ground Observations and Remote-Sensing Metrics

Accurate monitoring of leaf phenology, from individual trees to entire ecosystems, is vital for understanding and modeling forest carbon and water cycles, as well as assessing climate change impact. However, the accuracy of many remote-sensing phenological products remains difficult to directly corroborate using ground-based monitoring, owing to variations in the observed indicators and the scales used. This limitation hampers the practical implementation of remote-sensing phenological metrics. In our study, the start of growing season (SOS) from 2016 to 2021 was estimated for the continental USA using Sentinel-2 images. The results were then matched with several ground-based spring vegetation phenology metrics obtained by the USA National Phenology Network (USA-NPN). In this study, we focused on the relationships between the leaf-unfolding degree (LUD), the SOS, and the factors that drive these measures. Our results revealed that: (1) the ground-based leaves and increasing leaf size stages were significantly correlated with the SOS; (2) with the closest match being observed for a leaf spread of 13%; (2) the relationship between the SOS and LUD varied according to the species and ecoregion, and the pre-season cumulative radiation was found to be the main factor affecting the degree of matching between the ground observations and the metrics derived from the Sentinel-2 data. Our investigations provide a ground-based spring phenology metric that can be used to verify or evaluate remote-sensing spring phenology products and will help to improve the accuracy of remote-sensing phenology metrics.

Satellite-based water surface slope over a small mountain river in northern China

Satellite altimetry has become a crucial alternative for measuring inland water surface elevations apart from ground observations. Water surface slope (WSS), a fundamental parameter in river discharge simulation, also allowing for assimilating satellite and ground-observed water level data with different observation positions. There are various satellite data products offering precise measurements of river water levels and estimates of WSS on a global scale. However, satellites observation of water surface over small rivers, particularly those in mountainous areas with narrow river channels, continues to be difficult. This study focuses on evaluating the precision of the ICESat-2 ATL03 photon height data in estimating WSS throughout the mountainous river section of Yongding River, which flows across the Hebei Province and Beijing City in northern China. The ICESat-2 ATL03 data with minimum along-track sampling interval of 0.7 m makes it possible to accurately estimate the WSS for small river sections that ranged from 50 to 100 m in width. A histogram-based statistical approach was used for identifying the satellite lidar photon height located right over the water surface. The eleven pairs of satellite virtual stations selected for extracting river water surface elevation and estimating river WSS demonstrated an overall validation correlation coefficient of 0.98. The range of relative error in WSS estimation is between 0.13 % and 9.02 %. The results of this work have important implications for using satellite-based measurements to estimate critical hydrological parameters in small and mountainous river basins in the absence of ground observations.

Spatiotemporal variations and source on black carbon over Chongqing, China: Long-term changes and observational experiments

Black carbon (BC), as a critical light-absorbing constituent within aerosols, exerts profound effects on atmospheric radiation balance, climate, air quality and human health, etc. And it is also a long-standing focus in rapidly developing megacities. So, this study primarily focuses on investigating the variation characteristics and underlying causes of BC in Chongqing (31,914,300 population), which is one of the municipalities directly under the central government of China, serving as a pivotal economic hub in southwest China. Utilizing MERRA-2 reanalysis data, we examined the long-term changes of atmospheric BC over Chongqing 20 years (from 2002 to 2021). Moreover, BC mass concentration observations were conducted using an Aethalometer (AE-33) from March 15 to June 14, 2021 in Liangping District, Chongqing. The statistical analysis over the last 20 years reveals an annual mean BC concentration in Chongqing of 3.42 ± 0.20 μg/m3, exhibiting growth from 2002 to 2008, followed by a decline from 2008 to 2021. Monthly concentration displays a “U-shaped” trend, with the lowest values occurring in summer and the highest in winter. Due to topographical and meteorological influences, local emissions primarily contribute to BC pollution, characterized by a spatial distribution pattern of high in the west and low in the east. Ground observation indicates a distinct dual-peaked pattern in the diurnal variation of BC, with peak concentrations aligning with periods of high traffic emissions. The variation in BC is significantly influenced by meteorological conditions (wind, temperature, atmospheric boundary layer) and local pollution sources (predominantly traffic). Furthermore, extreme events analysis suggests that local emissions and regional transport (with higher contributions from Chongqing and the Sichuan Basin) predominantly contributed to BC pollution. This study effectively makes up for the deficiency in analyzing the distribution and sources of BC pollution in Chongqing, providing valuable scientific insights for the atmospheric environment of megacities.

Long-term trend of surface wind speed in the Guangdong-Hong Kong-Macau Greater Bay Area during 1980–2020: Spatiotemporal variation and urbanization effect

Understanding the surface wind speed variations is important for the climate and air quality assessment in the Guangdong-Hong Kong-Macau Greater Bay Area (GBA), known as the world class urban agglomeration in China. This study investigates the spatial and temporal variations of surface mean wind speed (MWS) in the GBA from 1980 to 2020, drawing upon ground observations and reanalysis data. After accounting for station relocations, the observed wind speed demonstrated a fluctuating declining trend of −0.036 m∙s−1 per decade over the examined period in line with reanalysis data. Wind speeds were found stronger during winter and daytime than summer and nighttime. The southern coastal regions exhibited higher MWS compared with northern mountainous areas. The Empirical Orthogonal Function (EOF) revealed the dominant mode of MWS changes was characterized by a cyclical pattern lasting over 20 years, probably influenced by atmospheric circulation. The urbanization reduced the observed wind speeds by 4.71% - 10.11% over the past decade (2011−2020) in urban areas of the GBA based on urban-minus-rural (UMR), observation-minus-reanalysis (OMR), and urban impact factor (UIF) methods. The long-term spatio-temporal analyses of MWS could benefit the climate and air quality studies and provide data support for regional and micro-scale modeling in the GBA.

Application of multi-temporal satellite imagery for urban tree species identification

Accurate tree inventories are critical for urban forest management but challenging to obtain, as many urban trees are on private property (backyards, etc.) and are excluded from public inventories. Here, we examined the feasibility of tree species identification in a large heterogenous urban area (>850 km2) by using multi-temporal PlanetScope images (3.2 m resolution, multi-spectral) and inventory data from more than 20,000 ground observations within the urban forest of the Greater Chicago area. Our approach achieved an overall classification accuracy of 0.60 and 0.71 for 18 species and ten genera, respectively, but varied from moderate to high for certain species (0.59–0.92) and genera (0.61–0.91). In particular, we identified key host tree species (Fraxinus americana, F. pennsylvanica, and Acer saccharinum) for two damaging invasive insects, emerald ash borer (EAB, Agrilus planipennis) and Asian longhorn beetle (ALB, Anoplophora glabripennis), with over 0.80 accuracies. In addition, we demonstrated that including images from the autumn months (September–November), either for a single-season model or a combined multiple-season model, improved the identification accuracy of temperate deciduous trees. Further, the high classification accuracy of support vector machine (SVM) over random forest (RF) and neural network (NN) approaches suggests that future work might benefit from comparing multiple classification methods to select the approach that maximizes species classification accuracy. Our study demonstrated the potential for applying multi-temporal high-resolution images in urban tree classification, which can be used for urban forest management at a large spatial scale.

Improvement of High-Resolution Daytime Fog Detection Algorithm Using GEO-KOMPSAT-2A/Advanced Meteorological Imager Data with Optimization of Background Field and Threshold Values

This study aimed to improve the daytime fog detection algorithm GK2A_HR_FDA using the GEO-KOMPSAT-2A (GK2A) satellite by increasing the resolution (2 km to 500 m), improving predicted surface temperature by the numerical model, and optimizing some threshold values. GK2A_HR_FDA uses numerical model prediction temperature to distinguish between fog and low clouds and evaluates the fog detection level using ground observation visibility data. To correct the errors of the numerical model prediction temperature, a dynamic bias correction (DBC) technique was developed that reflects the geographic location, time, and altitude in real time. As the numerical model prediction temperature was significantly improved after DBC application, the fog detection level improved (FAR: −0.02–−0.06; bias: −0.07–−0.23) regardless of the training and validation cases and validation method. In most cases, the fog detection level was improved due to DBC and threshold adjustment. Still, the detection level was abnormally low in some cases due to background reflectance problems caused by cloud shadow effects and navigation errors. As a result of removing navigation errors and cloud shadow effects, the fog detection level was greatly improved. Therefore, it is necessary to improve navigation accuracy and develop removal techniques for cloud shadows to improve fog detection levels.

A Retrospective Hydrological Uncertainty Analysis Using Precipitation Estimation Ensembles for a Poorly Gauged Basin in High Mountain Asia

Abstract We study the impact of uncertain precipitation estimates on simulated streamflows for the poorly gauged Yarlung Tsangpo basin (YTB), high mountain Asia (HMA). A process-based hydrological model at 0.5-km resolution is driven by an ensemble of precipitation estimation products (PEPs), including analyzed ground observations, high-resolution precipitation estimates, climate data records, and reanalyses over the 2008–15 control period. The model is then forced retrospectively from 1983 onward to obtain seamless discharge estimates till 2007, a period for which there is very sparse flow data coverage. Whereas temperature forcing is considered deterministic, precipitation is sampled from the predictive distribution, which is obtained through processing PEPs by means of a probabilistic processor of uncertainty. The employed Bayesian processor combines the PEPs and outputs the predictive densities of daily precipitation depth accumulation as well as the probability of precipitation occurrence, from which random precipitation fields for probabilistic model forcing are sampled. The predictive density of precipitation is conditional on the precipitation estimation predictors that are bias corrected and variance adjusted. For the selected HMA study site, discharges simulated from reanalysis and climate data records score lowest against observations at three flow gauging points, whereas high-resolution satellite estimates perform better, but are still outperformed by precipitation fields obtained from analyzed observed precipitation and merged products, which were corrected against ground observations. The applied methodology indicates how missing flows for poorly gauged sites can be retrieved and is further extendable to hydrological projections of climate. Significance Statement We show how to use different precipitation estimates, like computer simulations of weather or satellite observations, in conjunction with all available ground measurements in regions with generally poor meteorological and flow measurement infrastructure. We demonstrate how it is possible to retrieve past unobserved river flows using these estimates in combination with a hydrological computer model for streamflow simulations. The method can help us to better understand the hydrology of poorly gauged regions that play an important role in the distribution of water resources and can be affected by future changes. We applied the method to a large transboundary river basin in China. This basin holds water needed by large, densely populated regions of India that may become water constrained by warmer climate.

Evaluation of the simulation performance of WRF-Solar for a summer month in China using ground observation network data

Solar energy is a pivotal clean energy source in the transition to carbon neutrality from fossil fuels. However, the intermittent and stochastic characteristics of solar radiation pose challenges for accurate simulation and prediction. Accurately simulating and predicting solar radiation and its variability are crucial for optimizing solar energy utilization. This study conducted simulation experiments using the WRF-Solar model from 25 June to 25 July 2022, to evaluate the accuracy and performance of the simulated solar radiation across China. The simulations covered the whole country with a grid spacing of 27 km and were compared with ground observation network data from the Chinese Ecosystem Research Network. The results indicated that WRF-Solar can accurately capture the spatiotemporal patterns of global horizontal irradiance over China, but there is still an overestimation of solar radiation, and the model underestimates the total cloud cover. The root-mean-square error ranged from 92.83 to 188.13 Wm−2 and the mean bias (MB) ranged from 21.05 to 56.22 Wm−2. The simulation showed the smallest MB at Lhasa on the Qinghai–Tibet Plateau, while the largest MB was observed in Southeast China. To enhance the accuracy of solar radiation simulation, the authors compared the Fast All-sky Radiation Model for Solar with the Rapid Radiative Transfer Model for General Circulation Models and found that the former provides better simulation.摘要准确模拟和预测太阳辐射及其变化对于优化太阳能利用至关重要. 本研究使用WRF-Solar模式对中国2022 年 6 月 25 日至 7 月 25 日的太阳辐射情况进行了深入模拟, 模式网格水平分辨率为27 km, 通过与中国生态系统研究网络 (CERN) 的地面观测网络数据的37个观测站点进行对比, 以评估模式性能. 结果表明: WRF-Solar模式能较好地捕捉到中国上空的辐照度GHI (Global Horizontal Irradiance) 的时空分布特征, 但存在高估太阳辐射量, 以及低估总云量的情况. 全国的总辐射模拟均方根误差范围为92.83–188.13 W m−2, 平均偏差范围为21.05–56.22 W m−2. 青藏高原拉萨站的偏差最小, 在中国东南部的偏差最大. 为了进一步提升太阳辐射模拟的精确度, 本研究还对比了FARMS与RRTMG辐射方案, 发现FARMS方案的模拟精度更高.

Local feature acquisition and global context understanding network for very high-resolution land cover classification

Very high-resolution remote sensing images hold promising applications in ground observation tasks, paving the way for highly competitive solutions using image processing techniques for land cover classification. To address the challenges faced by convolutional neural network (CNNs) in exploring contextual information in remote sensing image land cover classification and the limitations of vision transformer (ViT) series in effectively capturing local details and spatial information, we propose a local feature acquisition and global context understanding network (LFAGCU). Specifically, we design a multidimensional and multichannel convolutional module to construct a local feature extractor aimed at capturing local information and spatial relationships within images. Simultaneously, we introduce a global feature learning module that utilizes multiple sets of multi-head attention mechanisms for modeling global semantic information, abstracting the overall feature representation of remote sensing images. Validation, comparative analyses, and ablation experiments conducted on three different scales of publicly available datasets demonstrate the effectiveness and generalization capability of the LFAGCU method. Results show its effectiveness in locating category attribute information related to remote sensing areas and its exceptional generalization capability. Code is available at https://github.com/lzp-lkd/LFAGCU.

Disentangling the Influential Factors Driving NPP Decrease in Shandong Province: An Analysis from Time Series Evaluation Using MODIS and CASA Model

Net Primary Productivity (NPP) is a critical metric for assessing terrestrial carbon sequestration and ecosystem health. While advancements in NPP modeling have enabled estimation at various scales, hidden anomalies within NPP time series necessitate further investigation to understand the driving forces. This study focuses on Shandong Province, China, generating a high-resolution (250 m) monthly NPP product for 2000–2019 using the Carnegie–Ames–Stanford Approach (CASA) model, integrated with satellite remote sensing and ground observations. We employed the Seasonal Mann–Kendall (SMK) Test and the Breaks For Additive Season and Trend (BFAST) algorithm to differentiate between gradual declines and abrupt losses, respectively. Beyond analyzing land use and land cover (LULC) transitions, we utilized Random Forest models to elucidate the influence of environmental factors on NPP changes. The findings revealed a significant overall increase in annual NPP across the study area, with a moderate average of 503.45 gC/(m2·a) during 2000–2019. Although 69.67% of the total area displayed a substantial monotonic increase, 3.89% of the area experienced abrupt NPP losses, and 8.43% exhibited gradual declines. Our analysis identified LULC transitions, primarily driven by urban expansion, as being responsible for 55% of the abrupt loss areas and 33% of the gradual decline areas. Random Forest models effectively explained the remaining areas, revealing that the magnitude of abrupt losses and the intensity of gradual declines were driven by a complex interplay of factors. These factors varied across vegetation types and change types, with explanatory variables related to vegetation status and climatic factors—particularly precipitation—having the most prominent influence on NPP changes. The study suggests that intensified land use and extreme climatic events have led to NPP diminishment in Shandong Province. Nevertheless, the prominent positive vegetation growth trends observed in some areas highlight the potential for NPP enhancement and carbon sequestration through targeted management strategies.

Radiometric Cross-Calibration of GF6-PMS and WFV Sensors with Sentinel 2-MSI and Landsat 9-OLI2

A panchromatic and multispectral sensor (PMS) and a wide-field-of-view (WFV) sensor were fitted aboard the Gaofen6 (GF6) satellite, which was launched on 2 June 2018. This study used the Landsat9-Operational Land Imager 2 and Sentinel2-Multispectral Instrument as reference sensors to perform radiometric cross-calibration on GF6-PMS and WFV data at the Dunhuang calibration site. The four selected sensor images were all acquired on the same day. The results indicate that: the calibration results between different reference sensors can be controlled within 3%, with the maximum difference from the official coefficients being 8.78%. A significant difference was observed between the coefficients obtained by different reference sensors when spectral band adjustment factor (SBAF) correction was not performed; from the two sets of validation results, the maximum mean relative difference in the near-infrared band was 9.46%, with the WFV sensor showing better validation results. The validation of calibration coefficients based on synchronous ground observation data and the analysis of the impact of different SBAF methods on the calibration results indicated that Landsat9 is more suitable as a reference sensor for radiometric cross-calibration of GF6-PMS and WFV.

Evaluation of Gridded Rainfall Products in Three West African Basins

In recent years, accessing rainfall data from ground observation networks maintained by national meteorological services in West Africa has become increasingly challenging. This is primarily due to high acquisition costs and the often sparse distribution of rainfall gauges across the region, which limits their use in hydrological studies and related research. At the same time, the rising availability of precipitation products derived from satellite/earth observations, reanalysis datasets, and in situ measurements presents exciting prospects for hydrological applications. Nonetheless, these datasets constitute indirect measurements, necessitating rigorous validation against ground-based rainfall data. This study comprehensively assesses twenty-three gridded rainfall products, including sixteen from satellites, six from reanalysis data, and one from in situ measurements, across the Senegal, Gambia, and Casamance River basins. Performance evaluation is conducted across distinct climatic zones, both pre- and post-resampling against observed rainfall data gathered from forty-nine rainfall stations over a six-year period (2003–2008). Evaluation criteria include the Kling–Gupta Efficiency (KGE) and Percentage of Bias (PBIAS) metrics, assessed at daily, monthly, and seasonal time steps. The results reveal distinct performance levels among the evaluated rainfall products. RFE, ARC2, and CPC notably yield the highest KGE scores at the daily time step, while GPCP, CHIRP, CHIRPS, RFE, MSWEP, ARC2, CPC, TAMSAT, and CMORPHCRT demonstrate superior performance at the monthly time step. During the rainy season, these products generally exhibit robustness. However, rainfall estimates derived from reanalysis datasets (ERA5, EWEMBI, MERRA2, PGF, WFDEICRU, and WFDEIGPCC) perform poorly in the studied basins. Based on the PBIAS metric, most products tend to underestimate precipitation values, while only PERSIANN and PERSIANNCCS lead to significant overestimations. Spatially, optimal performance of the products is observed in the Casamance basin and the Sudanian and Sahelian climatic zones within the Gambia and Senegal basins. Conversely, in the Guinean zone of the Gambia and Senegal Rivers, the rainfall products displayed the poorest performance.

Unraveling the Mechanism of the Holes in the Blanket of Fog Over the Indo‐Gangetic Plains: Are They Driven by Urban Heat Islands or Aerosol?

AbstractFog holes were reported over cities of the Indo‐Gangetic plains of India, which were attributed to the urban heat island (UHI) effect. The present study reports the observational evidence of fog holes over rural area using satellite and ground observations. Delayed onset and early dissipation of fog is observed over the holes. Numerical simulations demonstrate that aerosol‐radiation interaction (ARI) leads to fog holes over both urban and rural regions. The anthropogenic aerosols and associated ARI dominate the UHI effect over the urban fog holes, while the aerosol absorption due to ARI mainly controls the rural fog holes. Turning off aerosol absorption causes ∼ 2 hours delay in the fog dissipation over urban area, whereas it yields persistent fog over rural area under saturated environment. An enhancement of soluble Aitken black carbon over the fog holes indicates the importance of aging process during fog dissipation.