Multiple Satellite Observations Research Articles

Exploring Spatial and Temporal Dynamics of Red Sea Air Quality through Multivariate Analysis, Trajectories, and Satellite Observations

The Red Sea, a significant ecoregion and vital marine transportation route, has experienced a consistent rise in air pollution in recent years. Hence, it is imperative to assess the spatial and temporal distribution of air quality parameters across the Red Sea and identify temporal trends. This study concentrates on utilizing multiple satellite observations to gather diverse meteorological data and vertical tropospheric columns of aerosols and trace gases, encompassing carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O3). Furthermore, the study employs the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model to analyze the backward trajectory of air mass movement, aiding in the identification of significant sources of air pollutants. A principal component analysis (PCA) with varimax rotation is applied to explore the relationship and co-variance between the aerosol index (AI), trace gas concentrations, and meteorological data. The investigation reveals seasonal and regional patterns in the tropospheric columns of trace gases and AI over the Red Sea. The correlation analysis indicates medium-to-low positive correlations (0.2 < r < 0.6) between air pollutants (NO2, SO2, and O3) and meteorological parameters, while negative correlations (−0.3 < r < −0.7) are observed between O3, aerosol index, and wind speed. The results from the HYSPLIT model unveil long-range trajectory patterns. Despite inherent limitations in satellite observations compared to in situ measurements, this study provides an encompassing view of air pollution across the Red Sea, offering valuable insights for future researchers and policymakers.

Developing an operational algorithm for near-real-time monitoring of crop progress at field scales by fusing harmonized Landsat and Sentinel-2 time series with geostationary satellite observations

Crop phenology has been widely detected from multiple historical satellite observations. Conversely, Near-Real-Time (NRT) monitoring of crop progress from timely available remote sensing data is barely investigated because of the lack of high-frequency cloud-free satellite observations and future potential crop development. To address the challenge, this study proposes a novel algorithm for operational NRT monitoring of crop progress at the field scale. This algorithm first fuses the high spatial resolution (30 m) Harmonized Landsat and Sentinel-2 (HLS) data and the high temporal frequent (10 min) Advanced Baseline Imager (ABI) observations to generate cloud-free time series of HLS-ABI EVI2 (two-band Enhanced Vegetation Index) with a Spatiotemporal Shape-Matching Model (SSMM). It then predicts future potential EVI2 values at a given pixel using a reference EVI2 time series obtained from the neighboring pixels in the preceding year. Integrating the currently available HLS-ABI observations and the predicted future EVI2 values to generate annual EVI2 time series, the algorithm finally detects six crop phenometrics including greenup onset, mid-greenup phase, maturity onset, senescence onset, mid-senescence phase, and dormancy onset. The NRT monitoring, which are separated as near-real-time prediction (phenological event detected after the occurrence), real-time prediction (phenological event detected around the occurrence), and short-term prediction (phenological event detected before the occurrence), are continuously updated and improved with new HLS and ABI observations at a weekly basis throughout the growing season. We evaluate the NRT monitoring against standard phenology products, PhenoCam observations, as well as the weekly Crop Progress Reports (CPRs) released from the National Agricultural Statistics Service (NASS) of the United States Department of Agriculture (USDA) in 2020 across Iowa. The evaluation demonstrates the robustness of the developed algorithm in NRT monitoring of crop phenology. Although the uncertainties are relatively large for short-term prediction compared with standard detections, the real-time prediction shows that the Mean Absolute Difference (MAD) is <10 days for greenup and dormancy onset, and ∼ 5 days in the other four phenometrics. Further, the real-time prediction aligns well with PhenoCam observations (R2 = 0.96, P < 0.001) with a MAD of 7.8 days. Moreover, the HLS-ABI real-time prediction of crop phenometrics is capable of tracking NASS crop progress closely with small time shifts (≤ 5 days) and significant correlations (R2 > 0.85, P < 0.001) for various phenological stages of corn and soybean. These results prove that the algorithm could be implemented for NRT monitoring of various crop phenometrics from field, state, to national scales.

Comparisons of the Urbanization Effect on Heat Stress Changes in Guangdong during Different Periods

While rapid urbanization promotes social and economic development, it exacerbates human outdoor thermal comfort, which increases the risks to human health. This paper uses four thermal comfort indices and multiple satellite observations to explore the urbanization effect on summer heat stress in Guangdong from 1979–2018, a coastal province of China. Two types of thermal comfort index are used here, namely the direct thermal comfort index (Heat Index, HI; Temperature–Humidity Index, THI; Discomfort Index, DI) and the physiological thermal comfort index (Universal Thermal Climate Index, UTCI). We compare the differences in the urbanization effects on the changes in the three direct thermal comfort indices (HI, THI, and DI) and a physiological thermal comfort index (UTCI). The results show that all four thermal comfort indices indicate an overall warming trend. Of them, urban sites show a higher warming trend than rural sites, indicating that heat stress changes are significantly influenced by urbanization from 1979–2018, which is consistent with the effect of urbanization on surface air temperature. However, except for the UTCI, this warming of direct thermal comfort indices affected by urbanization has become insignificant under the regional vegetation greening from 2004–2018 (also consistent with surface air temperature). This is primarily attributed to the different effects of wind speed on the physiological thermal comfort index in urban and rural areas: Decreasing wind speeds in urban areas lead to an increase in UTCI, while wind speeds in rural areas increase instead and decrease UTCI, thus widening the UTCI differences between urban and rural areas. Our results indicate that urbanization has a different effect on thermal comfort indices. When using the thermal comfort index, it is necessary to consider that different thermal comfort indices may bring different results. UTCI considers more factors that affect human heat perception, so it can better describe human outdoor thermal comfort. It also highlights the importance of urban ventilation and urban greenness in mitigating urban outdoor thermal comfort in the sustainable construction of future urbanization in coastal cities.

Diagnosing Product Variability in the Soil Moisture Response to Precipitation on the Tibetan Plateau

Abstract Previous studies show that some soil moisture products have a good agreement with in situ measurements on the Tibetan Plateau (TP). However, the soil moisture response to precipitation variability in different products is yet to be assessed. In this study, we focus on the soil moisture response to precipitation variability across weekly to decadal time scales in satellite observations and reanalyses. The response of soil moisture to precipitation variability differs between products, with large uncertainties observed for variations in weekly accumulated precipitation. Using June 2009 as an example, weekly mean anomalous soil moisture varies by up to 25% between products. Across decadal time scales, soil moisture trends vary spatially and across different products. In light of the soil moisture response to precipitation at different time scales, we conclude that remote sensing products developed as part of the European Space Agency’s (ESA) Water Cycle Multimission Observation Strategy and Soil Moisture Climate Change Initiative (CCI) projects are the most reliable, followed by the Global Land Evaporation Amsterdam Model (GLEAM) dataset. Even products that strongly agree with in situ observations on daily time scales, such as the Global Land Data Assimilation System (GLDAS), show inconsistent soil moisture responses to decadal precipitation trends. European Centre for Medium-Range Weather Forecasts (ECWMF) reanalysis products have a relatively poor agreement with in situ observations compared to satellite observations and land-only reanalysis datasets. Unsurprisingly, products which show a consistent soil moisture response to precipitation variability are those mostly aligned to observations or describe the physical relationship between soil moisture and precipitation well. Significance Statement We focus on soil moisture responses to precipitation across weekly to decadal time scales by using multiple satellite observations and reanalysis products. Several soil moisture products illustrate good consistency with in situ measurements in different biomes on the Tibetan Plateau, while the response to precipitation variability differs between products, with large uncertainties observed for variations in weekly accumulated precipitation. The response of soil moisture to decadal trends in boreal summer precipitation varies spatially and temporally across products. Based on the assessments of the soil moisture response to precipitation variability across different time scales, we conclude that remote sensing products developed as part of the European Space Agency’s Water Cycle Multimission Observation Strategy and Soil Moisture Climate Change Initiative (CCI) projects are the most reliable, followed by the Global Land Evaporation Amsterdam Model (GLEAM) dataset. Reanalysis products from ECWMF show inconsistent soil moisture responses to precipitation. The results highlight the importance of using multiple soil moisture products to understand the surface response to precipitation variability and to inform developments in soil moisture modeling and satellite retrievals.

Ocean response offshore of Taiwan to super typhoon Nepartak (2016) based on multiple satellite and buoy observations

Multi-satellite and buoy observation data were used to systematically analyze the ocean response offshore of Taiwan to Super Typhoon Nepartak in 2016. The satellite data showed that a high sea surface temperature combined with a thick warm water layer and deep mixed layer provided a good thermal environment for continuous intensification of the typhoon. Two high-resolution buoys (NTU1 and NTU2) moored 375 and 175 km offshore of southeastern Taiwan were used to clarify the typhoon–ocean interaction as the typhoon approached Taiwan. The ocean conditions were similar at the two buoys before the typhoon, and both buoys were on the left side of the typhoon track and suffered similar typhoon factors (e.g., typhoon intensity and translation speed) during its passage. However, the ocean response differed significantly at the two buoys. During the forced period, the entire upper ocean was cooled at NTU1. In contrast, there was a clear three-layer vertical structure at NTU2 consisting of cool surface and deep layers with a warmer layer between the two cool layers. These responses can be attributed to strong upwelling of a cold eddy at NTU1 and vertical mixing at NTU2. These results indicate that, under similar preexisting conditions and typhoon factors, the movement of ocean eddies under typhoon forcing is an unexpected mechanism that results in upwelling and thus needs to be considered when predicting changes in the ocean environment and typhoon intensity.

Climatology and interannual variability of type-dependent aerosol optical depth and vertical distribution over southwest China and northern India from multiple satellite and aerosol reanalysis datasets

Aerosol loading and properties have an important influence on the radiative effect of aerosols and regional climate change. Based on the long-term datasets of aerosol optical products retrieved from multiple satellite observations (MODIS/Terra, MODIS/Aqua, MISR, and CALIOP/CALIPSO) and a reanalysis product (CAMS) from 2007 to 2019, the climatology and interannual variability in total aerosol optical depth (AOD) and type-dependent AODs related to aerosol particle properties and components over southwest China (SWC) and northern India (NI) are investigated, and the vertical distribution of type-dependent aerosol extinctions are also examined. Overall, consistent spatial distribution characteristics of the AOD climatology are captured across the different datasets, albeit with slight differences in magnitude. Multi-year mean high AODs are mainly concentrated in areas such as the Sichuan Basin (SCB) (∼0.5) and NI (∼0.6), dominated by anthropogenic aerosols, and the Tarim Basin (∼0.5), dominated by mineral dust aerosols. In contrast, the central Tibetan Plateau (CTP) area, with a relatively clean background, has a lower AOD (∼0.1) on a multi-year average basis. When the total AOD is separated by chemical components, the CAMS reanalysis demonstrates that sulfate and carbonaceous aerosols (CAs) contribute nearly equally to AOD in SCB, while CAs is the largest contributor to the high aerosol loading in NI. An enhanced vertical aerosol extinction coefficient (AEC) is mainly located below 1 km in both SCB and NI, reaching 0.35 km−1 and 0.55 km−1. In CTP, the AEC has two peaks (∼0.02 km−1) at 2.5 km and 3.5 km. By partitioning the column AOD by different altitude ranges, we find that about 73.8% (80.6%) of the AOD in SCB (NI) is located in the lower troposphere (0–2 km). The resulting trend analyses show that a significant decreasing (increasing) trend (P < 0.05) in AOD in SCB (NI) is accurately captured by both the satellite products and the CAMS reanalysis. In contrast, a significant decline (P < 0.1) in the CTP region is observed in CAMS only, while a non-significant decline is detected in the satellite products. The results of this study have potential implications for understanding the vertical distribution and interannual variability of regional aerosol loading.

The generation mechanism of cold eddies and the related heat flux exchanges in the upper ocean during two sequential tropical cyclones

The impacts of two sequential tropical cyclones (TCs), Kyarr and Maha, [from October 24 to November 06, 2019, over the Arabian Sea (AS)] on upper ocean environments were investigated using multiple satellite observations, Argo float profiles and numerical model outputs. To obtain a realistic TC strength, the Weather Research and Forecasting (WRF) model was used to reproduce Kyarr and Maha. During Kyarr and Maha, three distinct cold patches were observed at the sea surface with a maximum sea surface cooling of approximately 5°C. The comparison between WRF model simulation results and ERA5 wind field showed that the WRF model simulation indicated high simulation accuracy with respect to the SST decrease in the AS under the influence of Kyarr and Maha’s wind stress curls. Meanwhile, concentration of chlorophyll a (chl-a) and positive relative vorticity of sea surface also appeared in the three cold patch areas. Through the use of eddy detection algorithms, three mesoscale cold cyclonic eddies were identified along the track of TC Kyarr, and the locations of these cold eddies were highly correlated with three obvious negative sea surface height anomalies (SSHAs). The radii of the three cold eddies were 69 km, 50 km, and 41 km. With a focus on the thermodynamic responses of the three cold eddy fields to Kyarr and Maha, the central regions of the three cold eddies were explored. The central regions of the three cold eddies exhibited relatively shallow mixed-layer depths (MLDs) and low mixed-layer temperatures (MLTs). The depth integrated heat (DIH) content was also calculated to explore the heat flux exchanges occurring in different layers in the upper 200 m of the centre of each eddy. The results showed that DIH in each eddy centre varied by one order of magnitude, accounting for between 127.3 MJ m-2 and 1220.0 MJ m-2 of heat loss. This study suggests that the effect of long forcing time on intense positive wind stress curls can produce upwelling caused by Ekman response, which is the main influencing factor of the three cold eddies generation mechanism. At the same time, the positive relative vorticity injected into the sea surface also has some contribution. TC-induced vertical mixing and upwelling (strengthened by unstable structures inside the cold eddies) cause substantial redistribution of the DIH, and related heat flux exchanges at different layers occur in the eddy fields.

Features of the Extreme Fire Season of 2021 in Yakutia (Eastern Siberia) and Heavy Air Pollution Caused by Biomass Burning

Yakutia (Eastern Siberia) is one of the most fire-prone regions of Russia, which is frequently affected by large-scale wildfires despite a relatively short warm period, which usually lasts from May to September. In 2021, Yakutia experienced the worst fire season over the last four decades. In this study, we investigate features of the extreme fire season, factors that promote extreme fire weather, and heavy air pollution caused by biomass burning in the region utilizing multiple satellite and ground-based observations along with reanalysis data and forward-trajectory modelling. The results demonstrate that the total number of hotspots (HS) in 2021 amounted to ~150,000, which is almost twice as much as the previous record year (2020). One of the main features of the 2021 fire season was the period of extensive growth of the number of HS, which occurred from 24 July to 12 August. High fire danger during the fire season was promoted by positive anomalies in monthly air temperature (August) and negative anomalies in monthly precipitation (May–July). August of 2021 in central Yakutia was the second most hot August (14.9 °C) during a 43-year NCEP-DOE Reanalysis record (1979–2021). In addition, the intensification of wildfires during August 2021 was associated with persistent high-pressure systems, which promoted dry weather conditions in the region by blocking the transport of moist air masses from the western part of Russia. The low wind speeds, observed in the center of a high-pressure system, led to the accumulation of wildfire emissions in the atmosphere, which significantly affect air quality in the region. The monthly mean aerosol optical depth values in July 2021 were 0.82 (MODIS MAIAC) and 1.37 (AERONET) which were 14.9 and 18.7 times higher than respective values of 2007 (the year with minimal wildfires in the Asian part of Russia and Yakutia). Based on aerosol index observations and forward trajectories, we demonstrate that smoke plumes originated from the study area were transported over long distances reaching the Ural Mountains in the west, Mongolia in the south, the North Pole in the north, and Alaska in the east, traveling the distances of ~2000–7000 km. Maximum spatial extent of the smoke plumes reached ~10–12 mln. km2.

Improved estimation of fire particulate emissions using a combination of VIIRS and AHI data for Indonesia during 2015–2020

Smoke aerosol emissions from Indonesian fires frequently cause adverse environmental consequences across southeast Asia. Satellite observations provide us with a great opportunity to monitor such emissions at large scales. However, existing satellite-based estimates of Indonesian fire emissions vary considerably in magnitude, differing by a factor of four. Here, we aim to improve Indonesian fire emissions estimates through improved calculations of fire radiative energy (FRE: time-integrated fire radiative power (FRP)) and smoke aerosol emission coefficients (Ce) using multiple new-generation satellite observations. Specifically, peatland and non-peatland Ce values were derived from FRP and emission rates of smoke aerosols based on Visible Infrared Imaging Radiometer Suite (VIIRS) active fire and aerosol products. FRE was calculated from the diurnal FRP cycle that was reconstructed by fusing cloud-corrected FRP retrievals from the high temporal-resolution Himawari-8 Advanced Himawari Imager (AHI) with those from high spatial-resolution VIIRS. Then, fuel type-specific Ce values and fused AHI-VIIRS FRE were used to produce hourly and daily fire emission data from 2015 to 2020 across Indonesia. To evaluate AHI-VIIRS estimates, we generated a reference dataset of total particulate matter (TPM) by computing Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol observation differences from successive Terra and Aqua satellites overpasses over a set of manually-selected smoke plumes. AHI-VIIRS-based fire emissions correlated significantly with the MODIS reference data (r = 0.84; p < 0.001). The interannual time series analysis of AHI-VIIRS emissions showed extreme variability (~26 fold), with the greatest amount in 2015 (6.09 Tg) and the least in 2020 (0.23 Tg), during the study period. Mean annual fire emissions distributed across Indonesia's islands were primarily from Kalimantan (43.8%) and Sumatra (41.7%), followed by Sulawesi (5.6%), Java (4.5%), and Papua (4.4%). Additionally, contributions of Indonesian fire emissions from peatland, forest, cropland, and savanna/grassland fuel types were 51.4%, 37.8%, 7.0%, and 3.8%, respectively, during the fire season of the strong 2015 El Niño event. The analysis suggests that the majority of Indonesian fire emissions are very likely associated with large-scale land use conversion from peatland to agriculture, as well as prolonged droughts induced by El Niño events.

Reconstruction of Subsurface Salinity Structure in the South China Sea Using Satellite Observations: A LightGBM-Based Deep Forest Method

Accurately estimating the ocean’s interior structures using sea surface data is of vital importance for understanding the complexities of dynamic ocean processes. In this study, we proposed an advanced machine-learning method, the Light Gradient Boosting Machine (LightGBM)-based Deep Forest (LGB-DF) method, to estimate the ocean subsurface salinity structure (OSSS) in the South China Sea (SCS) by using sea surface data from multiple satellite observations. We selected sea surface salinity (SSS), sea surface temperature (SST), sea surface height (SSH), sea surface wind (SSW, decomposed into eastward wind speed (USSW) and northward wind speed (VSSW) components), and the geographical information (including longitude and latitude) as input data to estimate OSSS in the SCS. Argo data were used to train and validate the LGB-DF model. The model performance was evaluated using root mean square error (RMSE), normalized root mean square error (NRMSE), and determination coefficient (R2). The results showed that the LGB-DF model had a good performance and outperformed the traditional LightGBM model in the estimation of OSSS. The proposed LGB-DF model using sea surface data by SSS/SST/SSH and SSS/SST/SSH/SSW performed less satisfactorily than when considering the contribution of the wind speed and geographical information, indicating that these are important parameters for accurately estimating OSSS. The performance of the LGB-DF model was found to vary with season and water depth. Better estimation accuracy was obtained in winter and autumn, which was due to weaker stratification. This method provided important technical support for estimating the OSSS from satellite-derived sea surface data, which offers a novel insight into oceanic observations.

Addressing the difficulties in quantifying droplet number response to aerosol from satellite observations

Abstract. Aerosol–cloud interaction is the most uncertain component of the overall anthropogenic forcing of the climate, in which cloud droplet number concentration (Nd) sensitivity to aerosol (S) is a key term for the overall estimation. However, satellite-based estimates of S are especially challenging, mainly due to the difficulty in disentangling aerosol effects on Nd from possible confounders. By combining multiple satellite observations and reanalysis, this study investigates the impacts of (a) updraft, (b) precipitation, (c) retrieval errors, and (d) vertical co-location between aerosol and cloud on the assessment of S in the context of marine warm (liquid) clouds. Our analysis suggests that S increases remarkably with both cloud-base height and cloud geometric thickness (proxies for vertical velocity at cloud base), consistent with stronger aerosol–cloud interactions at larger updraft velocity for midlatitude and low-latitude clouds. In turn, introducing the confounding effect of aerosol–precipitation interaction can artificially amplify S by an estimated 21 %, highlighting the necessity of removing precipitating clouds from analyses of S. It is noted that the retrieval biases in aerosol and cloud appear to underestimate S, in which cloud fraction acts as a key modulator, making it practically difficult to balance the accuracies of aerosol–cloud retrievals at aggregate scales (e.g., 1∘×1∘ grid). Moreover, we show that using column-integrated sulfate mass concentration (SO4C) to approximate sulfate concentration at cloud base (SO4B) can result in a degradation of correlation with Nd, along with a nearly twofold enhancement of S, mostly attributed to the inability of SO4C to capture the full spatiotemporal variability of SO4B. These findings point to several potential ways forward to practically account for the major influential factors by means of satellite observations and reanalysis, aiming at optimal observational estimates of global radiative forcings due to the Twomey effect and also cloud adjustments.

Estimation of Global Irrigation Water Use by the Integration of Multiple Satellite Observations

AbstractQuantification of the global irrigation water use (IWU) is crucial to understanding the anthropogenic disturbance of the natural hydrological cycle and optimal agricultural water management. However, it is challenging to obtain time series data with the conventional survey‐based approach, while the current satellite‐based IWU estimations are subject to data gaps and the model structure. In this paper, we propose a comprehensive framework to couple the different processes associated with irrigation and integrate multiple satellite observations to estimate the global IWU. The ensemble IWU estimate demonstrates an improved performance when compared to the IWU obtained from individual satellite observations. The results show reasonable correlation with the survey‐based irrigation water withdrawal in states of the US (bias = −0.42 km3), provinces of China (bias = −3.10 km3), and country statistics from the Food and Agriculture Organization (bias = −10.84 km3). Large amounts of IWU are apparent in India, China, the US, Europe, and Pakistan, making up &gt;70% of the global IWU. A general underestimation of IWU is found both in this work and previous studies, due to the coarse resolution and asynchronism of the various satellite products, the changes in irrigated areas, and the deficiency in detecting irrigation events under the case of saturated soil moisture. Nevertheless, we demonstrate advantages in integrating multiple satellite observations to reduce the uncertainty in estimating global IWU. However, additional efforts are needed to produce high‐quality and finer spatiotemporal resolution satellite‐based products, to further improve the accuracy of the global IWU estimation.

Estimating 250-m Land Surface and Atmospheric Variables From MERSI Top-of-Atmosphere Reflectance

The medium-resolution spectral imager (MERSI) onboard China’s new generation of polar-orbiting meteorological satellite series FengYun-3 (FY-3) is providing global observations of the Earth’s environment. One of the important characteristics of the MERSI sensor is that it has four bands at 250-m spatial resolution. However, very few high-level land surface and atmospheric products have been generated from MERSI data. This article presents an effective optimization method to estimate the continuous temporal distribution of multiple land surface and atmospheric variables at a 250-m spatial resolution from time series FY-3B MERSI top-of-atmosphere (TOA) observations. The MODIS cloud detection method was applied to the MERSI TOA reflectance to determine the clear observations. Then, a physics-based BRDF correction was adopt to reduce the topographic effects using the slope angle and aspect angle of the slope. Finally, a coupled land surface-atmosphere radiative transfer (RT) model and the shuffled complex evolution (SCE) optimization algorithm were used to estimate a suit of variables, including the leaf area index (LAI), the aerosol optical depth (AOD), the cloud optical thickness (COT), the cloud effective particle radius (CER), the land surface reflectance, the shortwave and visible albedo, the incident shortwave radiation (ISR), the incident photosynthetically active radiation (PAR), the fraction of absorbed PAR (FAPAR), the surface broadband emissivity (BBE), and the TOA shortwave albedo. The method was applied to the data from the 18 SURFRAD, the America FluxNet (AmeriFlux), and the Images and Beijing Normal University net (BNUnet) sites. All the estimated variables were also compared with Moderate Resolution Imaging Spectroradiometer (MODIS) and Global Land Surface Satellite (GLASS) products. The estimated LAI, PAR, FAPAR, shortwave albedo, and ISR were also validated using ground measurements from the selected sites. The results show that our method can simultaneously estimate multiple temporally continuous variables in both clear-and cloudy-sky conditions, with an accuracy comparable to those of MODIS and GLASS products. The estimated LAI, PAR, FAPAR, shortwave albedo, and ISR are consistent with the field measurements, with coefficients of determination ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> ) of 0.692, 0.783, 0.788, 0.559, and 0.833, and root mean square errors (RMSEs) of 0.427, 56.681 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , 0.092, 0.084, and 115.305 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , respectively. The proposed algorithm can effectively estimate the instantaneous atmospheric and land surface variables directly from satellite observations without cumbersome atmospheric corrections. Moreover, it can potentially be applied to multiple satellite observations.

A Pixel-Based Vegetation Greenness Trend Analysis over the Russian Tundra with All Available Landsat Data from 1984 to 2018

As Arctic warming continues, its impact on vegetation greenness is complex, variable and inherently scale-dependent. Studies with multiple spatial resolution satellite observations, with 30 m resolution included, on tundra greenness have been implemented all over the North American tundra. However, finer resolution studies on the greenness trends in the Russian tundra have only been carried out at a limited local or regional scale and the spatial heterogeneity of the trend remains unclear. Here, we analyzed the fine spatial resolution dataset Landsat archive from 1984 to 2018 over the entire Russian tundra and produced pixel-by-pixel greenness trend maps with the support of Google Earth Engine (GEE). The entire Russian tundra was divided into six geographical regions based on World Wildlife Fund (WWF) ecoregions. A Theil–Sen regression (TSR) was used for the trend identification and the changed pixels with a significance level p &lt; 0.05 were retained in the final results for a subsequent greening/browning trend analysis. Our results indicated that: (1) the number of valid Landsat observations was spatially varied. The Western and Eastern European Tundras (WET and EET) had denser observations than other regions, which enabled a trend analysis during the whole study period from 1984 to 2018; (2) the most significant greening occurred in the Yamal-Gydan tundra (WET), Bering tundra and Chukchi Peninsula tundra (CT) during 1984–2018. The EET had a greening trend of 2.3% and 6.6% and the WET of 3.4% and 18% during 1984–1999 and 2000–2018, respectively. The area of browning trend was relatively low when we first masked the surface water bodies out before the trend analysis; and (3) the Landsat-based greenness trend was broadly similar to the AVHRR-based trend over the entire region but AVHRR retrieved more browning areas due to spectral mixing adjacent effects. Higher resolution images and field measurement studies are strongly needed to understand the vegetation trend over the Russian tundra ecosystem.

Impact of variability in the hydrological cycle components on vegetation growth in an alpine basin of the southeastern Tibet Plateau, China

Abstract Vegetation is affected by hydrological cycle components that have altered under the influence of climate change. Therefore, it is necessary to investigate the impact of hydrological cycle components on regional vegetation growth, especially in alpine regions. In this study, we employed multiple satellite observations to comprehensively investigate the spatial heterogeneity of hydrological cycle components in the Yarlung Zangbo River (YZR) basin for the period 1982–2014 and to determine the underlying mechanisms driving regional vegetation growth. Results showed that the normalized difference vegetation index (NDVI) values during May–October were high, and the NDVI values increased from the upper reaches of the YZR to its lower reaches, reflecting the enhancement of vegetation growth. Annual precipitation, precipitation-actual evapotranspiration (AET), and snow water equivalent (SWE) all affect terrestrial water storage in the YZR basin through changes in soil moisture (SM), i.e., SM is the intermediate variable. Seasonal variability of vegetation is controlled mainly by precipitation, temperature, AET, SM anomaly, and SWE. Groundwater storage anomalies (GWA) and terrestrial water storage anomalies (TWSA) were not reliable indicators of vegetation growth in the YZR basin and the midstream and downstream regions. The effects of GWA and TWSA on vegetation occurred in the upstream region.

The Response and Feedback of Ocean Mesoscale Eddies to Four Sequential Typhoons in 2014 Based on Multiple Satellite Observations and Argo Floats

Four sequential tropical cyclones generated and developed in the Northwest Pacific Ocean (NWP) in 2014, which had significant impacts on the oceanic environment and coastal regions. Based on a substantial dataset of multiple-satellite observations, Argo profiles, and reanalysis data, we comprehensively investigated the interactions between the oceanic environment and sequential tropical cyclones. Super typhoon Neoguri (2014) was the first typhoon-passing studied area, with the maximum sustained wind speed of 140 kts, causing strong cold wake along the track. The location of the strongest cold wake was consistent with the pre-existing cyclonic eddy (CE), in which the average sea surface temperature (SST) cooling exceeded −5 °C. Subsequently, three tropical cyclones passed the ocean environment left by Neoguri, namely, the category 2 typhoon Matmo (2014), the tropical cyclone Nakri (2014) and the category 5 typhoon Halong (2014), which caused completely different subsequent responses. In the CE, due to the fact that the ocean stratification was strongly destroyed by Neoguri and difficult to recover, even the weak Nakri could cause a secondary response, but the secondary SST cooling would be overridden by the first response and thus could cause no more serious ocean disasters. If the subsequent typhoon was super typhoon Halong, it could cause an extreme secondary SST cooling, exceeding −8 °C, due to the deep upwelling, exceeding 700 m, surpassing the record of the maximum cooling caused by the first typhoon. In the anti-cyclonic eddy (AE), since the first typhoon Neoguri caused strong seawater mixing, it was difficult for the subsequent weak typhoons to mix the deeper, colder and saltier water into the surface, thus inhibiting secondary SST cooling, and even the super typhoon Halong would only cause as much SST cooling as the first typhoon. Therefore, the ocean responses to sequential typhoons depended on not only TCs intensity, but also TCs track order and ocean mesoscale eddies. In turn, the cold wake caused by the first typhoon, Neoguri, induced different feedback effects on different subsequent typhoons.

The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations

Abstract. The terrestrial forest carbon pool is poorly quantified, in particular in regions with low forest inventory capacity. By combining multiple satellite observations of synthetic aperture radar (SAR) backscatter around the year 2010, we generated a global, spatially explicit dataset of above-ground live biomass (AGB; dry mass) stored in forests with a spatial resolution of 1 ha. Using an extensive database of 110 897 AGB measurements from field inventory plots, we show that the spatial patterns and magnitude of AGB are well captured in our map with the exception of regional uncertainties in high-carbon-stock forests with AGB &gt;250 Mg ha−1, where the retrieval was effectively based on a single radar observation. With a total global AGB of 522 Pg, our estimate of the terrestrial biomass pool in forests is lower than most estimates published in the literature (426–571 Pg). Nonetheless, our dataset increases knowledge on the spatial distribution of AGB compared to the Global Forest Resources Assessment (FRA) by the Food and Agriculture Organization (FAO) and highlights the impact of a country's national inventory capacity on the accuracy of the biomass statistics reported to the FRA. We also reassessed previous remote sensing AGB maps and identified major biases compared to inventory data, up to 120 % of the inventory value in dry tropical forests, in the subtropics and temperate zone. Because of the high level of detail and the overall reliability of the AGB spatial patterns, our global dataset of AGB is likely to have significant impacts on climate, carbon, and socio-economic modelling schemes and provides a crucial baseline in future carbon stock change estimates. The dataset is available at https://doi.org/10.1594/PANGAEA.894711 (Santoro, 2018).

Enhanced Oceanic Environmental Responses and Feedbacks to Super Typhoon Nida (2009) during the Sudden-Turning Stage

The ocean surface and subsurface biophysical responses and their feedbacks to super typhoon Nida were comprehensively investigated based on a substantial dataset of multiple-satellite observations, Argo profiles, and reanalysis data. Nida experienced two Category 5 stages: a rapid intensification stage that was fast moving along a straight-line track, and a rapid weakening stage that was slowly moving along a sharp-left sudden-turning track. During the straight-line stage, Nida caused an average sea surface temperature (SST) cooling of 1.44 °C and a chlorophyll-a (chl-a) concentration increase of 0.03 mg m−3. During the sudden-turning stage, cyclonic sudden-turning induced a strong cold cyclonic eddy (SSHA &lt; −60 cm) by strong upwelling, which caused the maximum SST cooling of 6.68 °C and a long-lasting chl-a bloom of 0.6 mg m−3 on the left-hand side of the track, resulting in substantial impacts on the ocean ecological environment. Furthermore, the enhanced ocean cold wake and the longer air–sea interaction in turn decreased the average inner-core SST of 4 °C and the corresponding enthalpy flux of 780 W m−2, which induced a notable negative feedback to the typhoon intensity by weakening it from Category 5 to Category 2. The left bias response and notable negative feedback are special due to sharp-left sudden-turning of typhoon. Comparing with the previously found slow translation speed (~4 m s−1) of significant ocean response, the negative feedback requires even more restriction of translation speed (&lt;2 m s−1) and sharp sudden-turning could effectively relax restrictions by making equivalent translation speed lower and air-sea interaction time longer. Our findings point out that there are some unique features in ocean–typhoon interactions under sudden-turning and/or lingering tracks comparing with ordinary tracks.

Persistent Occurrence of Strip‐Like Plasma Density Bulges at Conjugate Lower‐Mid Latitudes During the September 8–9, 2017 Geomagnetic Storm

AbstractDuring the geomagnetic storm on September 8–9, 2017, shoulder‐like structures were persistently captured in the lower‐mid latitudes at multiple local times, by Swarm and DMSP satellite observations. A more telling terminology of such shoulder‐like structure called strip‐like bulge is proposed in this study, due to its global presence that spanning over 150° in longitude but only over 1°–5° in latitude. The strip‐like bulges can be categorized into sharp and blunt types depending on the sharpness of the density peaks. The blunt type was short‐lived and appeared earlier than the sharp type in the afternoon‐sunset sector. The sharp type was long‐lived and appeared at all the observed local times. Both two types of strip‐like bulges were dominated by the ion composition of the H+/He+, rather than O+. This is the first evidence that the plasmaspheric particles are involved in forming the ionospheric structure at such low latitude. Moreover, the equatorward movement of the bulges shared the same trends with the earthward movement of the plasmapause. These two types of strip‐like bulges showed different longitudinal dependencies controlled by the magnetic declination. We suggest that the combined effects from the plasmaspheric downwelling and disturbance neutral wind were responsible for the appearance of the strip‐like bulges.

Impacts of the Kuroshio Intrusion through the Luzon Strait on the Local Precipitation Anomaly

The Kuroshio Current has its origin in the northwestern Pacific, flowing northward to the east of Taiwan and the northern part of Luzon Island. As the Kuroshio Current flows northward, it quasi-periodically intrudes (hereafter referred to as Kuroshio intrusion (KI)) into the northern South China Sea (SCS) basin through the Luzon Strait. Despite the complex generation mechanisms of KI, the purpose of this study is to improve our understanding of the effects of KI through the Luzon Strait on the regional atmospheric and weather variations. Long-term multiple satellite observations, including absolute dynamic topography, absolute geostrophic currents, sea surface winds by ASCAT, multi-scale ultra-high resolution sea surface temperature (MURSST) level-four analysis, and research-quality three-hourly TRMM multi-satellite precipitation analysis (TMPA), was used to systematically examine the aforementioned scientific problem. Analysis indicates that the KI is interlinked with the consequential anomalous precipitation off southwestern Taiwan. This anomalous precipitation would lead to ~560 million tons of freshwater influx during each KI event. Subsequently, independent moisture budget analysis suggests that moisture, mainly from vertical advection, is the possible source of the precipitation anomaly. Additionally, a bulk formula analysis was applied to understand how KI can trigger the precipitation anomaly through vertical advection of moisture without causing an evident change in the low-level flows. These new research findings might reconcile the divisiveness on why winds are not showing a synchronous response during the KI and consequential anomalous precipitation events.