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The 2019 International Symposium on Remote Sensing (ISRS-2019) took place in Taipei, Taiwan from 17 to 19 April 2019. ISRS is one of the distinguished conferences on the photogrammetry, remote sensing and spatial information sciences, especially in East Asia. More than 220 papers were presented in 37 technical sessions organized at the conference. This Special Issue publishes a limited number of featured peer-reviewed papers extended from their original contributions at ISRS-2019. The selected papers highlight a variety of topics pertaining to innovative concepts, algorithms and applications with geospatial sensors, systems, and data, in conjunction with emerging technologies such as artificial intelligence, machine leaning and advanced spatial analysis algorithms. The topics of the selected papers include the following: the on-orbit radiometric calibration of satellite optical sensors, environmental characteristics assessment with remote sensing, machine learning-based photogrammetry and image analysis, and the integration of remote sensing and spatial analysis. The selected contributions also demonstrate and discuss various sophisticated applications in utilizing remote sensing, geospatial data, and technologies to address different environmental and societal issues. Readers should find the Special Issue enlightening and insightful for understanding state-of-the-art remote sensing and spatial information science research, development and applications. [ABSTRACT FROM AUTHOR]

Total electron content (TEC) is a vital parameter for describing the state of the ionosphere, and precise prediction of TEC is of great significance for improving the accuracy of the Global Navigation Satellite System (GNSS). At present, most deep learning prediction models just consider TEC temporal variation, while ignoring the impact of spatial location. In this paper, we propose a TEC prediction model, ED-ConvLSTM, which combines convolutional neural networks with recurrent neural networks to simultaneously consider spatiotemporal features. Our ED-ConvLSTM model is built based on the encoder-decoder architecture, which includes two modules: encoder module and decoder module. Each module is composed of ConvLSTM cells. The encoder module is used to extract the spatiotemporal features from TEC maps, while the decoder module converts spatiotemporal features into predicted TEC maps. We compared the predictive performance of our model with two traditional time series models: LSTM, GRU, a spatiotemporal mode1 ConvGRU, and the TEC daily forecast product C1PG provided by CODE on a total of 135 grid points in East Asia (10°–45°N, 90°–130°E). The experimental results show that the prediction error indicators MAE, RMSE, MAPE, and prediction similarity index SSIM of our model are superior to those of the comparison models in high, normal, and low solar activity years. The paper also analyzed the predictive performance of each model monthly. The experimental results indicate that the predictive performance of each model is influenced by the monthly mean of TEC. The ED-ConvLSTM model proposed in this paper is the least affected and the most stable by the monthly mean of TEC. Additionally, the paper compared the predictive performance of each model during two magnetic storm periods when TEC changes sharply. The results indicate that our ED-ConvLSTM model is least affected during magnetic storms and its predictive performance is superior to those of the comparative models. This paper provides a more stable and high-performance TEC spatiotemporal prediction model. [ABSTRACT FROM AUTHOR]

The ratio of the total electron content (TEC) to the F2-layer peak electron density (NmF2) is known as the ionospheric equivalent slab thickness (EST, also known as τ), and it is a crucial indicator of the ionosphere. Using TEC and NmF2 data from the years 2010 to 2017, this work conducts a comprehensive statistical analysis of the ionospheric slab thickness in Beijing, which is in the midlatitude of East Asia. The outcomes show that the τ have different diurnal variations at different seasons for high/low-solar-activity years. On the whole, daytime τ significantly greater than nighttime τ in summer, and it is the opposite for the τ in winter regardless of the solar cycle, whereas the τ during equinox shows different morphology for high/low-solar-activity years. Specifically, daytime τ is larger than nighttime τ during equinox in years of high-solar activity, while the opposite situation applies for the τ during equinox in years of low-solar activity. Moreover, the pre-sunrise and post-sunset peaks are most pronounced during winter for low-solar-activity years. In summer, there is a great increase in τ during the morning hours when compared with other seasons. Furthermore, the τ decreases with the solar activity during nighttime, whereas it seems there is no correlation between daytime τ and solar activity. This paper explained the primary diurnal variations in τ across different seasons during high-/low-solar-activity years by analyzing relative fluctuations of TEC and NmF2 throughout the corresponding period. In addition, based on the disturbance index (DI), which is calculated by instantaneous τ and its corresponding median, this paper found that the storm-time τ might increase when compared with its median value during the daytime, while it may both increase and decrease during the nighttime, especially around dawn and dusk hours. To further analyze the physical mechanism, an example on 2 October 2013 is also presented. The results indicate that the positive disturbance of τ during the main phase of a geomagnetic storm might be caused by the prompt penetration electric field and neutral wind during the storm, and the τ increases during the early recovery phase might be due to the disturbance dynamo electric field as well as the neutral wind during the storm. Moreover, there is a negative disturbance of τ in the recovery phase during the most disturbed sunrise hours, and it might be due to the electric field reversal, neutral wind or other factors during this period. This paper notes the differences of τ in midlatitude between different longitudinal sectors from the related climatology and storm-time behavior, as it would be helpful to improve the current understanding of τ at midlatitudes in East Asia. [ABSTRACT FROM AUTHOR]

Spread-F (SF) is one of the most important types of the ionospheric irregularities as it causes ionospheric scintillation which can severely affect the performance and reliability of communication, navigation, and radar systems. The ionosonde provides the most effective and economical way to study the ionosphere and SF. However, the manual identification of SF from an ionogram is boring and hard work. To automatically identify SF on the ionogram and extend the study of SF into the middle and low latitudes of East Asia, this paper presents a statistical analysis of SF in this region, based on the naïve Bayesian classifier. The results showed that the accuracy of automatic identification reached up to 97% on both the validation datasets and test datasets composed of Mohe, I-Cheon, Jeju, Wuhan, and Sanya ionograms, suggesting that it is a promising way to automatically identify SF on ionograms. Based on the classification results, the statistical analysis shows that SF has a complicated morphology in the middle and low latitudes of East Asia. Specifically, there is a peak of occurrence of SF in the summer in I-Cheon, Jeju, Sanya, and Wuhan; however, the Mohe station has the highest occurrence rate of SF in December. The different seasonal variations of SF might be due to the different geographic local conditions, such as the inland-coastal differences and formation mechanism differences at these latitudes. Moreover, SF occurs more easily in the post-midnight hours when compared with the pre-midnight period in these stations, which is consistent with the previous results. Furthermore, this paper extracts the frequency SF (FSF) index and range SF (RSF) index to characterize the features of SF. The results shows that the most intense FSF/RSF appeared in the height range of 220–300 km/1–7 MHz in these stations, although there are different magnitude extensions on different season in these regions. In particular, strong spread-F (SSF) reached its maximum at the equinox at Sanya, confirming the frequent SSF occurrence at the equinox at the equator and low latitudes. These results would be helpful for understanding the characteristics of SF in East Asia. [ABSTRACT FROM AUTHOR]

In this paper, a statistical analysis of the diurnal, seasonal and solar cycle variation in the TEC longitudinal difference in midlatitudes of East Asia is presented using CODE GIMs data in 2015–2019. Moreover, the empirical neutral wind model HWM-14 and geomagnetic field model IGRF-2020 were employed to analyze the influence of geomagnetic configuration-neutral wind mechanism on the TEC longitudinal difference, and the F2 layer peak electron density (NmF2) data from the Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) were also used to study the role of local electron density in the TEC longitudinal difference. For the high solar activity year, the results show that east-west TEC longitudinal difference index Re/w is negative in the noon and positive at evening-night. Moreover, the longitudinal difference of daytime TEC is most evident in summer, less in autumn and least in spring and winter, while the nighttime difference is most obvious in equinox, followed by summer and winter during nighttime. The model simulation shows that the TEC longitude difference around noon is mainly caused by the zonal wind-declination mechanism, and a 4-h time delay seems to be an optimal result for the vertical drift velocity to cause the longitudinal TEC difference during pre-noon hours. At night, the uplifting electron flux, which is a product of local electron density and vertical drift velocity, shows a good correlation with Re/w, indicating that the local electron density is also an important factor affecting the TEC longitudinal difference during the nighttime. Moreover, there was about a 3-h time delay between the TEC longitudinal variations and the uplifting electron flux at night. For the low solar activity years, the western TEC is greater than eastern TEC during most of the year except in the summer nighttime. The TEC diurnal variation in the east and west suggested that the nighttime Re/w should be related to other physical process, such as the midlatitude summer nighttime anomaly (MSNA) in the east and the ionospheric nighttime enhancement (INE). The current study provides evidence for the longitudinal difference of NmF2 in East Asian midlatitudes and geomagnetic configuration-neutral wind mechanism proposed in previous studies and finds some new features which need further studying to improve our current understanding of ionospheric longitudinal difference in the low solar activity years. The results provide new insight into TEC longitudinal variations at midlatitudes, and they can contribute to understanding the ionosphere-thermosphere coupling system. [ABSTRACT FROM AUTHOR]

The ionospheric equivalent slab thickness (EST, also named τ) is defined as the ratio of the total electron content (TEC) to the F2-layer peak electron density (NmF2), and it is a significant parameter representative of the ionosphere. This paper presents a comprehensive statistical study of the ionospheric slab thickness at Yakutsk, located at the high latitude of East Asia, using the GPS-TEC and ionosonde NmF2 data for the years 2010–2017. The results show that the τ has different diurnal and seasonal variations in high- and low-solar-activity years, and the τ is greatest in the winter, followed by the equinox, and it is smallest in the summer in both high- and low-solar-activity years, except during the noontime of low-solar-activity years. Specifically, the τ in inter of high-solar-activity year shows an approximate single peak pattern with the peak around noon, while it displays a double-peak pattern with the pre-sunrise and sunset peaks in winter of the low-solar-activity years. Moreover, the τ in the summer and equinox have smaller diurnal variations, and there are peaks with different magnitudes during the sunrise and post-sunset periods. The mainly diurnal variation of τ in different seasons of high- and low-solar-activity years can be explained within the framework of relative variation of TEC and NmF2 during the corresponding period. By defining the disturbance index (DI), which can visually assess the relationship between instantaneous values and the median, we found that the geomagnetic storm would enhance the τ at Yakutsk. An example on 7 June 2013 is also presented to analyze the physical mechanism. It should be due to the intense particle precipitation and expanded plasma convection electric field during the storm at high-latitude Yakutsk station. The results would improve the current understanding of climatological and storm-time behavior of τ at high latitudes in East Asia. [ABSTRACT FROM AUTHOR]

Computational intelligence techniques have been widely used for automatic building detection from high-resolution remote sensing imagery and especially the methods based on neural networks. However, existing methods do not pay attention to the value of high-frequency and low-frequency information in the frequency domain for feature extraction of buildings in remote sensing images. To overcome these limitations, this paper proposes a frequency spectrum intensity attention network (FSIANet) with an encoder–decoder structure for automatic building detection. The proposed FSIANet mainly involves two innovations. One, a novel and plug-and-play frequency spectrum intensity attention (FSIA) mechanism is devised to enhance feature representation by evaluating the informative abundance of the feature maps. The FSIA is deployed after each convolutional block in the proposed FSIANet. Two, an atrous frequency spectrum attention pyramid (AFSAP) is constructed by introducing FSIA in widely used atrous spatial pyramid pooling. The AFSAP is able to select the features with high response to building semantic features at each scale and weaken the features with low response, thus enhancing the feature representation of buildings. The proposed FSIANet is evaluated on two large public datasets (East Asia and Inria Aerial Image Dataset), which demonstrates that the proposed method can achieve the state-of-the-art performance in terms of F1-score and intersection-over-union. [ABSTRACT FROM AUTHOR]

High temporal resolution and spatially complete (seamless) land surface temperature (LST) play a crucial role in numerous geoscientific aspects. This paper proposes a data fusion method for producing hourly seamless LST from Himawari-8 Advanced Himawari Imager (AHI) data. First, the high-quality hourly clear-sky LST was retrieved from AHI data by an improved temperature and emissivity separation algorithm; then, the hourly spatially complete China Land Data Assimilation System (CLDAS) LST was calibrated by a bias correction method. Finally, the strengths of the retrieved AHI LST and bias-corrected CLDAS LST were combined by the multiresolution Kalman filter (MKF) algorithm to generate hourly seamless LST at different spatial scales. Validation results showed the bias and root mean square error (RMSE) of the fused LST at a finer scale (0.02°) were −0.65 K and 3.38 K under cloudy sky conditions, the values were −0.55 K and 3.03 K for all sky conditions, respectively. The bias and RMSE of the fused LST at the coarse scale (0.06°) are −0.46 K and 3.11 K, respectively. This accuracy is comparable to the accuracy of all-weather LST derived by various methods reported in the published literature. In addition, we obtained the consistent LST images across different scales. The seamless finer LST data over East Asia can not only reflect the spatial distribution characteristics of LST during different seasons, but also exactly present the diurnal variation of the LST. With the proposed method, we have produced a 0.02° seamless LST dataset from 2016 through 2021 that is freely available at the National Tibetan Plateau Data Center. It is the first time that we can obtain the hourly seamless LST data from AHI. [ABSTRACT FROM AUTHOR]

East Asia is the second-largest mineral dust source in the world, after the Sahara. When dispersed in the atmosphere, mineral dust can alter the Earth's radiation budget by changing the atmosphere's absorption and scattering properties. Therefore, the mineralogical composition of dust is key to understanding the impact of mineral dust on the atmosphere. This paper presents new information on mineralogical dust during East Asian dust events that were obtained from laboratory dust measurements combined with satellite remote sensing dust detections from the Infrared Atmospheric Sounding Interferometer (IASI). However, the mineral dust in this region is lifted above the continent in the lower troposphere, posing constraints due to the large variability in the Land Surface Emissivity (LSE). First, a new methodology was developed to correct the LSE from a mean monthly emissivity dataset. The results show an adjustment in the IASI spectra by acquiring aerosol information. Then, the experimental extinction coefficients of pure minerals were linearly combined to reproduce a Gobi dust spectrum, which allowed for the determination of the mineralogical mass weights. In addition, from the IASI radiances, a spectral dust optical thickness was calculated, displaying features identical to the optical thickness of the Gobi dust measured in the laboratory. The linear combination of pure minerals spectra was also applied to the IASI optical thickness, providing mineralogical mass weights. Finally, the method was applied after LSE optimization, and mineralogical evolution maps were obtained for two dust events in two different seasons and years, May 2017 and March 2021. The mean dust weights originating from the Gobi Desert, Taklamakan Desert, and Horqin Sandy Land are close to the mass weights in the literature. In addition, the spatial variability was linked to possible dust sources, and it was examined with a backward trajectory model. Moreover, a comparison between two IASI instruments on METOP-A and -B proved the method's applicability to different METOP platforms. Due to all of the above, the applied method is a powerful tool for exploiting dust mineralogy and dust sources using both laboratory optical properties and IASI detections. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) can detect ground information with high precision, which provides another opportunity for the retrieval of rain. Rainfall intensities in East Asia are mainly moderate. The current retrieval algorithms have high accuracy in rainstorms, but they overestimate the rainfall intensity greatly in moderate rain. Therefore, it is very important to reduce the retrieval error of SAR in moderate rain. After analyzing the scattering model of precipitation, this paper proposes an algorithm for retrieving 2-D moderate rain distribution (MRA). Since the 2-D distribution of rain is related to the vertical and horizontal distributions, MRA combines the empirical regression equation with the directional model of rain rates at different levels to retrieve the vertical distribution of precipitation. Compared with the model-oriented statistical (MOS) algorithm, MRA reduces the root mean square error when retrieving the surface rain rate from 2.6 to 0.1. In addition, based on the high-precision rain parameters retrieved by MRA, the horizontal distribution is retrieved through the likelihood distance. This horizontal distribution retrieval method not only has less amount of calculation but also avoids the difficulties of mathematical analysis. [ABSTRACT FROM AUTHOR]

Deep convolutional neural network (DCNN)-based methods have shown great improvements in building extraction from high spatial resolution remote sensing images. In this paper, we propose a postprocessing method based on DCNNs for building extraction. Specifically, building regions and boundaries are learned simultaneously or separately by DCNNs. The predicted building regions and boundaries are then combined by the postprocessing method to produce the final building regions. In addition, we introduce a manually labeled dataset based on high spatial resolution images for building detection, the XIHU building dataset. This dataset is then used in the experiments to evaluate our methods. Besides the WHU building dataset, East Asia (WHUEA) is also included. Results demonstrate that our method that combines the results of DeepLab and BDCN shows the best performance on the XIHU building dataset, which achieves 0.78% and 23.30% F1 scores, and 1.13% and 28.45% intersection-over-union (IoU) improvements compared with DeepLab and BDCN, respectively. Additionally, our method that combines the results of Mask R-CNN and DexiNed performs best on the WHUEA dataset. Moreover, our methods outperform the state-of-the-art multitask learning network, PMNet, on both XIHU and WHUEA datasets, which indicates that the overall performance can be improved although building regions and boundaries are learned in the training stage. [ABSTRACT FROM AUTHOR]

This paper presents a new algorithm to retrieve the aerosol optical depth (AOD) from a Himawari-8 Advanced Himawari Imager (AHI). Six typical aerosol models that derived from the long-term ground-based observations of East Asia are used in AOD retrieval. To accurately determine the surface reflectance, improved channel relationships between red, blue, and shortwave infrared (SWIR) are built up according to the infrared Normalized Difference Vegetation Index (NDVISWIR). Based on the new derived aerosol models and improved channel relationships, AOD over East Asian is retrieved by using the AHI data. The results are compared with Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products (MOD04 and MYD04) and yielded a correlation coefficient lager than 0.8 (R = 0.87 and 0.92, respectively). In addition, the retrieved AOD values are also validated by ground-based measurements at 12 Aerosol Robotic Network (AERONET) locations and revealed a good agreement between them (R = 0.86). [ABSTRACT FROM AUTHOR]

In recent decades, eutrophication in inland and coastal waters (ICWs) has increased due to anthropogenic activities and global warming, thus requiring timely monitoring. Compared with traditional sampling and laboratory analysis methods, satellite remote sensing technology can provide macro-scale, low-cost, and near real-time water quality monitoring services. The Geostationary Ocean Color Imager (GOCI), aboard the Communication Ocean and Meteorological Satellite (COMS) from the Republic of Korea, marked a significant milestone as the world's inaugural geostationary ocean color observation satellite. Its operational tenure spanned from 1 April 2011 to 31 March 2021. Over ten years, the GOCI has observed oceans, coastal waters, and inland waters within its 2500 km × 2500 km target area centered on the Korean Peninsula. The most attractive feature of the GOCI, compared with other commonly used water color sensors, was its high temporal resolution (1 h, eight times daily from 0 UTC to 7 UTC), providing an opportunity to monitor ICWs, where their water quality can undergo significant changes within a day. This study aims to comprehensively review GOCI features and applications in ICWs, analyzing progress in atmospheric correction algorithms and water quality monitoring. Analyzing 123 articles from the Web of Science and China National Knowledge Infrastructure (CNKI) through a bibliometric quantitative approach, we examined the GOCI's strength and performance with different processing methods. These articles reveal that the GOCI played an essential role in monitoring the ecological health of ICWs in its observation coverage (2500 km × 2500 km) in East Asia. The GOCI has led the way to a new era of geostationary ocean satellites, providing new technical means for monitoring water quality in oceans, coastal zones, and inland lakes. We also discuss the challenges encountered by Geostationary Ocean Color Sensors in monitoring water quality and provide suggestions for future Geostationary Ocean Color Sensors to better monitor the ICWs. [ABSTRACT FROM AUTHOR]

Based on OMI/MLS data (2005–2020) and Community Earth System Model (CESM2) simulated results (2001–2020), annual variation trends of tropospheric column ozone (TCO) in the recent two decades are explored, and the separate impacts of meteorological conditions and emissions on TCO are quantified. The stratospheric ozone tracer (O3S) is used to quantify the contribution of stratospheric ozone to the trend of TCO. The evaluation shows that the simulated results capture the spatial-temporal distributions and the trends of tropospheric column ozone well. Over the East Asia and Southeast Asia regions, TCO is increasing, with a rate of ~0.2 DU/yr, which is primarily attributed to the emission changes in ozone precursors, nitrogen oxide (NOx) and volatile organic chemicals (VOCs). But the changes in meteorological conditions weaken the increase in TCO, even leading to a decrease in East Asia in spring and summer. TCO is decreasing in the middle and high latitudes of the southern hemisphere, which is mainly attributed to the changes in meteorological conditions. The increasing rates are the highest in autumn, especially over North America, East Asia, Europe and South of East Asia, with rate values of 0.20, 0.31, 0.17, and 0.32 DU/yr, respectively. Over the equatorial region, the contribution of stratospheric ozone to TCO is below 10 DU, and shows a weak positive trend of ~0.2 DU/yr. In the latitude of ~30°N/S, the stratospheric contribution is high, ~25 DU, and is affected by the sinking branch of the Brewer–Dobson circulation and stratosphere–troposphere exchange in the vicinity of tropical jet stream. The stratospheric contribution to TCO in the north of 30°N is significantly decreasing (~0.6 DU/yr) under the influence of meteorological conditions. Changes in emissions weaken the decrease in stratospheric contributions in the north of 30°N and enhance the increase in 30°S–30°N significantly. The trends of stratospheric contributions on TCO partly explain the trends of TCO which are mostly affected by the change in emissions. To control the increasing TCO, actions to reduce emissions are urgently needed. [ABSTRACT FROM AUTHOR]

With the development of the Chinese Fengyun satellite series, Fengyun-2G (FY-2G) quantitative precipitation estimates (QPE) can provide real-time and high-quality precipitation data over East Asia. However, FY-2G QPE cannot offer precipitation information beyond the latitude band of 50°N due to the limitation of the observation coverage of the FY-2G-based satellite-borne sensor. To this end, a precipitation space reconstruction using the geographically weighted regression (GWR) coupled with a geographical differential analysis (GDA) (PSR2G) algorithm was developed, based on the land surface variables related to precipitation, including vegetational cover, land surface temperature, geographical location, and topographic characteristics. This study used the PSR2G-based reconstructed model to estimate the FY-2G QPE over Northeast China (the latitude band beyond 50°N) from December 2015 to November 2019 with a spatiotemporal resolution of 0.1°/month. The PSR2G-based reconstructed results were validated with the ground observations of 80 rain gauges, and also compared to the reconstructed results using random forest (RF) and GWR. The results show that the spatio-temporal pattern of PSR2G QPE is closer to ground observations than those of RF and GWR, which indicates that the PSR2G QPE is more competent to capture the spatio-temporal variation of rainfall over Northeast China than other two reconstruction methods. In addition, the reconstructed precipitation dataset using PSR2G has higher accuracy over study area than the FY-2G QPE below the band of 50°N. It suggested that PSR2G reconstruction precipitation strategies do not lose the precision of the original satellite precipitation data. [ABSTRACT FROM AUTHOR]

Crop type maps are critical for tracking agricultural land use and estimating crop production. Remote sensing has proven an efficient and reliable tool for creating these maps in regions with abundant ground labels for model training, yet these labels remain difficult to obtain for many regions and years. NASA's Global Ecosystem Dynamics Investigation (GEDI) spaceborne LiDAR instrument, originally designed for forest monitoring, has shown promise for distinguishing tall and short crops. In the current study, we leverage GEDI to develop wall-to-wall maps of short vs. tall crops on a global scale at 10 m resolution for 2019–2021. Specifically, we show that (i) GEDI returns can reliably be classified into tall and short crops after removing shots with extreme view angles or topographic slope, (ii) the frequency of tall crops over time can be used to identify months when tall crops are at their peak height, and (iii) GEDI shots in these months can then be used to train random forest models that use Sentinel-2 time series to accurately predict short vs. tall crops. Independent reference data from around the world are then used to evaluate these GEDI-S2 maps. We find that GEDI-S2 performed nearly as well as models trained on thousands of local reference training points, with accuracies of at least 87% and often above 90% throughout the Americas, Europe, and East Asia. A systematic underestimation of tall crop area was observed in regions where crops frequently exhibit low biomass, namely Africa and South Asia, and further work is needed in these systems. Although the GEDI-S2 approach only differentiates tall from short crops, in many landscapes this distinction is sufficient to map individual crop types (e.g., maize vs. soy, sugarcane vs. rice). The combination of GEDI and Sentinel-2 thus presents a very promising path towards global crop mapping with minimal reliance on ground data. [ABSTRACT FROM AUTHOR]

A better understanding of the water and energy cycles at climate scale in the Third Pole Environment is essential for assessing and understanding the causes of changes in the cryosphere and hydrosphere in relation to changes of plateau atmosphere in the Asian monsoon system and for predicting the possible changes in water resources in South and East Asia. This paper reports the following results: (1) A platform of in situ observation stations is briefly described for quantifying the interactions in hydrosphere-pedosphere-atmosphere-cryosphere-biosphere over the Tibetan Plateau. (2) A multiyear in situ L-Band microwave radiometry of land surface processes is used to develop a new microwave radiative transfer modeling system. This new system improves the modeling of brightness temperature in both horizontal and vertical polarization. (3) A multiyear (2001–2018) monthly terrestrial actual evapotranspiration and its spatial distribution on the Tibetan Plateau is generated using the surface energy balance system (SEBS) forced by a combination of meteorological and satellite data. (4) A comparison of four large scale soil moisture products to in situ measurements is presented. (5) The trajectory of water vapor transport in the canyon area of Southeast Tibet in different seasons is analyzed, and (6) the vertical water vapor exchange between the upper troposphere and the lower stratosphere in different seasons is presented. [ABSTRACT FROM AUTHOR]

Dust storm disasters have emerged as a significant environmental challenge in East Asia. However, relying on a single monitoring method to track dust storms presents limitations and can be variable. Therefore, it is necessary to use a combination of ground and remote sensing monitoring methods to explore the source and impact range of dust storms in order to fully characterize them. To achieve this, we examined the sources and impact ranges of dust storms in East Asia from 1980 to 2020 using both ground station data and remote sensing data. In addition, we focused on three specific dust storm events in the region. Our results indicate that the central source areas of dust storms are located in southern Mongolia and the Taklamakan Desert in China. Dust storms are mainly transported and spread in the northwestern region, while they are relatively rare in the southeastern region. The HYSPLIT model simulations reveal that the primary source directions of dust storms in East Asia are northwest, west, and north, the region involved includes Kazakhstan, southern Mongolia, and the Taklimakan Desert in China. The vertical structure of the dust storm layer depends on the source of the dust storm and the intensity of the dust storm event. Dust grain stratification probably occurs due to differences in dust storm sources, grain size, and regularity. These findings demonstrate that a combination of ground-based and remote sensing monitoring methods is an effective approach to fully characterize dust storms and can provide more comprehensive information for dust storm studies. [ABSTRACT FROM AUTHOR]

As the risk posed by climate change becomes increasingly evident, countries across the world are constantly seeking alternative energy sources. Wind energy has substantial potential for future energy portfolios without having negative impacts on the environment. In developing nationwide and worldwide energy plans, understanding the spatio-temporal pattern of wind is crucial. We analyze wind vectors in the region of East Asia from the fifth-generation ECMWF atmospheric reanalysis. To model the wind vectors, we consider Tukey g-and-h transformation-based non-Gaussian processes, along with multivariate covariance functions. The proposed model can address non-Gaussian features and nonstationary dependence structures of wind vectors. In addition, a two-step inference scheme coupled with the composite likelihood method is applied to handle the computational issues posed by a large dataset. In the first step, we fit the temporal dependence structures of data with a location-specific non-Gaussian time series model. This allows us to remove substantial amounts of nonstationary variations in both space and time, and thus, relatively simple covariance models can handle large and complicated data in the second step. We show that the proposed method with a covariance structure reflecting the nonstationarity due to the latitude difference and the land–ocean difference leads to better predictions for wind speed as well as wind potential, which is crucial for planning wind power generation. [ABSTRACT FROM AUTHOR]

One of the important tasks of the Chinese geostationary and meteorological satellite Fengyun-2 (FY2) series is to provide quantitative precipitation estimates (QPE) with high spatiotemporal resolutions for East Asia. To analyze the monitoring capabilities of FY2-based QPEs in extreme rainfall events, this study comprehensively evaluated and compared the performances of FY-2G and FY-2H QPEs for the "7.20" rainstorm in Henan province, China from 17 July 2021 to 22 July 2021. Three continuous metrics and three categorical metrics were adopted to assess the accuracies of FY-2G and FY-2H QPEs, referenced by gauge observations from 116 meteorological stations. The results show that the FY-2G QPE has lower BIAS (−9.64% for FY-2G, −46.22% for FY-2H) and RMSE (5.83 mm/h for FY-2G, 8.4 mm/h for FY-2H) and higher CC (0.57 for FY-2G, 0.24 for FY-2H) than FY-2H QPE in this rainstorm event. Moreover, the FY-2G QPE is not only more consistent with the ground reference with respect to the rainfall amount, but also has higher detecting capability in the "7.20" rainstorm event when compared with the FY-2H QPE. The FY-2G QPE presented a higher capability to correctly capture the precipitation event for the "7.20" rainstorm because of higher POD (probability of detection) and CSI (critical success index) relative to FY-2H QPE, especially in complex topography. From the spatial distribution of precipitation amount, the FY-2G QPE captured the rainstorm center of extreme precipitation more accurately relative to the latest FY-2H product. On the other hand, the previous generation of FY-2G QPE was closer to the continuous rainfall process and precipitation duration with ground observations than the latest FY-2H QPE. Therefore, the precipitation retrieval algorithm of FY-2H QPE still had room to improve. It is necessary to introduce error correction algorithms, especially in complex topography for rainstorm events. [ABSTRACT FROM AUTHOR]

Boreal forest areas in the Russian Far East contained very large intact forests. This particular area is considered one of the most productive and diverse forests in the boreal biome of the world, and it is also home to many endangered species. Zeya State Nature Reserve is located at the southern margin of the boreal forest area in the Russian Far East and has rich fauna and flora. However, the forest in the region faced large-scale forest fires and clearcutting for timber recently. The information of disturbances is rarely understood. This study aimed to explore the effects of disturbance and forest dynamics around the reserve. Our study used two-year overlaid Landsat images from Landsat 5 Thematic Mapper (TM) and 8 Operational Land Imager (OLI), to generate forest-cover-change maps of 1988–1999, 1999–2010, and 2010–2016. In this paper, we analyze the direction of forest successional stages, to demonstrate the effectiveness of this protected area in terms of preventing human-based deforestation on the vegetation indices. The vegetation indices included the normalized burn ratio (NBR), the normalized difference vegetation index (NDVI), and the normalized difference water index (NDWI). The study provided information on the pattern of forest-cover change and disturbance area around the reserve. The NDWI was used to differentiate between water and non-water areas. The mean values of NBR and NDVI were calculated and determine the forest successional stages between burn, vegetation recovery, grass, mixed forest, oak forest, and birch and larch forest. The accuracy was assessed by using field measurements, field photos, and high-resolution images as references. Overall, our classification results have high accuracy for all three periods. The most disturbed area occurred during 2010–2016. The reserve was highly protected, with no human-disturbance activity. However, large areas from fire disturbance were found (137 km2) during 1999–2010. The findings also show a large area of disturbance, mostly located outside of the reserve. Mixed disturbance increased to almost 50 km2 during 2010–2016, in the buffer zone and outside of the reserve. We recommend future works to apply our methods to other ecosystems, to compare the forest dynamics and disturbance inside and outside the protected area. [ABSTRACT FROM AUTHOR]

The incident and emitted radiative energy data for the top of the atmosphere (TOA) are essential in climate research. Since East Asia (11–61°N, 80–175°E) is complexly composed of land and ocean, real-time satellite data are used importantly for analyzing the detailed energy budget or climate characteristics of this region. Therefore, in this study, the radiative energy budget for East Asia, during the year 2021, was analyzed using GEO-KOMPSAT-2A/Advanced Metrological Imager (GK-2A/AMI) and the European Centre for Medium-range Weather Forecasts reanalysis (ERA5) data. The results showed that the net fluxes for the TOA and surface were −4.09 W·m−2 and −8.24 W·m−2, respectively. Thus, the net flux difference of 4.15 W·m−2 between TOA and surface implied atmospheric warming. These results, produced by GK-2A/AMI, were well-matched with the ERA5 data. However, they varied with surface characteristics; the atmosphere over ocean areas warmed because of the large amounts of longwave radiation emitted from surfaces, while the atmosphere over the plain area was relatively balanced and the atmosphere over the mountain area was cooled because large amount of longwave radiation was emitted to space. Although the GK2A/AMI radiative products used for this study have not yet been sufficiently compared with surface observation data, and the period of data used was only one year, they were highly correlated with the CERES (Clouds and the Earth's Radiant Energy System of USA), HIMAWARI/AHI (Geostationary Satellite of Japan), and ERA5 data. Therefore, if more GK-2A/AMI data are accumulated and analyzed, it could be used for the analysis of radiant energy budget and climate research for East Asia, and it will be an opportunity to greatly increase the utilization of total meteorological products of 52 types, including radiative products. [ABSTRACT FROM AUTHOR]

The climate effect and environmental pollution caused by dust discharged into the atmosphere have attracted much attention. However, the driving factors of dust emissions have not been studied thoroughly. Here, spatiotemporal variations in dust emissions and the relationship between dust emissions and large-scale atmospheric circulation in East Asia from 2000 to 2021 were investigated using Modern-Era Retrospective Analysis for Research and Applications version 2, Cloud-Aerosol Lidar Pathfinder Satellite Observations, ERA5 reanalysis data, and climate indices. Results showed that the Taklimakan Desert in the Tarim Basin, the Gurbantonggut Desert in the Junggar Basin, the Turpan Basin, and the Gobi Desert in western Inner Mongolia and southern Mongolia are the main sources of dust emissions in East Asia. The period of strong dust emissions is from March to May, and emissions to the atmosphere were mainly distributed at 0–4 km in the troposphere. In the eastern and southwestern Tarim Basin, northern Junggar Basin, and parts of the Gobi Desert in southern Mongolia, dust emissions have significantly increased over the past 22 years, whereas in the southwestern Tibetan Plateau, southwestern Inner Mongolia, and a small part of the northern Mongolian Gobi Desert there was a significant decreasing trend. The winter North Atlantic Oscillation (NAO) and Arctic Oscillation (AO) were significantly negatively correlated with East Asian dust emissions the following spring. The various phases of the AO/NAO coupling have clear different effects on East Asian dust emissions in the spring. When the AO/NAO coupling was negative (positive), the East Asian trough and Siberian High were strengthened (weakened), the frequency of cold air activity increased (weakened), 800 hPa wind speed strengthened (weakened), and East Asian emissions increased (decreased). In AO−/NAO+ years, the Asian polar vortex was stronger to the south and the East Asian trough was stronger to the west. The Lake Baikal trough was in the deepening phase, which caused more polar cold air to move into East Asia, aggravating the intensity of dust activity. In the AO+/NAO− years, the Siberian High and East Asian trough weakened, which was unfavorable to the southward movement of cold air from Siberia. Therefore, the frequency of windy weather in East Asia decreased, partly weakening dust emissions. However, a positive geopotential anomaly in northeast China and a negative geopotential anomaly in South Asia triggered an anomalous enhancement in easterly wind in the tropospheric area over northwest China. Strengthening of the Balkhash trough provides favorable conditions for gale weather in northwest China. The frequency of gale weather increased, and dust emissions were enhanced in northwest China. [ABSTRACT FROM AUTHOR]

Vegetation growth in drylands is highly constrained by water availability. How dryland vegetation responds to changes in precipitation and soil moisture in the context of a warming climate is not well understood. In this study, warm drylands in the temperate zone between 30 and 50° N, including North America (NA), the Mediterranean region (MD), Central Asia (CA), and East Asia (EA), were selected as the study area. After verifying the trends and anomalies of three kinds of leaf area index (LAI) datasets (GLASS LAI, GLEAM LAI, and GLOBAMAP LAI) in the study area, we mainly used the climate (GPCC precipitation and ERA5 temperature), GLEAM soil moisture, and GLASS LAI datasets from 1981 to 2018 to analyze the response of vegetation growth to changes in precipitation and soil moisture. The results of the three mutually validated LAI datasets show an overall greening of dryland vegetation with the same increasing trend of 0.002 per year in LAI over the past 38 years. LAI and precipitation exhibited a strong correlation in the eastern part of the NA drylands and the northeastern part of the EA drylands. LAI and soil moisture exhibited a strong correlation in the eastern part of the NA drylands, the eastern part of the MD drylands, the southern part of the CA drylands, and the northeastern part of the EA drylands. The results of this study will contribute to the understanding of vegetation dynamics and their response to changing water conditions in the Northern Hemisphere midlatitude drylands. [ABSTRACT FROM AUTHOR]

In the context of global warming, vegetation activity in northeastern East Asia (40–45°N, 105–130°E) (NEA) shows a significant growth trend on a multidecadal scale, but how vegetation changes on a decadal scale is unclear. In this study, we find a significant trend of vegetation greening in northeastern East Asia during 1982–1998 and a slowdown in the greening trend during 1998–2014. Trend analysis of the extreme climate indices reveals that the trends of precipitation-related extreme climate indices are similar to those of vegetation change, and further correlation analysis reveals that precipitation-related extreme climate indices have a strong positive correlation with the NDVI. The results indicate that the vegetation in northeastern East Asia is more sensitive to precipitation changes, especially extreme precipitation, compared with the temperature and related extreme indices. Furthermore, the analysis of large-scale atmospheric circulation changes suggests a role of Northwest Pacific subtropical high (NPSH) in the trend changes of precipitation-related extreme indices. The strengthening of NPSH before 1998 enhances the moisture transport to the NEA, providing abundant water vapor favorable for extreme precipitation events, while after 1998, the NPSH trend is much weakened, corresponding to a decrease in the moisture transport trend. [ABSTRACT FROM AUTHOR]

Dust aerosols have significant impact on the environment and climate through long-range transport. We report, in this paper, a case of dust–cloud interaction process using combined measurements of a ground-based polarization and Raman (PR) lidar systems, and implemented by the spaceborne Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) lidar observations. The dust event occurred on 14 March 2009 over East Asia. During the two hours of observing time, the ground-based lidar observed an ongoing process of decreasing of the depolarization ratio (DR) accompanied by the increase of the water vapor simultaneously, indicating a dust–cloud interaction and particle transformation. CALIPSO measurements also found similar layers of dusts and clouds over lands and oceans with properties similar to the ground based lidar measurements. Our observation was a real-time dust–cloud process with the observation of occurrence of particle transformation. The depolarization reduced from 0.2 to 0.1 corresponding to a change of aspect ratio from 1.2 to 1.1. A discussion of a dust–cloud interaction in terms of three-stage cloud processes is made based on back-trajectory analyses and lidar observations. The result shows that dust aerosols decrease the cloud extinction coefficient by 41% but increase the cloud optical depth (COD) of water cloud by 12.79%, compared with that of pure water clouds. Furthermore, if dust aerosols participate as cloud condensation nuclei (CCN) in cloud physical processes, then they significantly reduce the size of the cloud droplet by 44–79%. Finally, based on three-year collocated CALIPSO and CloudSat measurements from 2007 to 2010, we found approximately one-third of clouds are originally dusty in the spring over the Pacific Coast areas. [ABSTRACT FROM AUTHOR]

Central Asia is one of the most important sources of mineral saline dust worldwide. A comprehensive understanding of Central Asian dust transport is essential for evaluating its impacts on human health, ecological safety, weather and climate. This study first puts forward an observation-based climatology of Central Asian dust transport flux by using the 3-D dust detection of Cloud-Aerosol LiDAR with Orthogonal Polarization (CALIOP). The seasonal difference of transport flux and downstream contribution are evaluated and compared with those of the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). Central Asian dust can be transported not only southward in summer under the effect of the South Asian summer monsoon, but also eastward in other seasons under the control of the westerly jet. Additionally, the transport of Central Asian dust across the Pamir Plateau to the Tibetan Plateau is also non-negligible, especially during spring (with a transport flux rate of 150 kg m−1 day−1). The annual CALIOP-based downstream contribution of Central Asian dust to South Asian (164.01 Tg) is 2.1 times that to East Asia (78.36 Tg). This can be attributed to the blocking effect of the higher terrain between Central and East Asia. Additionally, the downstream contributions to South and East Asia from MERRA-2 are only 0.36 and 0.84 times that of CALIOP, respectively. This difference implies the overestimation of the wet and dry depositions of the model, especially in the low latitude zone. The quantification of the Central Asian dust transport allows a better understanding of the Central Asian dust cycle, and supports the calibration/validation of aerosol-related modules of regional and global climate models. [ABSTRACT FROM AUTHOR]

Absorbing aerosols can strongly absorb solar radiation and have a profound impact on the global and regional climate. Black carbon (BC), organic carbon (OC) and dust are three major types of absorbing aerosols. In order to deepen the overall understanding of absorbing aerosols over East Asia and provide a basis for further investigation of its role in enhanced warming in drylands, the spatial-temporal distribution of absorbing aerosols over East Asia for the period of 2005–2016 was investigated based on the Ozone Monitoring Instrument (OMI) satellite retrievals. Overall, high values of Aerosol Absorption Optical Depth (AAOD) mainly distribute near dust sources as well as BC and OC sources. AAOD reaches its maximum during spring over East Asia as a result of dust activity and biomass burning. Single-scattering albedo (SSA) is comparatively high (>0.96) in the most part of East Asia in the summer, indicating the dominance of aerosol scattering. Hyper-arid regions have the highest Aerosol Optical Depth (AOD) and AAOD among the five climatic regions, with springtime values up to 0.72 and 0.04, respectively. Humid and sub-humid regions have relatively high AOD and AAOD during the spring and winter and the highest SSA during the summer. AAOD in some areas shows significant upward trends, which is likely due to the increase of BC and OC emission. SSA shows overall downward trends, indicating the enhancement of the aerosol absorption. Analysis of emission inventory and dust index data shows that BC and OC emissions mainly come from the humid regions, while dust sources mainly distribute in drylands. [ABSTRACT FROM AUTHOR]

Surface urban heat island (SUHI) impacts control the exchange of sensible heat and latent heat between land and atmosphere and can worsen extreme climate events, such as heat waves. This study assessed SUHIs over three megacities (Seoul, Tokyo, Beijing) in East Asia using one-year (April 2011-March 2012) land surface temperature (LST) data retrieved from the Communication, Ocean and Meteorological Satellite (COMS). The spatio-temporal variations of SUHI and the relationship between SUHI and vegetation activity were analyzed using hourly cloud-free LST data. In general, the LST was higher in low latitudes, low altitudes, urban areas and dry regions compared to high latitudes, high altitudes, rural areas and vegetated areas. In particular, the LST over the three megacities was always higher than that in the surrounding rural areas. The SUHI showed a maximum intensity (10-13 °C) at noon during the summer, irrespective of the geographic location of the city, but weak intensities (4-7 °C) were observed during other times and seasons. In general, the SUHI intensity over the three megacities showed strong seasonal (diurnal) variations during the daytime (summer) and weak seasonal (diurnal) variations during the nighttime (other seasons). As a result, the temporal variation pattern of SUHIs was quite different from that of urban heat islands, and the SUHIs showed a distinct maximum at noon of the summer months and weak intensities during the nighttime of all seasons. The patterns of seasonal and diurnal variations of the SUHIs were clearly dependent on the geographic environment of cities. In addition, the intensity of SUHIs showed a strong negative relationship with vegetation activity during the daytime, but no such relationship was observed during the nighttime. This suggests that the SUHI intensity is mainly controlled by differences in evapotranspiration (or the Bowen ratio) between urban and rural areas during the daytime. [ABSTRACT FROM AUTHOR]

Aerosol particles originated from anthropogenic emissions, volcanic eruptions, biomass burning, and fossil combustion emissions, and their radiative effect is one of the most uncertain factors in climate change. Meanwhile, aerosol particles in fine particle size could also cause irreversible effects on the human respiratory system. This study attempted to analyse the spatial and temporal variations of global aerosol optical depth (AOD, 550 nm) during 1980–2018 using MERRA-2 aerosol reanalysis products and to investigate the effects of natural/anthropogenic emissions of different types of aerosols on AOD values. The results show that the global annual mean AOD values kept high levels with significant fluctuations during 1980–1995 and showed a consistent decreasing and less volatile trend after 1995. Spatially, the AOD values are relatively higher in the Northern Hemisphere than in the Southern Hemisphere, especially in North Africa (0.329), Northern India (0.235), and Eastern China (0.347), because of the intensive natural/anthropogenic aerosol emissions there. The sulphate-based aerosols emitted by biomass burning and anthropogenic emissions are the main types of aerosols worldwide, especially in densely populated and industrialized regions such as East Asia and Europe. Dust aerosols are also the main aerosol type in desert areas. For example, the AOD and AODP values for the Sahara Desert are 0.3178 and 75.32%, respectively. Both black carbon aerosols (BC) and organic carbon aerosols (OC) are primary or secondary from carbon emissions of fossil fuels, biomass burning, and open burning. Thus, the regions with high BC and OC aerosol loadings are mainly located in densely populated or vegetated areas such as East Asia, South Asia, and Central Africa. Sea salt aerosols are mainly found in coastline areas along the warm current pathway. This study could help relevant researchers in the fields of atmospheric science, environmental protection, air pollution, and ecological environment to understand the global spatial–temporal variations and main driving factors of aerosol loadings. [ABSTRACT FROM AUTHOR]

This study presents a global scale analysis of cropping intensity, crop duration and fallow land extent computed by using the global dataset on monthly irrigated and rainfed crop areas MIRCA2000. MIRCA2000 was mainly derived from census data and crop calendars from literature. Global cropland extent was 16 million km2 around the year 2000 of which 4.4 million km2 (28%) was fallow, resulting in an average cropping intensity of 0.82 for total cropland extent and of 1.13 when excluding fallow land. The lowest cropping intensities related to total cropland extent were found for Southern Africa (0.45), Central America (0.49) and Middle Africa (0.54), while highest cropping intensities were computed for Eastern Asia (1.04) and Southern Asia (1.0). In remote or arid regions where shifting cultivation is practiced, fallow periods last 3-10 years or even longer. In contrast, crops are harvested two or more times per year in highly populated, often irrigated tropical or subtropical lowlands where multi-cropping systems are common. This indicates that intensification of agricultural land use is a strategy that may be able to significantly improve global food security. There exist large uncertainties regarding extent of cropland, harvested crop area and therefore cropping intensity at larger scales. Satellite imagery and remote sensing techniques provide opportunities for decreasing these uncertainties and to improve the MIRCA2000 inventory. [ABSTRACT FROM AUTHOR]

Since vegetation is closely related to a variety of hydrological factors, the vegetation condition during a drought is greatly affected by moisture supply or moisture demand from the atmosphere. However, since feedback between vegetation and climate in the event of drought is very complex, it is necessary to construct a joint probability distribution that can describe and investigate the interrelationships between them. In other words, it is required to understand the interaction between vegetation and climate in terms of joint probability. In this study, the possibility of drought stress experienced by vegetation under various conditions occurring during drought was investigated by dividing drought into two aspects (atmospheric moisture supply and moisture demand). Meteorological drought indices that explain different aspects of drought and vegetation-related drought indexes that describe the state of vegetation were estimated using data remotely sensed by satellites in parts of Far East Asia centered on South Korea. Bivariate joint probability distribution modeling was performed from vegetation drought index and meteorological drought index using Copula. It was found that the relationship between the vegetation drought index and the meteorological drought index has regional characteristics and there is also a seasonal change. From the copula-based model, it was possible to quantify the conditional probability distribution for the drought stress of vegetation under meteorological drought scenarios that occur from different causes. Through this, by mapping the vulnerability of vegetation to meteorological drought in the study area, it was possible to spatially check how the vegetation responds differently depending on the season and meteorological causes. The probabilistic mapping of vegetation vulnerability to various aspects of meteorological drought may provide useful information for establishing mitigation strategies for ecological drought. [ABSTRACT FROM AUTHOR]

The Visible Infrared Imaging Radiometer Suite (VIIRS) fire detection algorithm mostly relies on thermal infrared channels that possess fixed or context-sensitive thresholds. The main channel used for fire identification is the mid-infrared channel, which has relatively low temperature saturation. Therefore, when the high temperature of a fire in this channel is used for initial screening, the threshold is relatively high. Although screening results are tested at different levels, few small fires will be lost under these strict test conditions. However, crop burning fires often occur in East Asia at a small scale and relatively low temperature, such that their radiative characteristics cannot meet the global threshold. Here, we propose a new weighted fire test algorithm to accurately detect small-scale fires based on differences in the sensitivity of test conditions to fire. This method reduces the problem of small fires being ignored because they do not meet some test conditions. Moreover, the adaptive threshold suitable for small fires is selected by bubble sorting according to the radiation characteristics of small fires. Our results indicate that the improved algorithm is more sensitive to small fires, with accuracies of 53.85% in summer and 73.53% in winter, representing an 18.69% increase in accuracy and a 28.91% decline in error rate. [ABSTRACT FROM AUTHOR]

The lack of accurate estimation of intense precipitation is a universal limitation in precipitation retrieval. Therefore, a new rainfall retrieval technique based on the Random Forest (RF) algorithm is presented using the Advanced Himawari Imager-8 (Himawari-8/AHI) infrared spectrum data and the NCEP operational Global Forecast System (GFS) forecast information. And the gauge-calibrated rainfall estimates from the Global Precipitation Measurement (GPM) product served as the ground truth to train the model. The two-step RF classification model was established for (1) rain area delineation and (2) precipitation grades' estimation to improve the accuracy of moderate rain and heavy rain. In view of the imbalance categories' distribution in the datasets, the resampling technique including the Random Under-sampling algorithm and Synthetic Minority Over-sampling Technique (SMOTE) was implemented throughout the whole training process to fully learn the characteristics among the samples. Among the features used, the contributions of meteorological variables to the trained models were generally greater than those of infrared information; in particular, the contribution of precipitable water was the largest, indicating the sufficient necessity of water vapor conditions in rainfall forecasting. The simulation results by the RF model were compared with the GPM product pixel-by-pixel. To prove the universality of the model, we used independent validation sets which are not used for training and two independent testing sets with different periods from the training set. In addition, the algorithm was validated against independent rain gauge data and compared with GFS model rainfall. Consequently, the RF model identified rainfall areas with a Probability Of Detection (POD) of around 0.77 and a False-Alarm Ratio (FAR) of around 0.23 for validation, as well as a POD of 0.60–0.70 and a FAR of around 0.30 for testing. To estimate precipitation grades, the value of classification was 0.70 in validation and in testing the accuracy was 0.60 despite a certain overestimation. In summary, the performance on the validation and test data indicated the great adaptability and superiority of the RF algorithm in rainfall retrieval in East Asia. To a certain extent, our study provides a meaningful range division and powerful guidance for quantitative precipitation estimation. [ABSTRACT FROM AUTHOR]

Space-borne ozone (O3) measurements have indicated consistent positive trends across the entire Asia–Pacific region despite the considerable reduction of NOx since 2000s. The rate of increase in O3 derived from lower free tropospheric column measurements was observed to be 0.21 ± 0.05 DU/decade during 2005–2018. Our space-borne-based diagnosis of the nonlinear photochemical formation regimes, NOx-limited and NOx-saturated, show that O3 chemistry is undergoing a transitional process to the NOx-limited regime throughout most of the Asian region. Nevertheless, NOx-saturated conditions persist at present in and over eight major megacities. These NOx-saturated conditions in megacities contribute to the increased O3 due to NOx reduction, which could also affect the enhanced O3 concentrations throughout the Asia–Pacific region via long-range transport. This indicates that VOC limits along with NOx reductions are needed in megacities in the short term to reduce O3 levels. Moreover, NOx saturation in major megacities will continue until 2025, according to the forecast emission scenarios from the Intergovernmental Panel on Climate Change (IPCC). These scenarios gradually shift nearly all cities to the NOx-limited regime by 2050 with the exception of few cities under IPCC RCP8.5. Thus, continued reductions in NOx will be a key factor in reducing O3 in the long term. [ABSTRACT FROM AUTHOR]

Light pollution, a phenomenon in which artificial nighttime light (NTL) changes the form of brightness and darkness in natural areas such as protected areas (PAs), has become a global concern due to its threat to global biodiversity. With ongoing global urbanization and climate change, the light pollution status in global PAs deserves attention for mitigation and adaptation. In this study, we developed a framework to evaluate the light pollution status in global PAs, using the global NTL time series data. First, we classified global PAs (30,624) into three pollution categories: non-polluted (5974), continuously polluted (8141), and discontinuously polluted (16,509), according to the time of occurrence of lit pixels in/around PAs from 1992 to 2018. Then, we explored the NTL intensity (e.g., digital numbers) and its trend in those polluted PAs and identified those hotspots of PAs at the global scale with consideration of global urbanization. Our study shows that global light pollution is mainly distributed within the range of 30°N and 60°N, including Europe, north America, and East Asia. Although the temporal trend of NTL intensity in global PAs is increasing, Japan and the United States of America (USA) have opposite trends due to the implementation of well-planned ecological conservation policies and declining population growth. For most polluted PAs, the lit pixels are close to their boundaries (i.e., less than 10 km), and the NTL in/around these lit areas has become stronger over the past decades. The identified hotspots of PAs (e.g., Europe, the USA, and East Asia) help support decisions on global biodiversity conservation, particularly with global urbanization and climate change. [ABSTRACT FROM AUTHOR]

Global Positioning System (GPS) radio occultation (RO) and radiosonde (RS) observations are two major types of observations assimilated in numerical weather prediction (NWP) systems. Observation error variances are required input that determines the weightings given to observations in data assimilation. This study estimates the error variances of global GPS RO refractivity and bending angle and RS temperature and humidity observations at 521 selected RS stations using the three-cornered hat method with additional ERA-Interim reanalysis and Global Forecast System forecast data available from 1 January 2016 to 31 August 2019. The global distributions, of both RO and RS observation error variances, are analyzed in terms of vertical and latitudinal variations. Error variances of RO refractivity and bending angle and RS specific humidity in the lower troposphere, such as at 850 hPa (3.5 km impact height for the bending angle), all increase with decreasing latitude. The error variances of RO refractivity and bending angle and RS specific humidity can reach about 30 N-unit2, 3 × 10−6 rad2, and 2 (g kg−1)2, respectively. There is also a good symmetry of the error variances of both RO refractivity and bending angle with respect to the equator between the Northern and Southern Hemispheres at all vertical levels. In this study, we provide the mean error variances of refractivity and bending angle in every 5°-latitude band between the equator and 60°N, as well as every interval of 10 hPa pressure or 0.2 km impact height. The RS temperature error variance distribution differs from those of refractivity, bending angle, and humidity, which, at low latitudes, are smaller (less than 1 K2) than those in the midlatitudes (more than 3 K2). In the midlatitudes, the RS temperature error variances in North America are larger than those in East Asia and Europe, which may arise from different radiosonde types among the above three regions. [ABSTRACT FROM AUTHOR]

Aerosol optical depth (AOD) is a key parameter that reflects the characteristics of aerosols, and is of great help in predicting the concentration of pollutants in the atmosphere. At present, remote sensing inversion has become an important method for obtaining the AOD on a large scale. However, AOD data acquired by satellites are often missing, and this has gradually become a popular topic. In recent years, a large number of AOD recovery algorithms have been proposed. Many AOD recovery methods are not application-oriented. These methods focus mainly on to the accuracy of AOD recovery and neglect the AOD recovery ratio. As a result, the AOD recovery accuracy and recovery ratio cannot be balanced. To solve these problems, a two-step model (TWS) that combines multisource AOD data and AOD spatiotemporal relationships is proposed. We used the light gradient boosting (LightGBM) model under the framework of the gradient boosting machine (GBM) to fit the multisource AOD data to fill in the missing AOD between data sources. Spatial interpolation and spatiotemporal interpolation methods are limited by buffer factors. We recovered the missing AOD in a moving window. We used TWS to recover AOD from Terra Satellite's 2018 AOD product (MOD AOD). The results show that the MOD AOD, after a 3 × 3 moving window TWS recovery, was closely related to the AOD of the Aerosol Robotic Network (AERONET) (R = 0.87, RMSE = 0.23). In addition, the MOD AOD missing rate after a 3 × 3 window TWS recovery was greatly reduced (from 0.88 to 0.1). In addition, the spatial distribution characteristics of the monthly and annual averages of the recovered MOD AOD were consistent with the original MOD AOD. The results show that TWS is reliable. This study provides a new method for the restoration of MOD AOD, and is of great significance for studying the spatial distribution of atmospheric pollutants. [ABSTRACT FROM AUTHOR]

Drought forecasting is essential for effectively managing drought-related damage and providing relevant drought information to decision-makers so they can make appropriate decisions in response to drought. Although there have been great efforts in drought-forecasting research, drought forecasting on a short-term scale (up to two weeks) is still difficult. In this research, drought-forecasting models on a short-term scale (8 days) were developed considering the temporal patterns of satellite-based drought indices and numerical model outputs through the synergistic use of convolutional long short term memory (ConvLSTM) and random forest (RF) approaches over a part of East Asia. Two widely used drought indices—Scaled Drought Condition Index (SDCI) and Standardized Precipitation Index (SPI)—were used as target variables. Through the combination of temporal patterns and the upcoming weather conditions (numerical model outputs), the overall performances of drought-forecasting models (ConvLSTM and RF combined) produced competitive results in terms of r (0.90 and 0.93 for validation SDCI and SPI, respectively) and nRMSE (0.11 and 0.08 for validation of SDCI and SPI, respectively). Furthermore, our short-term drought-forecasting model can be effective regardless of drought intensification or alleviation. The proposed drought-forecasting model can be operationally used, providing useful information on upcoming drought conditions with high resolution (0.05°). [ABSTRACT FROM AUTHOR]

Radio-frequency-interference (RFI) signals have gradually become a more serious problem in active and passive microwave remote sensing. However, currently, there is no reliable RFI source distribution data to evaluate the accuracy of existing RFI identification methods. In this study, a simplified generalized RFI detection method (GRDM) is proposed to detect RFI applied to the ocean surface. Two RFI detection methods, the GRDM and the double-principal component analysis (DPCA) method, are used for cross-validation to obtain RFI recognition thresholds of DPCA in the Advanced Microwave Scanning Radiometer 2 (AMSR2) ocean data. In addition, in the present work the source and distribution characteristics of RFI over the ocean surface are also analyzed. The results show that the proposed scheme can effectively identify RFI signals from AMSR2 data, and only 7.3, 10.65, and 18.7 GHz channels are contaminated by RFI over the ocean surface. There are strong 7.3 GHz interference signals over the waters of East Asia (with the value of ΔTBH mostly between 5 and 30 K and ΔTBv mostly between 5 and 40 K), Europe (with the value of ΔTBH mostly between 5 and 40 K and ΔTBv mostly between 5 and 30 K), and North America (with the value of ΔTBH mostly between 5 and 50 K and ΔTBv mostly between 5 and 30 K). The RFI signals in 10.65 GHz data are mainly distributed over the Mediterranean and other European waters (with the value of ΔTBH mostly between 5 and 35 K and ΔTBv mostly between 5 and 20 K). The RFI signals at 18.7 GHz are mainly present over the offshore marine areas of North America (with the value of ΔTBH mostly between 5 and 50 K and ΔTBv mostly between 5 and 40 K), with the strongest RFI distributed near the Great Lakes of America, and the RFI magnitudes over the east and west coasts are stronger than over the south coast. Satellite-borne microwave observations over the ocean suffer from interference mainly from stationary communication/television satellites. Due to the reflection of the sea surface, the range and intensity of RFI are strongly dependent on the relative geometric positions of stationary satellites and space-borne passive instruments. Therefore, RFI coverage area changes every day over the ocean in one 16-day period, which is very different from that over the land. [ABSTRACT FROM AUTHOR]

Radiative transfer model can be used to convert the geophysical variables (e.g., atmospheric thermodynamic state) to the radiation field. In this study, the Community Radiative Transfer Model (CRTM) is used to connect regional Weather Research and Forecasting (WRF) model outputs and satellite observations. A heavy rainfall event caused by the Mei-Yu front on the June 1, 2017, in the vicinity of Taiwan, was chosen as a case study. The simulated cloud performance of WRF with four microphysics schemes (i.e., Goddard (GCE), WRF single-moment 6 class (WSM), WRF double-moment 6 class (WDM), and Morrison (MOR) schemes) was investigated objectively using multichannel observed satellite radiances from a Japanese geostationary satellite Himawari-8. The results over the East Asia domain (9 km) illustrate that all four microphysics schemes overestimate cloudy pixels, in particular, the high cloud of simulation with MOR when comparing with satellite data. Sensitivity tests reveal that the excess condensation of ice at ≥14 km with MOR might be associated with the overestimated high cloud cover. However, GCE displayed an improved performance on water vapor channel in clear skies. When focusing on Taiwan using a higher (3 km) model resolution, each scheme displayed a decent performance on cloudy pixels. In the grid-by-grid skill score analysis, the distribution of high clouds was the most accurate among the three cloud types. The results also suggested that all schemes required a longer simulation time to describe the low cloud horizontal extend. [ABSTRACT FROM AUTHOR]

Shortly after the outbreak of the novel infectious disease (COVID-19) started at the end of 2019, it turned into a global pandemic, which caused the lockdown of many countries across the world. Various strict measures were adopted to reduce anthropogenic activities in order to prevent further spread and infection of the disease. In this study, we utilized continental scale remotely sensed data along with city scale in situ air quality observations for 2020 as well as data from the baseline period (2015–2019) to provide an early insight on air pollution changes in response to the COVID-19 pandemic lockdown, by combining both continental and city scales. For the continental scale analysis, data of NO2, SO2, and O3 were acquired from the ozone monitoring instrument (OMI) and data of aerosol optical depth (AOD) were collected from the moderate resolution imaging spectroradiometer (MODIS). For city scale analysis, data of NO2, CO, PM2.5, O3, and SO2 were derived from ground-based air quality observations. Results from satellite observations at the continental scale showed that concentrations of NO2, SO2, and AOD substantially dropped in 2020 during the lockdown period compared to their averages for the baseline period over all continents, with a maximum reduction of ~33% for NO2 in East Asia, ~41% for SO2 in East Asia, and ~37% for AOD in South Asia. In the case of O3, the maximum overall reduction was observed as ~11% in Europe, followed by ~10% in North America, while a slight increase was found in other study regions. These findings align with ground-based air quality observations, which showed that pollutants such as NO2, CO, PM2.5, and SO2 during the 2020 lockdown period decreased significantly except that O3 had varying patterns in different cities. Specifically, a maximum reduction of ~49% in NO2 was found in London, ~43% in CO in Wuhan, ~38% in PM2.5 in Chennai, and ~48% in SO2 in Beijing. In the case of urban O3, a maximum reduction of ~43% was found in Wuhan, but a significant increase of ~47% was observed in Chennai. It is obvious that restricted human activities during the lockdown have reduced the anthropogenic emissions and subsequently improved air quality, especially across the metropolitan cities. [ABSTRACT FROM AUTHOR]

In this study, the spatio-temporal variability of aerosol optical depth (AOD), total column ozone (TCO), and total column NO2 (TCN) was identified over East Asia using long-term datasets from ground-based and satellite observations. Based on the statistical results, optimized spatio-temporal ranges for the validation study were determined with respect to the target materials. To determine both spatial and temporal ranges for the validation study, we confirmed that the observed datasets can be statistically considered as the same quantity within the ranges. Based on the thresholds of R2>0.95 (temporal) and R>0.95 (spatial), the basic ranges for spatial and temporal scales for AOD validation was within 30 km and 30 min, respectively. Furthermore, the spatial scales for AOD validation showed seasonal variation, which expanded the range to 40 km in summer and autumn. Because of the seasonal change of latitudinal gradient of the TCO, the seasonal variation of the north-south range is a considerable point. For the TCO validation, the north-south range is varied from 0.87° in spring to 1.05° in summer. The spatio-temporal range for TCN validation was 20 min (temporal) and 20–50 km (spatial). However, the nearest value of satellite data was used in the validation because the spatio-temporal variation of TCN is large in summer and autumn. Estimation of the spatio-temporal variability for respective pollutants may contribute to improving the validation of satellite products. [ABSTRACT FROM AUTHOR]

Crop yield modeling at the regional level is one of the most important methods to ensure the profitability of the agro-industrial economy and the solving of the food security problem. Due to a lack of information about crop distribution over large agricultural areas, as well as the crop separation problem (based on remote sensing data) caused by the similarity of phenological cycles, a question arises regarding the relevance of using data obtained from the arable land mask of the region to predict the yield of individual crops. This study aimed to develop a regression model for soybean crop yield monitoring in municipalities and was conducted in the Khabarovsk Territory, located in the Russian Far East. Moderate Resolution Imaging Spectroradiometer (MODIS) data, an arable land mask, the meteorological characteristics obtained using the VEGA-Science web service, and crop yield data for 2010–2019 were used. The structure of crop distribution in the Khabarovsk District was reproduced in experimental fields, and Normalized Difference Vegetation Index (NDVI) seasonal variation approximating functions were constructed (both for total district sown area and different crops). It was found that the approximating function graph for the experimental fields corresponds to a similar graph for arable land. The maximum NDVI forecast error on the 30th week in 2019 using the approximation parameters according to 2014–2018 did not exceed 0.5%. The root-mean-square error (RMSE) was 0.054. The maximum value of the NDVI, as well as the indicators characterizing the temperature regime, soil moisture, and photosynthetically active radiation in the region during the period from the 1st to the 30th calendar weeks of the year, were previously considered as parameters of the regression model for predicting soybean yield. As a result of the experiments, the NDVI and the duration of the growing season were included in the regression model as independent variables. According to 2010–2018, the mean absolute percentage error (MAPE) of the regression model was 6.2%, and the soybean yield prediction absolute percentage error (APE) for 2019 was 6.3%, while RMSE was 0.13 t/ha. This approach was evaluated with a leave-one-year-out cross-validation procedure. When the calculated maximum NDVI value was used in the regression equation for early forecasting, MAPE in the 28th–30th weeks was less than 10%. [ABSTRACT FROM AUTHOR]

Recently, surface diffuse solar radiation (Rdif) has been attracting a growing interest in view of its function in improving plant productivity, thus promoting global carbon uptake, and its impacts on solar energy utilization. To date, very few radiation products provide estimates of Rdif, and systematic validation and evaluation are even more scare. In this study, Rdif estimates from Reanalysis Fifth Generation (ERA5) of European Center for Medium-Range Weather Forecasts and satellite-based retrieval (called JiEA) are evaluated over East Asia using ground measurements at 39 stations from World Radiation Data Center (WRDC) and China Meteorological Administration (CMA). The results show that JiEA agrees well with measurements, while ERA5 underestimates Rdif significantly. Both datasets perform better at monthly mean scale than at daily mean and hourly scale. The mean bias error and root-mean-square error of daily mean estimates are −1.21 W/m2 and 20.06 W/m2 for JiEA and −17.18 W/m2 and 32.42 W/m2 for ERA5, respectively. Regardless of over- or underestimation, correlations of estimated time series of ERA5 and JiEA show high similarity. JiEA reveals a slight decreasing trend at regional scale, but ERA5 shows no significant trend, and neither of them reproduces temporal variability of ground measurements. Data accuracy of ERA5 is more robust than JiEA in time but less in space. Latitudinal dependency is noted for ERA5 while not for JiEA. In addition, spatial distributions of Rdif from ERA5 and JiEA show pronounced discrepancy. Neglect of adjacency effects caused by horizontal photon transport is the main cause for Rdif underestimation of ERA5. Spatial analysis calls for improvements to the representation of clouds, aerosols and water vapor for reproducing fine spatial distribution and seasonal variations of Rdif. [ABSTRACT FROM AUTHOR]

Drought is the meteorological phenomenon with the greatest impact on agriculture. Accordingly, drought forecasting is vital in lessening its associated negative impacts. Utilizing remote exploration in the agricultural sector allows for the collection of large amounts of quantitative data across a wide range of areas. In this study, we confirmed the applicability of drought assessment using the evaporative stress index (ESI) in major East Asian countries. The ESI is an indicator of agricultural drought that describes anomalies in actual/reference evapotranspiration (ET) ratios that are retrieved using remotely sensed inputs of land surface temperature (LST) and leaf area index (LAI). The ESI is available through SERVIR Global, a joint venture between the National Aeronautics and Space Administration (NASA) and the United States Agency for International Development (USAID). This study evaluated the performance of ESI in assessing drought events in South Korea. The evaluation of ESI is possible because of the availability of good statistical data. Comparing drought trends identified by ESI data from this study to actual drought conditions showed similar trends. Additionally, ESI reacted to the drought more quickly and with greater sensitivity than other drought indices. Our results confirmed that the ESI is advantageous for short and medium-term drought assessment compared to vegetation indices alone. [ABSTRACT FROM AUTHOR]

Although some studies reported the impact of black carbon (BC) on the climate over the Tibetan Plateau (TP), the contribution and mechanisms of BC affecting the water vapor transport to Tibet are not fully understood yet. Here, utilizing the satellite observations and reanalysis data, the effects of BC on the climate over the TP and water vapor transport to the Tibet were investigated by the Community Earth System Model (CESM 2.1.0). Due to the addition of BC, a positive net heat forcing (average is 0.39 W/m2) is exerted at the surface, which induces a pronounced warming effect over the TP and consequently intensifies the East Asian Summer monsoon (EASM). However, significant cooling effects in northern India, Pakistan, Afghanistan and Iran are induced due to the BC and related feedbacks, which reduces significantly the meridional land–sea thermal contrast and finally weakens the South Asian summer monsoon (SASM). Consequently, the water vapor transport to the south border will be decreased due to addition of BC. Moreover, through affecting the atmospheric circulation, the BC could induce an increase in the imported water vapor from the west and east borders of the TP, and an increase outflowing away from the north border of the TP. Overall, due to the BC, the annual mean net importing water vapor over TP is around 271 Gt, which could enhance the precipitation over the TP. The results show that the mean increase in the precipitation over TP is about 0.56 mm/day. [ABSTRACT FROM AUTHOR]

The spatial distribution and dynamic changes of the forests in Primorsky Krai, Russia, are of great significance for regional ecological security and sustainable economic and societal development. With the support of the Google Earth Engine cloud computing platform, we first synthesized yearly Landsat surface reflectance images of the best quality of the research area and then used the random forest method to calculate the forest classification probability of the study area year by year from 1998 to 2015. Furthermore, we used a time–series segmentation algorithm to perform temporal trajectory segmentation for forest classification probability estimation, and determined the spatial and temporal distribution characteristics and change laws of the forest. We extended the existing algorithms and parameters of forest classification probability trajectory analysis, achieving a high overall accuracy (86.2%) in forest change detection in the study area. The extended method can accurately capture the time node information of the changes. In the present research we observed: (1) that from 1998 to 2015, the forest area of the whole district showed a net loss state, with a loss area of 0.56 × 106 ha, of which the cumulative forest disturbance area reached 1.12 × 106 ha, and the cumulative forest recovery area reached 0.55 × 106 ha; and (2) that more than 90% of the forest change occurred in areas with a slope of less than 18°, at a distance of less than 20 km from settlements, and at a distance of less than 10 km from roads. The forest disturbance monitoring results are consistent with the changes in official statistical results over time, but there was a 20% overestimation. The technical method we extended in this study can be used as a reference for large–scale and high–precision dynamic monitoring of the forests in Russia's Far East and other regions of the world; it also provides a basis for estimating illegal timber harvesting and determining the appropriate amount of forest harvested. [ABSTRACT FROM AUTHOR]

Aerosol type is a critical piece of information in both aerosol forcing estimation and passive satellite remote sensing. However, the major aerosol types in China and their variability is still less understood. This work uses direct sun measurements and inversion derived parameters from 47 sites within the Aerosol Robotic Network (AERONET) in China, with more than 39,000 records obtained between April 1998 and January 2017, to identify dominant aerosol types using two independent methods, namely, K means and Self Organizing Map (SOM). In total, we define four aerosol types, namely, desert dust, scattering mixed, absorbing mixed and scattering fine, based on their optical and microphysical characteristics. Seasonally, dust aerosols mainly occur in the spring and over North and Northwest China; scattering mixed are more common in the spring and summer, whereas absorbing aerosols mostly occur in the autumn and winter during heating period, and scattering fine aerosols have their highest occurrence frequency in summer over East China. Based on their spatial and temporal distribution, we also generate seasonal aerosol type maps that can be used for passive satellite retrieval. Compared with the global models used in most satellite retrieval algorithms, the unique feature of East Asian aerosols is the curved single scattering albedo spectrum, which could be related to the mixing of black carbon with dust or organic aerosols. [ABSTRACT FROM AUTHOR]