Some aspects of accuracy of radar/satellite fixes of tropical cyclone over Bay of Bengal

Consequent to installation of 10 cyclone detection radars and availability of INSAT observations availability of fixes* of cyclones by two or more radars and satellite has become a common feature during tracking of cyclones. Generally these fixes differ from each other to some extent. The paper presents a study of tracks of four cyclones in Bay of Bengal as determined by coastal radars and satellite. It is seen that satellite fix is generally closer to coast as compared to radar fix. Amongst radar fixes, the fix of radar closest to the storm may be considered as best fix of the system.

Three different glacier surges at a spot: What satellites observe and what not

In the Karakoram, dozens of glacier surges occurred in the past two decades, making the region one of its global hot spots. Detailed analyses of dense time series from optical and radar satellite images revealed a wide range of surge behaviour in this region: from slow advances longer than a decade at low flow velocities to short, pulse-like advances over one or two years with high velocities. In this study, we present an analysis of three currently surging glaciers in the central Karakoram: North and South Chongtar Glaciers and an unnamed glacier referred to as NN9. All three glaciers flow towards the same region but differ strongly in surge behaviour. A full suite of satellite sensors and digital elevation models (DEMs) from different sources are used to (a) obtain comprehensive information about the evolution of the surges from 2000 to 2021 and (b) to compare and evaluate capabilities and limitations of the different satellite sensors for monitoring relatively small glaciers in steep terrain. A strongly contrasting evolution of advance rates and flow velocities is found, though the elevation change pattern is more similar. For example, South Chongtar Glacier had short-lived advance rates above 10 km y−1, velocities up to 30 m d−1 and surface elevations increased by 200 m. In contrast, the neighbouring and three times smaller North Chongtar Glacier had a slow and near linear increase of advance rates (up to 500 m y−1), flow velocities below 1 m d−1 and elevation increases up to 100 m. The even smaller glacier NN9 changed from a slow advance to a full surge within a year, reaching advance rates higher than 1 km y−1. It seems that, despite a similar climatic setting, different surge mechanisms are at play and a transition from one mechanism to another can occur during a single surge. The sensor inter-comparison revealed a high agreement across sensors for deriving flow velocities, but limitations are found on small and narrow glaciers in steep terrain, in particular for Sentinel-1. All investigated DEMs have the required accuracy to clearly show the volume changes during the surges and elevations from ICESat-2 ATL06 data fit neatly. We conclude that the available satellite data allow for a comprehensive observation of glacier surges from space when combining different sensors to determine the temporal evolution of length, elevation and velocity changes.

Comparative Analysis of DTM Results with Hydro Enforcement LiDAR Data Method and Interferometric Synthetic Aperture Radar (InSAR) from Radar Satellite Imagery (Case Study of Kebumen Regency)

Data containing information on the terrain elevation model is necessary for several uses related to human activities, such as development planning, spatial planning, disaster modeling, disaster mitigation planning, land productivity estimation, etc. Information about the ground elevation can be presented in a 3-dimensional topographical model such as Digital Terrain Model (DTM). There are several technologies used to form DTM data, including by using LiDAR and radar satellites (Sentinel-1). The hydro enforcement method is used to process DTM with LiDAR data by modifying the elevation value of LiDAR data in water areas during data processing. The height of this feature is modified digitally to achieve hydrological connectivity. This method aims to produce a DTM according to the principles of hydro enforcement and hydro flatten. While for processing DTM radar data, the InSAR method is used. InSAR is a remote sensing technique to extract three-dimensional information from the earth’s surface with the phase of radar waves. Additional data of morphological information and break lines were added to provide more representative information on the actual situation. The result of this research is the value of vertical geometry accuracy (LE90) of DTM to RBI data with a scale of 1:25,000. In this research, 5 kinds of DTM have been successfully formed with LE90 vertical accuracy values are as follows: LiDAR DTM with LE90 of 4.614 m; InSAR DTM with LE90 of 9.583 m; InSAR breakline with LE90 of 9.433 m; InSAR RBI assimilation with LE90 of 2.532 m; and InSAR DTM-LiDAR assimilation with LE90 of 4.077 m. DTM with the highest accuracy based on Topographic Map (RBI) 1:25,000 is InSAR DTM RBI assimilation and the lowest accuracy is DTM InSAR without breakline and assimilation data.

The June 2020 Aniangzhai landslide in Sichuan Province, Southwest China: slope instability analysis from radar and optical satellite remote sensing data

A large, deep-seated ancient landslide was partially reactivated on 17 June 2020 close to the Aniangzhai village of Danba County in Sichuan Province of Southwest China. It was initiated by undercutting of the toe of this landslide resulting from increased discharge of the Xiaojinchuan River caused by the failure of a landslide dam, which had been created by the debris flow originating from the Meilong valley. As a result, 12 townships in the downstream area were endangered leading to the evacuation of more than 20000 people. This study investigated the Aniangzhai landslide area by optical and radar satellite remote sensing techniques. A horizontal displacement map produced using cross-correlation of high-resolution optical images from Planet shows a maximum horizontal motion of approximately 15 meters for the slope failure between the two acquisitions. The undercutting effects on the toe of the landslide are clearly revealed by exploiting optical data and field surveys, indicating the direct influence of the overflow from the landslide dam and water release from a nearby hydropower station on the toe erosion. Pre-disaster instability analysis using a stack of SAR data from Sentinel-1 between 2014 and 2020 suggests that the Aniangzhai landslide has long been active before the failure, with the largest annual LOS deformation rate more than 50 mm/yr. The 3-year wet period that followed a relative drought year in 2016 resulted in a 14% higher average velocity in 2018–2020, in comparison to the rate in 2014–2017. A detailed analysis of slope surface kinematics in different parts of the landslide indicates that temporal changes in precipitation are mainly correlated with kinematics of motion at the head part of the failure body, where an accelerated creep is observed since spring 2020 before the large failure. Overall, this study provides an example of how full exploitation of optical and radar satellite remote sensing data can be used for a comprehensive analysis of destabilization and reactivation of an ancient landslide in response to a complex cascading event chain in the transition zone between the Qinghai-Tibetan Plateau and the Sichuan Basin.

Processing the results of space observation of the processed areas of Karaganda

The article is devoted to the development of a method for tracking deformation in the underworked territories of Karaganda based on the data processed by radar images from the ENVISAT satellite. The article provides an overview of the use of modern radar satellite systems. The step-by-step search of archival data on the territory of Karaganda in the Eoli-sa program is described. The processing of radar images from the ENVISAT satellite for the period from 2003 to 2010 in the SARscape module of the ENVI software package is described in detail. Based on the processed data, graphs of dynamic processes were compiled. The analysis of the results of interferometric processing of radar data is performed. Traditional and modern methods of tracking the deformation of underworked territories are also analyzed.

SAR data and field surveys combination to update rainfall-induced shallow landslides inventory

The Campania region has been recurrently hit by severe landslides in volcanoclastic deposits. The city of Naples, and in particular the Camaldoli and Agnano hills, also suffered several landslide crises in weathered volcanoclastic rocks as a consequence of intense rainfalls or wildfires. This work provides an updated landslide database for the suburbs of Naples. The obtained database consists of about 1322 landslides considering available information dated back to 1816, identified thanks to the historical newspaper's examination; 62 landslides were recorded between 2019 and 2020. Furthermore, the achieved database is the result of the standardization of available information from different sources, organized in a completely different way. The newly identified phenomena have been recognized thanks to a combination of optical satellite imagery with Google Earth, Sentinel-1 radar satellite imagery and field investigation. The implemented methodology is based on change detection analysis of satellite imagery by using polarimetric features. Subsequently, output derived from the segmentation procedure of satellite images have been compared with field trip observations. The main purpose of this procedure is to emphasize areas where land cover changes, potentially related to slope failures, occur in the Phegraean Fields, facilitating the following possible phases of mapping and/or field survey. Eventually, this type of information is expected to help decision-makers with land planning and risk assessment.

Detection and Analysis of the Causes of Intensive Harmful Algal Bloom in Kamchatka Based on Satellite Data

In this paper, the causes of the anomalous harmful algal bloom which occurred in the fall of 2020 in Kamchatka have been detected and analyzed using a long-term time series of heterogeneous satellite and simulated data with respect to the sea surface height (HYCOM) and temperature (NOAA OISST), chlorophyll-a concentration (MODIS Ocean Color SMI), slick parameters (SENTINEL-1A/B), and suspended matter characteristics (SENTINEL-2A/B, C2RCC algorithm). It has been found that the harmful algal bloom was preceded by temperature anomalies (reaching 6 °C, exceeding the climatic norm by more than three standard deviation intervals) and intensive ocean level variability followed by the generation of vortices, mixing water masses and providing nutrients to the upper photic layer. The harmful algal bloom itself was manifested in an increase in the concentration of chlorophyll-a, its average monthly value for October 2020 (bloom peak) approached 15 mg/m3, exceeding the climatic norm almost four-fold for the region of interest (Avacha Gulf). The zones of accumulation of a large amount of biogenic surfactant films registered in radar satellite imagery correlate well with the local regions of the highest chlorophyll-a concentration. The harmful bloom was influenced by river runoff, which intensively brought mineral and biogenic suspensions into the marine environment (the concentration of total suspended matter within the plume of the Nalycheva River reached 10 mg/m3 and more in 2020), expanding food resources for microalgae.

Assessment of a Gauge-Radar-Satellite Merged Hourly Precipitation Product for Accurately Monitoring the Characteristics of the Super-Strong Meiyu Precipitation over the Yangtze River Basin in 2020

The recently developed gauge-radar-satellite merged hourly precipitation dataset (CMPAS-NRT) offers broad applications in scientific research and operations, such as intelligent grid forecasting, meteorological disaster monitoring and warning, and numerical model testing and evaluation. In this paper, we take a super-long Meiyu precipitation process experienced in the Yangtze River basin in the summer of 2020 as the research object, and evaluate the monitoring capability of the CMPAS-NRT for the process from multiple perspectives, such as error indicators, precipitation characteristics, and daily variability in different rainfall areas, using dense surface rain-gauge observation data as a reference. The results show that the error indicators for CMPAS-NRT are in good agreement with the gauge observations. The CMPAS-NRT can accurately reflect the evolution of precipitation during the whole rainy season, and can accurately capture the spatial distribution of rainbands, but there is an underestimation of extreme precipitation. At the same time, the CMPAS-NRT product features the phenomenon of overestimation of precipitation at the level of light rain. In terms of daily variation of precipitation, the precipitation amount, frequency, and intensity are basically consistent with the observations, except that there is a lag in the peak frequency of precipitation, and the frequency of precipitation at night is less than observed, and the intensity of precipitation is higher than observed. Overall, the CMPAS-NRT product can successfully reflect the precipitation characteristics of this super-heavy Meiyu precipitation event, and has a high potential hydrological utilization value. However, further improvement of the precipitation algorithm is needed to solve the problems of overestimation of light rainfall and underestimation of extreme precipitation in order to provide more accurate hourly precipitation monitoring dataset.