Observation Satellite Research Articles

Spatio-temporal variability of XCO2 over Indian region inferred from Orbiting Carbon Observatory (OCO-2) satellite and Chemistry Transport Model

Investigation of spatio-temporal variability of column-averaged dry-air mole fraction of CO2 (XCO2) over the Indian region using remote sensing satellite measurements is of interest due to the sparseness of ground-based observations. In this study, we utilized OCO-2 satellite retrievals in conjunction with an atmospheric chemistry-transport model (ACTM) simulations for a set of known (bottom-up) and optimized (top-down) flux for the period September 2014 to December 2018. Results showed the highest XCO2 during the pre-monsoon season, due to prevailing seasonal-high temperatures and drier soil conditions that resulted in increased respiration and suppressed photosynthesis. In contrast, a reduction in XCO2 during the monsoon season is found as precipitation increased soil moisture and moderated the air temperature, driving vegetation growth by which photosynthesis exceeded respiration by the land biosphere. Model - observation differences of XCO2 have shown the overestimation (underestimation) during monsoon (pre-monsoon) especially over the central India region, which might be due to underestimation of the modeled peak-to-trough biospheric fluxes. Analyses of the latitudinal distribution of XCO2 averaged over Indian landmass shows a clear increasing trend and seasonality. An enormous increase in XCO2 of about ~12 ppm during 2015–2018 is estimated from OCO-2, which is in good agreement with model-simulated XCO2 (optimized flux case) and consistent with the global growth rate from surface observations. The time series and seasonal cycle of XCO2 have also been examined using model simulations over different parts of the Indian region and agreed well with those from OCO-2. Over the northern regions, especially over the Indo Gangetic Plain, the peak-to-trough seasonal cycle amplitudes of 2–3 ppm are twice than those in the southern and oceanic regions,~1–1.5 ppm. An annual total CO2 flux of −397 ± 99 TgC/yr is estimated by the inversion and that is consistent with the XCO2 measurements.

Optimization of Aeolus' aerosol optical properties by maximum-likelihood estimation

Abstract. The European Space Agency (ESA) Earth Explorer Mission Aeolus was launched in August 2018, carrying the first Doppler wind lidar in space. Its primary payload, the Atmospheric LAser Doppler INstrument (ALADIN), is an ultraviolet (UV) high-spectral-resolution lidar (HSRL) measuring atmospheric backscatter from air molecules and particles in two separate channels. The primary mission product is globally distributed line-of-sight wind profile observations in the troposphere and lower stratosphere. Atmospheric optical properties are provided as a spin-off product. Being an HSRL, Aeolus is able to independently measure the particle extinction coefficients, co-polarized particle backscatter coefficients and the co-polarized lidar ratio (the cross-polarized return signal is not measured). This way, the retrieval is independent of a priori lidar ratio information. The optical properties are retrieved using the standard correct algorithm (SCA), which is an algebraic inversion scheme and therefore sensitive to measurement noise. In this work, we reformulate the SCA into a physically constrained maximum-likelihood estimation (MLE) problem and demonstrate a predominantly positive impact and considerable noise suppression capabilities. These improvements originate from the use of all available information by the MLE in conjunction with the expected physical bounds concerning positivity and the expected range of the lidar ratio. To consolidate and to illustrate the improvements, the new MLE algorithm is evaluated against the SCA on end-to-end simulations of two homogeneous scenes and for real Aeolus data collocated with measurements by a ground-based lidar and the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite. The largest improvements were seen in the retrieval precision of the extinction coefficients and lidar ratio ranging up to 1 order of magnitude or more in some cases due to effective noise dampening. In real data cases, the increased precision of MLE with respect to the SCA is demonstrated by increased horizontal homogeneity and better agreement with the ground truth, though proper uncertainty estimation of MLE results is challenged by the constraints, and the accuracy of MLE and SCA retrievals can depend on calibration errors, which have not been considered.

High-Rate Attitude Determination of Moving Vehicles With GNSS: GPS, BDS, GLONASS, and Galileo

We investigate the long-term performance and improvement of high-rate multiconstellation Global Navigation Satellite System (GNSS) or multi-GNSS attitude determination in a dynamical environment and evaluate how much each GNSS contributes to such an improvement with applications to land vehicle navigation. We build a multiantenna GNSS system with three nondedicated receivers and conduct two experiments with quad-constellations in GNSS-friendly and GNSS-challenged environments on January 19 and 23, 2018, respectively. A high-grade inertial measurement unit (IMU) provides a reference of comparison for dynamic attitude solutions. In the static case, the global positioning system (GPS), BeiDou navigation satellite system (BDS), and GLObal NAvigation Satellite System (GLONASS) single-constellation solutions have basically the same performance for both experiments. In the dynamic case, GPS achieves the best accuracy in the experiment on January 19, while on January 23, GPS and GLONASS achieve very similar accuracy and significantly outperform BDS in the pitch and roll components. The different dynamic performances of these single constellations are primarily attributed to their differences in observation noise level and satellite geometry strength. By combining GPS, BDS, GLONASS, and Galileo, the multi-GNSS solution performs significantly better than any single-constellation solution. The average improvements in the dynamic cases are 26.2%, 40.7%, and 36.0% on January 19 and 19.2%, 30.0%, and 36.2% on January 23 in the yaw, pitch, and roll components, respectively. The attitude solutions on January 19 are always better determined than on January 23 in the dynamic cases, which should clearly indicate the importance of combining multiple GNSS constellations and the benefit of more observed satellites and a stronger satellite geometry in GNSS-challenged environments. We also introduce the concept of “influence rate” and quantitatively analyze the contribution of each GNSS system to the combined attitude solution.

Prospects Challenges of Bangabandhu Satellite-2

The development of the Bangabandhu Satellite-1 has changed the direction of Bangladesh's satellite communication research. Bangladesh's dream project was the Bangabandhu Satellite 1. This satellite's primary goal is to maintain effective internet and communication services in remote places. First of all, we must concentrate to the depth of satellite communication system and its process to reach any conclusion. Our main goal of the research is to recognize a feasibility review on Bangabandhu satellite-2. So, for the feasibility studies we reviewed the most promising technical parts of Bangabandhu satellite-1. Here, we only focused the certain parts of the satellite such as coding, modulation, battery, purpose of ground station and the benefits of the satellite communication system. Then we moved on the main parts of the Bangabandhu satellite-2. As, Bangabandhu satellite-1 was Geosynchronous equatorial orbit (GEO) communication satellite and Bangabandhu satellite-2 will be Low Earth Orbit (LEO) observation satellite (LEO) so, some of configurations between them might be changed. Furthermore, we largely focused on Facts, efficiency, performance and noticeable difference between two satellites.

Accurate Solar Wind Speed Prediction with Multimodality Information

When the solar wind passes over the Earth, it will cause geomagnetic storms, affect short-wave communications, and threaten the safety of pipelines such as electricity and oil. Accurate prediction of the solar wind speed will allow people to make adequate preparations to avoid wasting resources and affecting people’s life. Most existing methods only use single-modality data as input and do not consider the information complementarity between different modalities. This paper proposes a multimodality prediction (MMP) method that jointly learns vision and sequence information in a unified end-to-end framework for solar wind speed prediction. MMP includes three modules: Vmodule, Tmodule, and Fusion module. Vmodule, which uses pretrained GoogLeNet, is proposed to learn visual representations from the extreme ultraviolet (EUV) images. Tmodule combining one-dimensional CNN with bidirectional long short-term memory (BiLSTM) is applied for learning sequence representation from multivariate time series. Finally, a multimodality fusion method is applied to improve the overall performance. We adopt the EUV images observed by the solar dynamics observatory (SDO) satellite and the OMNIWEB dataset measured at Lagrangian point 1 (L1) to experiment. Comparative experiments have shown that the proposed MMP achieves best performance in many metrics. The ablation experiments also verify the validity of each module and the rationality of the hyperparameter setting.

On the Evolution of a Sub-C Class Flare: A Showcase for the Capabilities of the Revamped Catania Solar Telescope

Solar flares are occasionally responsible for severe space-weather events, which can affect space-borne and ground-based infrastructures, endangering anthropic technological activities and even human health and safety. Thus, an essential activity in the framework of space-weather monitoring is devoted to the observation of the activity level on the Sun. In this context, the acquisition system of the Catania Solar Telescope has been recently upgraded in order to improve its contribution to the European Space Agency (ESA) – Space Weather Service Network through the ESA Portal, which represents the main asset for space weather in Europe. Here, we describe the hardware and software upgrades of the Catania Solar Telescope and the main data products provided by this facility, which include full-disk images of the photosphere and chromosphere, together with a detailed characterization of sunspot groups. As a showcase of the observational capabilities of the revamped Catania Solar Telescope, we report the analysis of a B5.4 class flare that occurred on 7 December 2020, simultaneously observed by the Interface Region Imaging Spectrograph and the Solar Dynamics Observatory satellites.

Improving Leaf Area Index Retrieval Using Multi-Sensor Images and Stacking Learning in Subtropical Forests of China

The leaf area index (LAI) is a key indicator of the status of forest ecosystems that is important for understanding global carbon and water cycles as well as terrestrial surface energy balances and the impacts of climate change. Machine learning (ML) methods offer promising ways of generating spatially explicit LAI data covering large regions based on optical images. However, there have been few efforts to analyze the LAI in heterogeneous subtropical forests with complex terrain by fusing high-resolution multi-sensor data from the Sentinel-1 Synthetic Aperture Radar (SAR), Sentinel-2 Multi Spectral Instrument (MSI), and Advanced Land Observing Satellite-1 digital elevation model (DEM). Here, forest LAI mapping was performed by integrating the MSI, SAR, and DEM data using a stacking learning (SL) approach that incorporates distinct predictions from a set of optimized individual ML algorithms. The method’s performance was evaluated by comparison to field forest LAI measurements acquired in Xingguo and Gandong of subtropical China. The results showed that the addition of the SAR and DEM images using the SL model compared to the inputs of only optical images reduced the mean absolute error (MAE) and root mean square error (RMSE) by 26% and 18%, respectively, in Xingguo, and by 12% and 8%, respectively, in Gandong. Furthermore, the combination of all images had the best prediction performance. SL was found to be more robust and accurate than conventional individual ML models, while the MAE and RMSE were decreased by 71% and 64%, respectively, in Xingguo, and by 68% and 59%, respectively, in Gandong. Therefore, the SL model using the three-source data combination produced satisfied prediction accuracy with the coefficients of determination (R2), MAE, and RMSE of 0.96, 0.17, and 0.28, respectively, in Xingguo and 0.94, 0.30, and 0.47, respectively, in Gandong. This study revealed the potential of the SL algorithm for retrieving the forest LAI using multi-sensor data in areas with complex terrain.

An era of Sentinels in flood management: Potential of Sentinel-1, -2, and -3 satellites for effective flood management

AbstractThe repetitive and destructive nature of floods across the globe causes significant economic damage, loss of human lives, and leaves the people living in flood-prone areas with fear and insecurity. With enough literature projecting an increase in flood frequency, severity, and magnitude in the future, there is a clear need for effective flood management strategies and timely implementation. The earth observatory satellites of the European Space Agency’s Sentinel series, Sentinel-1, Sentinel-2, and Sentinel-3, have a great potential to combat these disastrous floods by their peerless surveillance capabilities that could assist in various phases of flood management. In this article, the technical specifications and operations of the microwave synthetic aperture radar (SAR) onboard Sentinel-1, optical sensors onboard Sentinel-2 (Multispectral Instrument) and Sentinel-3 (Ocean and Land Color Instrument), and SAR altimeter onboard Sentinel-3 are described. Moreover, the observational capabilities of these three satellites and how these observations can meet the needs of researchers and flood disaster managers are discussed in detail. Furthermore, we reviewed how these satellites carrying a range of technologies that provide a broad spectrum of earth observations stand out among their predecessors and have bought a step-change in flood monitoring, understanding, and management to mitigate their adverse effects. Finally, the study is concluded by highlighting the revolution this fleet of Sentinel satellites has brought in the flood management studies and applications.

An Investigation on Seasonal and Diurnal Cycles of TOA Shortwave Radiations from DSCOVR/EPIC, CERES, MERRA-2, and ERA5

Reflected shortwave (SW) solar radiations at the top of atmosphere from Clouds and the Earth’s Radiant Energy System (CERES), Modern Era-Retrospective analysis for Research and Applications version 2 (MERRA-2), and ECMWF Reanalysis 5th Generation (ERA5) are examined to better understand their differences in spatial and temporal variations (seasonal and diurnal cycle timescale) with respect to the observations from the Earth Polychromatic Imaging Camera (EPIC) on the Deep Space Climate Observatory (DSCOVR) satellite. Comparisons between two reanalyses (MERRA-2 and ERA5) and EPIC reveal that MERRA-2 has a generally larger deviation from EPIC than ERA5 in terms of the SW radiance and diurnal variability in all seasons, which can be attributed to larger cloud biases in MERRA-2. MERRA-2 produces more ice/liquid water content than ERA5 over the tropical warm pool, leading to positive SW biases in cloud and radiance, while both reanalyses underestimate the observed SW radiance from EPIC in the stratus-topped region off the western coast of US/Mexico in the boreal summer. Himalaya/Tibet region in the boreal spring/summer and the midlatitude Southern Hemisphere in the boreal winter are the regions where MERRA-2 and ERA5 deviate largely from EPIC, but their deviations have the opposite sign. Vertical structures of cloud ice/liquid water content explain reasonably well these contrasting differences between the two reanalyses. As two independent observations, CERES and EPIC agree well with each other in terms of the SW radiance maps, showing 2–3% mean absolute errors over the tropical midlatitudes. The CERES-EPIC consistency further confirms that the reanalyses still have challenges in representing the SW flux and its global distribution. In the CERES-EPIC observation differences, CERES slightly overestimates the diurnal cycle (as a function of local solar time) of the observed EPIC irradiance in the morning and underestimates it in the afternoon, while the opposite is the case in the reanalyses.

The Importance of Digital Elevation Model Selection in Flood Simulation and a Proposed Method to Reduce DEM Errors: A Case Study in Shanghai

Digital Elevation Models (DEMs) play a critical role in hydrologic and hydraulic modeling. Flood inundation mapping is highly dependent on the accuracy of DEMs. Various vertical differences exist among open access DEMs as they use various observation satellites and algorithms. The problem is particularly acute in small, flat coastal cities. Thus, it is necessary to assess the differences of the input of DEMs in flood simulation and to reduce anomalous errors of DEMs. In this study, we first conducted urban flood simulation in the Huangpu River Basin in Shanghai by using the LISFLOOD-FP hydrodynamic model and six open-access DEMs (SRTM, MERIT, CoastalDEM, GDEM, NASADEM, and AW3D30), and analyzed the differences in the results of the flood inundation simulations. Then, we processed the DEMs by using two statistically based methods and compared the results with those using the original DEMs. The results show that: (1) the flood inundation mappings using the six original DEMs are significantly different under the same simulation conditions—this indicates that only using a single DEM dataset may lead to bias of flood mapping and is not adequate for high confidence analysis of exposure and flood management; and (2) the accuracy of a DEM corrected by the Dixon criterion for predicting inundation extent is improved, in addition to reducing errors in extreme water depths—this indicates that the corrected datasets have some performance improvement in the accuracy of flood simulation. A freely available, accurate, high-resolution DEM is needed to support robust flood mapping. Flood-related researchers, practitioners, and other stakeholders should pay attention to the uncertainty caused by DEM quality.

FIVE DECADES OF SOLAR RESEARCH AT THE PIC DU MIDI TURRET-DOME (1960-2010). PART 1: OVERVIEW OF INSTRUMENTATION AND OBSERVATIONS

The Pic du Midi Turret-Dome, known as 'Lunette Jean Rösch'or LJR ('lunette' for refractor in French), was scientifically active during five decades between 1960 and 2010. It was dedicated to high spatial resolution observations in solar astronomy. We review fifty years of advances in solar instrumentation and solar physics made by this instrument in various domains, which took advantage of the good seeing at Pic du Midi. First, we summarize some of the LJR results in broad-band and wide-field imagery of the photosphere; as this topic was the initial goal and has been the most important contribution of the refractor, details will be provided in Part 2. Then, we present spectro-polarimetric instrumentation and observations with narrow slit and imaging spectroscopy of the photosphere and the chromosphere. The Turret-Dome also housed an original spectro-coronagraph, and observations of the high temperature coronal plasma are highlighted. Finally, we describe progress in the determination of the solar shape with the heliometer. The LJR remained active until the launch of the Hinode and Solar Dynamics Observatory satellites (in 2006 and 2010, respectively).

Earth Imaging From the Surface of the Moon With a DSCOVR/EPIC-Type Camera

The Earth Polychromatic Imaging Camera (EPIC) on the Deep Space Climate Observatory (DSCOVR) satellite observes the entire Sun-illuminated Earth from sunrise to sunset from the L1 Sun-Earth Lagrange point. The L1 location, however, confines the observed phase angles to ∼2°–12°, a nearly backscattering direction, precluding any information on the bidirectional surface reflectance factor (BRF) or cloud/aerosol phase function. Deploying an analog of EPIC on the Moon’s surface would offer a unique opportunity to image the full range of Earth phases, including observing ocean/cloud glint reflection for different phase angles; monitoring of transient volcanic clouds; detection of circum-polar mesospheric and stratospheric clouds; estimating the surface BRF and full phase-angle integrated albedo; and monitoring of vegetation characteristics for different phase angles.

Remote Sensing of Ecosystem Structure—Part 2: Initial Findings of Ecosystem Functioning through Intra- and Inter-Annual Comparisons with Earth Observation Data

This study examines the response of a cold-regions deltaic wetland ecosystem in northwestern Canada to two separate and differing seasonal wetting cycles. The goal of this paper was to examine the nature of reflected electromagnetic energy measured by earth observation (EO) satellites, and to assess whether seasonal wetland hydroperiod and episodic flooding events impact the information retrieved by the Sentinel-2 sensors. The year 2018 represents a year characterized by a large spring freshet and ice-jam flooding, while 2019 represents a year characterized more by summer open-water flooding. We applied the Modified Normalized Difference Wetness Index (MNDWI) to address the effects of the wetting cycles. The response of the vegetative cover was tracked using the fraction of the absorbed photosynthetically active radiation (fAPAR) and the Leaf Area Index (LAI). All three indices were viewed through the lens of cover classes as derived through a previously published study by the authors. The study provides a framework for designing longer-term studies where multiple intra- and inter-annual hydrological cycles can be accessed via EO data. Future studies will enable the examination of lag times inherent in the response to the various water sources applied to spectral response and incorporate this EO approach into a monitoring framework.

An Observational Study of Aerosols and Tropical Cyclones over the Eastern Atlantic Ocean Basin for Recent Hurricane Seasons

The aerosol vertical distribution in the tropical cyclone (TC) main development region (MDR) during the recent active hurricane seasons (2015–2018) was investigated using observations from NASA’s Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Satellite. The Total Attenuated Backscatter (TAB) at 532 nm was measured by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP Lidar) onboard CALIPSO which is a polar orbiting satellite that evaluates the role clouds and atmospheric aerosols play in Earth’s weather, climate and air quality. The TAB was used to illustrate the dispersion and magnitude of the aerosol vertical distribution in the TC-genesis region. A combination of extinction quality flag, cloud fraction, and cloud-aerosol discrimination (CAD) scores were used to filter out the impact of clouds. To better describe the qualitative and quantitative difference of aerosol along the paths of African Easterly Waves (AEWs), the MDR was further divided into two domains from 18° W to 30° W (Domain 1) and 30° W to 45° W (Domain 2), respectively. The distribution of average aerosol concentration from the time of active cyclogenesis was compared and quantified between each case. The resulting observations suggest that there are two distinct layers of aerosols in the vertical profile, a near surface layer from 0.5–1.75 km and an upper layer at 1.75–5 km in altitude. A quantification of the total aerosol concentration values indicate domain 2 cases were associated with higher aerosol concentrations than domain 1 cases. The environmental variables such as sea surface temperature (SST), vertical windshear (VWS), and relative humidity (RH) tended to be favorable for genesis to occur. Among all cases in this study, the results suggested tropical cyclone genesis and further development occurred under dust-loaded conditions while the environmental variables were favorable, indicating that dust aerosols may not play a significant role in inhibiting the genesis process of TCs.

Design and simulation in SAR satellites’ task planning system using genetic algorithm with entropy operator

Purpose This paper aims to innovatively propose to improve the efficiency of satellite observation and avoid the waste of satellite resources, a genetic algorithm with entropy operator (GAE) of synthetic aperture radar (SAR) satellites’ task planning algorithm. Design/methodology/approach The GAE abbreviated as GAE introduces the entropy value of each orbit task into the fitness calculation of the genetic algorithm, which makes the orbit with higher entropy value more likely to be selected and participate in the remaining process of the genetic algorithm. Findings The simulation result shows that in a condition of the same calculate ability, 85% of the orbital revisit time is unchanged or decreased and 30% is significantly reduced by using the GAE compared with traditional task planning genetic algorithm, which indicates that the GAE can improve the efficiency of satellites’ task planning. Originality/value The GAE is an optimization of the traditional genetic algorithm. It combines entropy in thermodynamics with task planning problems. The algorithm considers the whole lifecycle of task planning and gets the desired results. It can greatly improve the efficiency of task planning in observation satellites and shorten the entire task execution time. Then, using the GAE to complete SAR satellites’ task planning is of great significance in reducing satellite operating costs and emergency rescue, which brings certain economic and social benefits.

Solar Wind Speed Prediction With Two‐Dimensional Attention Mechanism

AbstractAs more and more high‐technical systems are exposed to the space environment, extreme space weather becomes a great threat to human society. In the solar system, space weather is influenced by the solar wind, such that reliable prediction of solar wind conditions in the near‐Earth environment effectively reduces the impact of space weather on human society. Solar wind speed prediction is improved by making full use of OMNI data measured at Lagrangian Point 1 (L1) by the National Aeronautics and Space Administration (NASA) and image data observed by the Solar Dynamics Observatory (SDO) satellite in this work. Specifically, we propose a model based on the “two‐dimensional attention mechanism” (TDAM) to predict solar wind speed. In this study, we first analyze and preprocess data from 2011 to 2017. Second, considering the characteristics of time series data, we adopt the gated recurrent units (GRU) model which can deal with long‐term dependence as the prediction part of our model. Third, we design a TDAM, which enables our prediction network to focus on important parts. Three performance indices are used: root‐mean‐square error (RMSE), mean absolute error (MAE), and correlation coefficient (CC). By comparing TDAM with other models, we find that the TDAM model achieves the best prediction results, with RMSE of 62.8 km/s, MAE of 47.8 km/s, and CC of 0.789 24 h in advance. The experimental results show that the proposed TDAM model can improve the prediction accuracy of solar wind speed.

Vertical structure of cloud radiative heating in the tropics: confronting the EC-Earth v3.3.1/3P model with satellite observations

Abstract. Understanding the coupling of clouds to large-scale circulation is one of the grand challenges for the global climate research community. In this context, realistically modelling the vertical structure of cloud radiative heating (CRH) and/or cooling in Earth system models is a key premise to understand this coupling. Here, we evaluate CRH in two versions of the European Community Earth System Model (EC-Earth) using retrievals derived from the combined radar and lidar data from the CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellites. One model version is also used with two different horizontal resolutions. Our study evaluates large-scale intraseasonal variability in the vertical structure of CRH and cloud properties and investigates the changes in CRH during different phases of the El Niño–Southern Oscillation (ENSO), a process that dominates the interannual climate variability in the tropics. EC-Earth generally captures both the intraseasonal and meridional pattern of variability in CRH over the convectively active and stratocumulus regions and the CRH during the positive and negative phases of ENSO. However, two key differences between model simulations and satellite retrievals emerge. First, the magnitude of CRH, in the upper troposphere, over the convectively active zones is up to twice as large in the models compared to the satellite data. Further dissection of net CRH into its shortwave and longwave components reveals noticeable differences in their vertical structure. The shortwave component of the radiative heating is overestimated by all model versions in the lowermost troposphere and underestimated in the middle troposphere. These over- and underestimates of shortwave heating are partly compensated by an overestimate of longwave cooling in the lowermost troposphere and heating in the middle troposphere. The biases in CRH can be traced back to disagreement in cloud amount and cloud water content. There is no noticeable improvement of CRH by increasing the horizontal resolution in the model alone. Our findings highlight the importance of evaluating models with satellite observations that resolve the vertical structure of clouds and cloud properties.

Tropical rainfall monitoring with commercial microwave links in Sri Lanka

Commercial microwave links (CMLs) from cellular telecommunication networks can provide a valuable ‘opportunistic’ source of high-resolution space-time rainfall information, complementing traditional in-situ measurement devices (rain gauges, disdrometers) and remote sensors (weather radars, satellites). Their greatest potential lies in areas with low gauge densities and lack of weather radars, often in developing countries with a subtropical or tropical climate and generally large spatial rainfall variability. Here, the open-source R package RAINLINK is employed to retrieve CML rainfall maps covering the majority of Sri Lanka for a 3.5 month period based on CML data from on average 1140 link paths. These are compared locally to hourly and daily rain gauge data, as well as to rainfall maps from the dual-frequency precipitation radar on board the global precipitation measurement core observatory satellite. The potential of CMLs for real-time tropical rainfall monitoring is demonstrated.

A study of different compression algorithms for multispectral images

Remote Sensing satellites acquire information about an area by analyzing the transmitted and reflected radio waves. The Earth Imaging satellites capture high resolution images which occupy more storage space on-board and consume a lot of bandwidth for downlink transmission. Multispectral sensors represent information of the images in multiple bands which are typically less than 15. The multispectral image compression algorithms aim to reduce the size of the images while preserving their quality. This paper involves a study of various algorithms used for compression of multispectral imagery.

Examining the Impact of Different DEM Sources and Geomorphology on Flash Flood Analysis in Hyper-Arid Deserts

Digital elevation models (DEMs) are the cornerstone for hydrological and geomorphological modeling. Herein, two Nile-tributary catchments (Wadi Al Rishrash and Wadi Atfeh) in Egypt are selected to examine the contribution of different DEMs to the accuracy of hydrological and geomorphological analyses in the hyper-arid Sahara. DEMs sources include: Advanced Land Observing Satellite-1 (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) (12.5 m resolution), ALOS World 3D with 30 m resolution (AW3D30), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER DEM with 30 m resolution) and the Shuttle Radar Topography Mission (SRTM with 30 and 90 m resolution), in addition to topographic map-derived DEM (90 m resolution). Using a hypothetical uniformly-distributed 10 mm rainfall event, the estimated parameters, including: flow duration, time to peak and peak discharge rates, are almost similar for the different DEMs and thus technical aspects related to sources and resolutions of the datasets impose insignificant control on quantitative flash-flood analyses. Conversely, variations in geological and geomorphological characteristics of the catchments show more significant control on the hydrograph magnitudes as indicated by the different parameters of the two catchments. These findings indicate that understanding the geological and hydrological evolution of the catchment is essential for integrated management strategies of floods especially in the Saharan–Arabian deserts and in similar conditions of hyper-aridity and scarce in situ data worldwide.