Utilization Of Satellite Data Research Articles

Research on Landsat 8 land surface temperature retrieval and spatial–temporal migration capabilities based on random forest model

Land surface temperature (LST) plays a crucial role in characterizing land surface processes and the energy balance. Accurate monitoring of spatial–temporal variations in LST holds great significance for global and regional climate change research. However, with the increasing of multi-source remote sensing data, it has become a challenging task to construct an LST model that reduces dependence on auxiliary data (such as land surface emissivity (LSE) and atmospheric water vapor content (WVC) synchronized with satellite observations. This study proposed an LST retrieval model based on random forest model (RFLSTR). The results of 10-fold cross-validation showed that the RFLSTR model had high modelling accuracy, the root mean square error (RMSE) and mean absolute error (MAE) were lower than 2 K. Landsat 8 images from the Beijing area and South Korea, were utilized to assess the spatial–temporal migration performance of the RFLSTR model. Additionally, Landsat 9 images from South Korea were employed to analyse the sensor migration performance of the RFLSTR model. In conclusion, the findings highlight that the proposed RFLSTR model achieve high retrieval accuracy without relying on auxiliary data (such as LSE and WVC) synchronized with satellite observations. Moreover, the RFLSTR model exhibits robust spatial–temporal capabilities, enhancing the efficiency of LST retrieval and facilitating the utilization of satellite data.

Towards Improved Satellite Data Utilization in China: Insights from an Integrated Evaluation of GSMaP-GNRT6 in Rainfall Patterns

To improve the utilization of satellite-based data and promote their development, this analysis comprehensively evaluates the performance of GSMaP Near-real-time Gauge-adjusted Rainfall Product version 6 (GSMaP_GNRT6) data in depicting precipitation over China from 2001 to 2020 by comparing four precipitation indices—accumulated precipitation, number of rainy days and rainstorm days, and precipitation maxima—with daily precipitation data from 2419 stations across China on monthly and annual time scales. The results show that the GSMaP-GNRT6 data effectively capture the overall spatial pattern of the four precipitation indices, but biases in the spatial distribution of the number of rainy days from July to September and the precipitation maxima during the wintertime are evident. A general underestimation of GSMaP-GNRT6 data is observed in the average for China. The annual precipitation amount, the number of rainy days and rainstorm days, and the precipitation maxima based on the GSMaP-GNRT6 data are 17.6%, 35.5%, 31.6% and 11.8% below the station observations, respectively. The GSMaP-GNRT6 data better depict the precipitation in eastern China, with the errors almost halved. And obvious overestimation of the number of rainstorm days and precipitation maxima occurs in western China, reaching up to 60%. Regarding the accumulated precipitation, the number of rainstorm days and the precipitation maxima, the GSMaP-GNRT6 data show an almost consistent interannual variation and increasing trends that are consistent with the station observations. However, the magnitude of the increasing trend based on the GSMaP-GNRT6 data is larger, especially at the beginning of the 21st century. Conversely, a considerable discrepancy in the annual variation and an almost opposite trend can be observed in the number of rainy days between the GSMaP-GNRT6 data and the station observations.

Spectral Fingerprinting of Methane from Hyper-Spectral Sounder Measurements Using Machine Learning and Radiative Kernel-Based Inversion

Satellite-based hyper-spectral infrared (IR) sensors such as the Atmospheric Infrared Sounder (AIRS), the Cross-track Infrared Sounder (CrIS), and the Infrared Atmospheric Sounding Interferometer (IASI) cover many methane (CH4) spectral features, including the ν1 vibrational band near 1300 cm−1 (7.7 μm); therefore, they can be used to monitor CH4 concentrations in the atmosphere. However, retrieving CH4 remains a challenge due to the limited spectral information provided by IR sounder measurements. The information required to resolve the weak absorption lines of CH4 is often obscured by interferences from signals originating from other trace gases, clouds, and surface emissions within the overlapping spectral region. Consequently, currently available CH4 data product derived from IR sounder measurements still have large errors and uncertainties that limit their application scope for high-accuracy climate and environment monitoring applications. In this paper, we describe the retrieval of atmospheric CH4 profiles using a novel spectral fingerprinting methodology and our evaluation of performance using measurements from the CrIS sensor aboard the Suomi National Polar-orbiting Partnership (SNPP) satellite. The spectral fingerprinting methodology uses optimized CrIS radiances to enhance the CH4 signal and a machine learning classifier to constrain the physical inversion scheme. We validated our results using the atmospheric composition reanalysis results and data from airborne in situ measurements. An inter-comparison study revealed that the spectral fingerprinting results can capture the vertical variation characteristics of CH4 profiles that operational sounder products may not provide. The latitudinal variations in CH4 concentration in these results appear more realistic than those shown in existing sounder products. The methodology presented herein could enhance the utilization of satellite data to comprehend methane’s role as a greenhouse gas and facilitate the tracking of methane sources and sinks with increased reliability.

Retrieval of Missing Remotely Sensed Tropospheric NO2 Data Using Tensor Completion

Missing values in remotely sensed satellite data present a significant challenge for accurate analysis and interpretation of environmental data. Factors such as dead pixel values or cloud coverage can lead to significant gaps in datasets, degrading its overall quality and value. Tensor completion utilizes high-dimensional arrays known as tensors and their low-rank decompositions to recover missing values. This paper demonstrates the application and value of applying Tensor Completion to enhance remotely sensed Nitrogen-Dioxide satellite data and proposes an algorithm for its use. An efficient and accurate recovery method is proposed by leveraging relationships within the tensor and by employing suitable tensor decomposition methods. The proposed algorithm will enhance the analysis, interpretation, and utilization of satellite data. The algorithm is validated on real-world satellite datasets and its superiority demonstrated against existing alternate data recovery methods such as IDW and Kriging.

The Utilization of Satellite Data and Machine Learning for Predicting the Inundation Height in the Majalaya Watershed

The Majalaya area is one of the most valuable economic districts in the south of Greater Bandung, West Java, Indonesia, and experiences at least six floods per year. The floods are characterized by a sudden rise in the water level approximately one to two hours after the rain occurs. With the aim of reducing flood risk, this study models a data-driven method for predicting the inundation height across the Majalaya Watershed. The flood inundation maps of selected events were modeled using the HEC-RAS 2D numerical model. Extracted data from the HEC-RAS model, GSMaP satellite rainfall data, elevation, and other spatial data were combined to build an artificial neural network (ANN) model. The trained model targets inundation height, while the spatiotemporal data serve as the explanatory variables. The results from the trained ANN model provided very good R2 (0.9537), NSE (0.9292), and RMSE (0.3701) validation performances. The ANN model was tested with a new dataset to demonstrate the capability of predicting flood inundation height with unseen data. Such a data-driven approach is a promising tool to be developed to reduce flood risks in the Majalaya Watershed and other flood-prone locations.

JAXA EARTH OBSERVATION DASHBOARD WITH COG AND WMS/WMTSS

Abstract. JAXA has developed and implemented earth observation (EO) dashboard jointly with ESA and NASA. The development of the JAXA dashboard, along with the "Earth-graphy" website and the newly developed "JAXA Earth API" service, demonstrate JAXA's commitment to providing climate change and earth science information to users worldwide. The EO dashboard serves as a platform to deliver valuable data and information related to climate change. The WMS/WMTS technology allows users to visualize and interact with geospatial information by providing web-based mapping services. This technology enhances the user experience by enabling the display of satellite imagery, overlays, and other geospatial data layers within the EO dashboard. To further facilitate the efficient use of satellite data, JAXA has developed the JAXA Earth API service. This service offers a user-friendly interface for accessing and utilizing JAXA's Earth observation satellite image data. By providing an easy-to-use format, JAXA aims to promote the effective utilization of satellite data and encourage its widespread use. Overall, the development and operation of the JAXA dashboard, with its integration of COG format data, WMS/WMTS technology, Python-based API. This paper introduces the status of development of JAXA Earth Observation dashboard with COG format data, WMS/WMTS technology, phyton based API and JAXA Earth Observation missions.

Evaluation of SST Data Products from Multi-Source Satellite Infrared Sensors in the Bohai-Yellow-East China Sea

The measurement of sea surface temperature (SST) is of utmost importance in the realm of oceanography. The increasing utilization of satellite data in SST research has highlighted the crucial need to compare and evaluate various satellite data sources. Using iQuam2 in situ SST data, this study aims to assess the accuracy of SST datasets obtained from three polar-orbiting satellites (AVHRR, Modis-Aqua, and Modis-Terra) and one geostationary satellite (Himawari-8) in the Bohai-Yellow-East China Sea (BYECS) throughout 2019. The results showed a strong correlation between satellite and in situ data, with R correlation coefficients exceeding 0.99. However, the accuracy of the satellite datasets exhibited some variability, with Himawari-8 showing the highest deviation error and MODIS-Aqua showing the least. Subsequently, the Modis-Aqua data were used as a benchmark to evaluate the SST data of the other three satellites over the previous six years (July 2015–June 2021). The results indicate that, in addition to intricate temporal variations, the deviations of the three satellites from Modis-Aqua also show significant spatial disparities due to the effect of seawater temperature. Compared to Modis-Aqua, the deviation of Himawari-8 generally displayed a negative trend in BYECS and showed pronounced seasonal variation. The deviation of AVHRR showed a negative trend across all regions except for a substantial positive value in the coastal region, with the time variation exhibiting intricate features. The SST values obtained from MODIS-Terra exhibited only marginal disparities from MODIS-Aqua, with positive values during the day and negative values at night. All three satellites showed significantly abnormal bias values after December 2020, indicating that the MODIS-Aqua-derived SST reference dataset may contain outliers beyond this period. In conclusion, the accuracy of the four satellite datasets varies across different regions and time periods. However, they could be effectively utilized and integrated with relevant fusion algorithms to synthesize high-precision datasets in the future.

Three Decades of Indian Remote Sensing in Coastal Research

The launch of the first Indian Remote Sensing Satellite in 1988 ushered in a new era of utilization of satellite data and technology in natural resources monitoring, coastal area management, disaster mitigation, weather and climate change studies. India with its very long coast line, areal extent, resources and population would have been a huge challenge to the coastal researchers and administrators if not for the Indian Remote Sensing satellite data that provided the much needed temporal, thematic information for effective mapping, monitoring and management of the coastal areas. The country’s large and diverse coastal regions are economically important and ecologically rich but also extremely vulnerable and highly prone to natural and anthropogenic hazards. Through the last three decades, the Indian remote sensing programme has contributed immensely to our understanding of the coast and ocean processes, coastal ecosystems, habitats, marine pollution and the vulnerability of the coast to natural hazards. IRS-1C with its improved spatial resolutions, additional spectral band, improved repetitively ushered in a new era of research specifically in the management of coastal areas and resources. Availability of spatial data at various resolutions, low cost, advances in technology resulted in new applications for management coastal areas. IRS-1C also can be aptly termed as the precursor for thematic satellite series such as OCEANSAT, RESOURCESAT and CARTOSAT, thereby setting the environs for thematic research in coastal areas. Integrated utilization of remote sensing data, numerical models, GIS and GPS has paved the way for the development of operation systems for management of coastal areas, thereby realizing the vision of Dr. Vikram Sarabhai, the father of Indian Space Programme, of building a self-reliant and indigenous Space Programme for the betterment of quality of life of the common people.

IRS-1C Satellite Data Utilization for Groundwater Prospects Mapping for the Entire Country Under National Rural Drinking Water Program (NRDWP): A State of the Art Initiative

Conventional methods of groundwater targeting often result in failures due to improper selection of sites as a result of the lack of scientific database. Satellite-based mapping using geographic information system (GIS) has brought a paradigm shift in the mapping of the natural resources including groundwater. Remote sensing is a cost effective technique to carry out the groundwater prospects mapping which otherwise is difficult to achieve at national scale. A comprehensive database could be created on groundwater resource mapping and assessment using IRS LISS III satellite data from IRS-1C and its follow-on missions like IRS-1D and Resourcesat-1 for the entire India which was first of its kind. A national mission called National Rural Drinking Water Program was launched in the year 1999 to carry out groundwater prospects mapping at 1:50,000 scale for the entire country using 23.5 m resolution IRS LISS III satellite data. The objective of the project was to generate groundwater prospects maps on 1:50,000 scale in GIS format showing (a) different prospective zones in terms of yield and depth of aquifer and (b) tentative sites of different recharge structures suitable in the area. It is imperative that the mammoth task of preparing 4898 groundwater prospects maps on 1:50,000 scale for the entire country has become a reality due to the timely and continuous availability of LISS III satellite images from IRS-1C sensor and its follow-on missions.

The Earth Observation Data Utilization Promotion Platform

In recent years, many satellites have been launched and there are many data obtained from various sensors. Utilization of satellite data is becoming active due to improvements in AI technology. In order to promote the use of satellite data, a platform that provides satellite data that is convenient for users is indispensable. This paper introduces “The Earth Observation Data Utilization Promotion Platform” currently operated by PASCO.

Influence of thin cirrus clouds on the short-wave infrared satellite observation of atmospheric CO2

Optically thin cirrus clouds have low solar reflectance but are still efficient at absorbing infrared radiation emitted by the surface and atmosphere, which is one of the important factors for satellite detecting the greenhouse gas CO2. The existence of the cloud greatly reduces the utilization of satellite data. Aiming at improving the utilization effectively of XCO2, the effects of thin cirrus clouds on forward modeling spectra and retrieved results are investigated in this study. The cloud base of 6 km, 8 km, 10 km and the thickness of 0.1 km, 0.2 km, 0.3 km, 0.4 km are simulated to obtain spectra of O2–A (0.76 μm) and CO2 (1.6 μm). The simulation results show that the radiation intensity is reduced due to the existence of thin cirrus clouds for O2–A band, and the radiation intensity is enhanced for the CO2 band. The mean values of spectral residuals for both bands are increased with the increase of cloud base and thickness, and they are nearly zero value. The retrieval results show that the relative errors of O2–A total column concentration are in the range of 0.08 %–0.09 % for all thin cirrus clouds, and the relative errors of CO2 total column concentration are 0.16 %–0.35 %. Therefore, the relative errors of XCO2 are 0.06 %–0.25 %.

Mapping of Snow Depth by Blending Satellite and In-Situ Data Using Two-Dimensional Optimal Interpolation—Application to AMSR2

The development of a snow depth product over North America is investigated by applying two-dimensional optimal interpolation to passive microwave satellite-derived and in-situ measured snow depth. At each snow-covered satellite footprint, the technique computes a snow depth increment as the weighted average of data increments, and updates the satellite-derived snow depth accordingly. Data increments are computed as the difference between the in-situ-measured and satellite snow depth at station locations surrounding the satellite footprint. Calculation of optimal weights is based on spatial lag autocorrelation of snow depth increments, modelled as functions of horizontal distance and elevation difference between pairs of observations. The technique is applied to Advanced Microwave Scanning Radiometer 2 (AMSR2) snow depth and in-situ snow depth obtained from the Global Historical Climatology Network. The results over North America during January–February 2017 indicate that the technique greatly enhances the performance of the satellite estimates, especially over mountain terrain, albeit with an accuracy inferior to that over low elevation areas. Moreover, the technique generates more accurate output compared to that from NOAA’s Global Forecast System, with implications for improving the utilization of satellite data in snow assessments and numerical weather prediction.

Two Sides to Same Drought: Measurement and Impact of Ethiopia’s 2015 Historical Drought

Climate change is likely to lead to more frequent and more severe droughts with risk of socio-economic suffering. Utilization of satellite data, available almost instantaneously, is a great tool now providing the backbone of drought monitoring. However, the measurement of drought for socio-economic impact is poorly understood, questioning our ability to deal efficiently with future challenges. This study showcases the challenge by estimating the impact of Ethiopia’s historical 2015 drought, reported to be the worst in five decades, according to different drought indicators. The analysis shows a sever meteorological and self-reported drought, with substantially less rainfall than average and a high share of households reporting drought exposure. Also, more than half of the rural households reported lower consumption in 2016 than two years prior, despite large amounts of food aid were distributed in response to the drought. There are, however, no or limited signs of an agricultural drought, based on vegetation indices and predicted crop losses. Agricultural production, price, and wage data also support 2015 being a normal agricultural year. Further, based on spatial rank correlation, none of the different drought indicators (self-reported, vegetation anomalies, predicted crop losses, or rain anomalies) provide a coherent story of where the drought was worst. In fact, across years the spatial rank correlation between drought indicators shifts sign, highlighting that the inconsistent information on drought from different indicators is not restricted to 2015. In terms of drought impact on consumption, no impact, on average, is found for rain anomalies, predicted crop losses, or vegetation anomalies in the growing season, while a large impact is found for self-reported drought exposure and a smaller impact for vegetation anomalies in the harvest season. The self-reported drought exposure is not a trustworthy drought indicator due to endogeneity, while the impact from vegetation anomalies in the harvest season seems driven by better than average, as opposed to worse than average, vegetation. With indicators showing two such different sides to the same drought and limited spatial correlation between them, the case study calls for more research on the suitability of these indicators, and care and robustness checks when utilizing drought indicators to understand socio-economic impact.

Aqua MODIS and altimetry satellite data utilization for determining the effective time and area of fishing in South Sulawesi

South Sulawesi is one of the provinces in Indonesia which geographically is a marine-based area with huge potential of marine resources. In sea resources exploration activities are often found obstacles caused by the variability of weather and climate that affect the condition of South Sulawesi waters. Limited availability of weather data in the waters of South Sulawesi Province has caused difficulties in analyzing and identifying conditions in these waters. The use of the MODIS Aqua satellite and Altimetry can be used as a solution in the analysis and identification of water conditions. Aqua MODIS satellite data used in this study is the sea surface temperature (SST) and chlorophyll-a data while the Altimetry satellite data used is significant wave height data. In addition, Niño 3.4 Index are used as supporting data to identify the weather and climate phenomena that occur in the waters of South Sulawesi in this case are El Niño and the Southern Oscillation (ENSO) phenomena. SST and chlorophyll-a data are used as indicators in determining fish catch potential and significant wave height data are used to determine the effective time in fishing activities in the waters of South Sulawesi Province. The results showed that a significant wave peak height in the waters of South Sulawesi occurred in July. The highest chlorophyll-a average occurred in August when the average SST showed the lowest value or when the El Niño event occurred.

On the Methodology of Satellite Data Utilization in Multi-Modeling Approach for Socio-Ecological Risks Assessment Tasks: A Problem Formulation

The problem of satellite data utilization in multi-modeling approach for socio-ecological risks assessment is formally defined. Methodology of natural geo-systems modeling for variables and indicators selections is described. Observation, measurement and modeling data utilization method in the framework of multi-model approach is described. Methodology and models of risk assessment in framework of decision support approach are defined and described. Brief conclusions on efficiency of the described methodology are proposed. Proposed methodology can applied for wide range of risk-related tasks, such as natural and technological disaster monitoring, air-water-soil pollution control, crop productivity control, etc.

Satellite-based technique for nowcasting of thunderstorms over Indian region

India experiences severe thunderstorms during the months, March–June. But these systems are not predicted well, mainly due to the absence of mesoscale observational network over Indian region and the expert system. As these are short lived systems, the nowcast is attempted worldwide based on satellite and radar observations. Due to inadequate radar network, satellite plays the dominant role for nowcast of these thunderstorms. In this study, a nowcast based algorithm ForTracc developed by Vila et al. (Weather Forecast 23:233–245, 2008) has been examined over the Indian region using Infrared Channel \((10.8~\upmu \hbox {m})\) of INSAT-3D for prediction of Mesoscale Convective Systems (MCS). In this technique, the current location and intensity in terms of Cloud Top Brightness Temperature (CTBT) of the MCS are extrapolated. The purpose of this study is to validate this satellite-based nowcasting technique for Convective Cloud Clusters that helps in optimum utilization of satellite data and improve the nowcasting. The model could predict reasonably the minimum CTBT of the convective cell with average absolute error (AAE) of \({<}7\hbox { K}\) for different lead periods (30–180 min). However, it was underestimated for all the lead periods of forecasts. The AAE in the forecasts of size of the cluster varies from about \(3\times 10^{4}\hbox { km}^{2}\) for 30-min forecast to \(7\times 10^{4}\hbox { km}^{2}\) for 120-min forecast. The mean absolute error in prediction of size is above 31–38% of actual size for different lead periods of forecasts from 30 to 180 min. There is over estimation in prediction of size for 30 and 60 min forecasts (17% and 2.6% of actual size of the cluster, respectively) and underestimation in 90 to 180-min forecasts (–2.4% to –28%). The direct position error (DPE) based on the location of minimum CTBT ranges from 70 to 144 km for 30–180-min forecast respectively.

S4: An O2R/R2O Infrastructure for Optimizing Satellite Data Utilization in NOAA Numerical Modeling Systems: A Step Toward Bridging the Gap between Research and Operations

Abstract In 2011, the National Oceanic and Atmospheric Administration (NOAA) began a cooperative initiative with the academic community to help address a vexing issue that has long been known as a disconnection between the operational and research realms for weather forecasting and data assimilation. The issue is the gap, more exotically referred to as the “valley of death,” between efforts within the broader research community and NOAA’s activities, which are heavily driven by operational constraints. With the stated goals of leveraging research community efforts to benefit NOAA’s mission and offering a path to operations for the latest research activities that support the NOAA mission, satellite data assimilation in particular, this initiative aims to enhance the linkage between NOAA’s operational systems and the research efforts. A critical component is the establishment of an efficient operations-to-research (O2R) environment on the Supercomputer for Satellite Simulations and Data Assimilation Studies (S4). This O2R environment is critical for successful research-to-operations (R2O) transitions because it allows rigorous tracking, implementation, and merging of any changes necessary (to operational software codes, scripts, libraries, etc.) to achieve the scientific enhancement. So far, the S4 O2R environment, with close to 4,700 computing cores (60 TFLOPs) and 1,700-TB disk storage capacity, has been a great success and consequently was recently expanded to significantly increase its computing capacity. The objective of this article is to highlight some of the major achievements and benefits of this O2R approach and some lessons learned, with the ultimate goal of inspiring other O2R/R2O initiatives in other areas and for other applications.

UTILIZATION OF LANDSAT DATA FOR WATER QUALITY OBSERVATION IN SMALL INLAND WATER BODIES

Abstract. Many studies deal with water quality evaluation using remotely sensed data. In the field of remote sensing, there have been proposed several procedures how to observe selected parameters of water quality and conditions. The majority of works use methods and procedures based on satellite data but they usually do not deal with suitability and practicability of the satellite data. This paper provides summary of determinants and limitations of satellite data utilization for water quality evaluation. Cloud cover and its influence on size of visible water surfaces is the most deeply evaluated determinants. Temporal resolution, spatial resolution and some other technical factors are discussed as next determinants. The case study demonstrates evaluation of the determinants for Landsat 7 and Landsat 8 data (level 1) and for area of small ponds in part of Pardubice region in the Czech Republic. It clearly demonstrates several limitations of Landsat data for evaluation of selected parameters of water quality and changes of small water bodies.

UTILIZATION OF LANDSAT DATA FOR WATER QUALITY OBSERVATION IN SMALL INLAND WATER BODIES

Many studies deal with water quality evaluation using remotely sensed data. In the field of remote sensing, there have been proposed several procedures how to observe selected parameters of water quality and conditions. The majority of works use methods and procedures based on satellite data but they usually do not deal with suitability and practicability of the satellite data. This paper provides summary of determinants and limitations of satellite data utilization for water quality evaluation. Cloud cover and its influence on size of visible water surfaces is the most deeply evaluated determinants. Temporal resolution, spatial resolution and some other technical factors are discussed as next determinants. The case study demonstrates evaluation of the determinants for Landsat 7 and Landsat 8 data (level 1) and for area of small ponds in part of Pardubice region in the Czech Republic. It clearly demonstrates several limitations of Landsat data for evaluation of selected parameters of water quality and changes of small water bodies.

防災減災の観点から考える

防災減災の観点から考える