Satellite Mapping(위성 매핑)란 무엇입니까?
Satellite Mapping 위성 매핑 - Microsatellite mapping (GA)15 showed markings distributed in euchromatic regions, while mapping with (CA)15 showed marking patterns in heterochromatic regions, together with a fully marked chromosome pair. [1] In this paper we present an overview on current investigations on the potential and performance of satellite mappings (optical and SAR) to complete existing avalanche databases and expand them to previously unobserved regions. [2] According to the pixel coordinate of the laser footprint in the stereo image, the laser altimeter together with the footprint camera can provide planimetric geodetic coordinates for the control points of a higher accuracy than the other traditional satellite laser altimeters, and represents a new technology for satellite mapping. [3] Satellite mapping of buildings and built-up areas used to be delineated from high spatial resolution (e. [4] Continuous monitoring of soil moisture in arid regions is a major problem as existing techniques such as point sensors or satellite mapping can have high associated costs per hectare. [5] Communities reported declines in many other provisioning ES, and these results were supported by satellite mapping, which showed substantial reductions in land covers with high ES value (shrubland and wetland), leading to consequent ES declines. [6] ABSTRACT In recent years, satellite mapping of North Korea, especially of its labor camps, has become an important form of evidence of human rights violations, used by transnational advocacy groups to lobby Western governments for change. [7] The topics concerned are satellite mapping of coastal landuse changes, numerical simulation of the tide and wave climate and of coastal erosion, coastal and estuarine mangrove squeeze, wave and current damping in mangroves and wave transmission through bamboo fences. [8] From 23 mangrove-fringed countries, they observed human activity by satellite mapping of nighttime light. [9] Key outcomes relevant to ExoMars rover included a key recognition of the importance of field trials for (i) understanding how to operate the ExoMars rover instruments as a suite, (ii) building an operations planning team that can work well together under strict time-limited pressure, (iii) developing new processes and workflows relevant to the ExoMars rover, (iv) understanding the limits and benefits of satellite mapping and (v) practicing efficient geological interpretation of outcrops and landscapes from rover-based data, by comparing the outcomes of the simulated mission with post-trial, in-situ field observations. [10] From broader discussion of the simulation scenario, it was possible to identify aspects of resilience for further study in a wider research project, such as identifying hazardous slopes from satellite mapping, informing the fieldwork program, designing social questionnaires to understand risk perception and formulating questions to guide focus-group discussions on community resilience. [11] Here we investigate the factors that contribute to gully formation using a combination of satellite mapping, field observations and statistical analysis of morphological data. [12]Microsatellite mapping(GA)15는 euchromatic 영역에 분포된 표시를 보여주었으며, (CA)15를 사용한 mapping은 완전히 표시된 염색체 쌍과 함께 heterochromatic 영역에 표시 패턴을 보여주었습니다. [1] 이 문서에서 우리는 기존의 눈사태 데이터베이스를 완성하고 이전에 관찰되지 않은 지역으로 확장하기 위한 위성 매핑(광학 및 SAR)의 잠재력과 성능에 대한 현재 조사에 대한 개요를 제시합니다. [2] 스테레오 이미지에서 레이저 발자국의 픽셀 좌표에 따르면 발자국 카메라와 함께 레이저 고도계는 다른 전통적인 위성 레이저 고도계보다 더 높은 정확도의 제어점에 대한 평면 측지 좌표를 제공할 수 있으며 위성을 위한 새로운 기술을 나타냅니다. 매핑. [3] 높은 공간 해상도(예: [4] 포인트 센서 또는 위성 매핑과 같은 기존 기술은 헥타르당 관련 비용이 높을 수 있으므로 건조한 지역의 토양 수분을 지속적으로 모니터링하는 것은 주요 문제입니다. [5] 지역 사회는 다른 많은 프로비저닝 ES의 감소를 보고했으며 이러한 결과는 위성 매핑에 의해 뒷받침되었는데, 이는 ES 값이 높은 토지 덮개(덤불 및 습지)의 상당한 감소를 보여 결과적 ES 감소로 이어졌습니다. [6] 요약 최근 몇 년 동안 북한, 특히 노동 수용소에 대한 위성 지도는 초국가적 옹호 단체가 서방 정부에 변화를 촉구하기 위해 사용하는 인권 침해의 중요한 형태가 되었습니다. [7] 관련된 주제는 해안 토지 이용 변화의 위성 매핑, 조수 및 파도 기후와 해안 침식의 수치 시뮬레이션, 해안 및 하구 맹그로브 압착, 맹그로브의 파도 및 조류 감쇠, 대나무 울타리를 통한 파도 전달입니다. [8] 맹그로브 숲이 우거진 23개국에서 그들은 야간 조명의 위성 매핑을 통해 인간 활동을 관찰했습니다. [9] ExoMars 로버와 관련된 주요 결과에는 (i) ExoMars 로버 장비를 제품군으로 작동하는 방법 이해, (ii) 엄격한 시간 제한 조건에서 함께 잘 작동할 수 있는 운영 계획 팀 구축을 위한 현장 시험의 중요성에 대한 주요 인식이 포함되었습니다. 압력, (iii) ExoMars 로버와 관련된 새로운 프로세스 및 워크플로 개발, (iv) 위성 매핑의 한계와 이점 이해, (v) 시험 후 현장 관찰을 통한 모의 임무. [10] 시뮬레이션 시나리오에 대한 광범위한 논의를 통해 위성 매핑에서 위험한 경사 식별, 현장 작업 프로그램에 알리기, 위험 인식을 이해하기 위한 사회적 설문 설계 및 지역사회 회복력에 대한 포커스 그룹 토론을 안내합니다. [11] 여기에서 우리는 위성 매핑, 현장 관찰 및 형태학적 데이터의 통계적 분석의 조합을 사용하여 협곡 형성에 기여하는 요인을 조사합니다. [12]
multi satellite precipitation
It evaluates six of the latest GPM-era SPPs: Integrated Multi-satellite Retrievals for GPM (IMERG) “Early”, “Late”, and “Final” run SPPs (IMERG-E, IMERG-L, and IMERG-F, respectively), and Global Satellite Mapping of Precipitation (GSMaP) near-real-time (GSMaP-NRT), standard version (GSMaP-MVK), and standard version with gauge-adjustment (GSMaP-GAUGE) SPPs, and two TRMM Multi-satellite Precipitation Analysis SPPs (3B42RT and 3B42V7). [1] This can improve a quality of the multi-satellite precipitation product called Global Satellite Mapping of Precipitation (GSMaP) developed by the JAXA under the Global Precipitation Measurement (GPM) Mission if realized. [2] With the launch of the Global Precipitation Measurement (GPM) Core Observatory, two advanced high-resolution multi-satellite precipitation products namely; Integrated Multi-satellite Retrievals for GPM (IMERG) and Global Satellite Mapping of Precipitation (GSMaP) are released. [3] A comprehensive validation of three satellite precipitation datasets (SPDs), including (1) the Climate Prediction Center Morphing algorithm (CMORPH), (2) Global Satellite Mapping of Precipitation (GSMaP) Reanalysis, and (3) Tropical Rainfall Measuring Mission multi-satellite precipitation analysis (TRMM) 3B42, was conducted using the rain gauge-based Vietnam Gridded Precipitation dataset (VnGP) and rain gauge station data for Central Vietnam. [4]최신 GPM 시대 SPP 중 6개를 평가합니다. GPM(IMERG) "Early", "Late" 및 "Final" 실행 SPP(각각 IMERG-E, IMERG-L 및 IMERG-F용 통합 다중 위성 검색) ), 그리고 GSMaP(Global Satellite Mapping of Precipitation) 근실시간(GSMaP-NRT), 표준 버전(GSMaP-MVK), 게이지 조정이 있는 표준 버전(GSMaP-GAUGE) SPP 및 2개의 TRMM 다중 위성 강수 분석 SPP(3B42RT 및 3B42V7). [1] 이것은 실현된다면 JAXA가 GPM(Global Precipitation Measurement) 임무에 따라 개발한 GSMaP(Global Satellite Mapping of Precipitation)라는 다중 위성 강수 제품의 품질을 향상시킬 수 있습니다. [2] nan [3] nan [4]
Global Satellite Mapping 글로벌 위성 매핑
, the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) 3B42V7, the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement V06 post-real time Final Run precipitation products (IMF6), Global Rainfall Map in Near-real-time Gauge-calibrated Rainfall Product (GSMaP_Gauge_NRT) for product version 6 (GNRT6) and gauge-adjusted Global Satellite Mapping of Precipitation V6 (GGA6) were considered. [1] We have improved a rainfall retrieval algorithm over coast for the Advanced Microwave Sounding Unit (AMSU) in order to upgrade to the Global Satellite Mapping of Precipitation (GSMaP) product version 8. [2] The selected SBP products were Global Satellite Mapping of Precipitation (GSMaP) and Climate Hazards Group Infrared Precipitations with Stations (CHIRPS). [3] This study aims to evaluate the performances of two latest released global precipitation measurement (GPM)-era satellite precipitation final run products (integrated merged multisatellite retrievals IMERG-V06B and Global Satellite Mapping of Precipitation GSMaP-V07) and one tropical rainfall measuring mission (TRMM)-era product (TMPA-3B42-V07) at hourly, daily, and monthly scale over the Tibetan plateau (TP), with special focus on the performances at different rain intensities, subbasins, and elevations at daily scales. [4] The aim of this study is to simulate recent flood inundation using global satellite mapping of precipitation (GSMaP) products. [5] This study evaluated the performance of three versions of Global Satellite Mapping of Precipitation (GSMaP) products at 0. [6] In this study, an attempt was made to correct the bias of Global Satellite Mapping of Precipitation near-real-time (GSMaP_NRT) product. [7] We employed Global Satellite Mapping of Precipitation (GSMaP)-NRT and Integrated Multi-satellitE Retrievals for GPM (IMERG)-Early for NRT SPPs as well as BTOP model (Block-wise use of the TOPMODEL (Topographic-based hydrologic model)) for flood runoff simulation. [8] The surface air temperature (T2m) and the precipitation in summer during 2016–2018 are evaluated against the in-situ station observations and the Global Satellite Mapping of Precipitation (GSMaP) dataset. [9] Global Satellite Mapping of Precipitation (GSMaP) products, as important satellite-based precipitation products (SPPs) of Global Precipitation Measurement (GPM) mission, have provided hydrologists with critical precipitation data sources for hydrological applications in gauge-sparse or ungauged basins. [10] Daily and monthly rainfall data derived from the Tropical Rainfall Measuring Mission (TRMM), Global Satellite Mapping of Precipitation (GSMaP) and Multifunctional Transport Satellites (MTSAT) were analyzed to identify meteorological drought. [11] The surface air temperature (T2m) and the precipitation in summer during 2016–2018 are evaluated against the in-situ station observations and the Global Satellite Mapping of Precipitation (GSMaP) dataset. [12] 0 Global Satellite Mapping of Precipitation (GSMaP) and version 6. [13] , the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement [IMERG], Global Satellite Mapping of Precipitation [GSMaP], and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks datasets) during five typhoon-related heavy precipitation events in the Philippines between 2016 and 2018. [14] This paper presents a case study comparing the latest algorithm version of Global Satellite Mapping of Precipitation (GSMaP) data with C-band and X-band Multi-Parameter (MP) radar as high-resolution rainfall data in terms of localized heavy rainfall events. [15] The data from rain gauge stations are used to evaluate the quality of Integrated Multi-satellite Retrievals for GPM–Early (IMERG-E), Global Satellite Mapping of Precipitation–Near Real-Time (GSMaP-NRT), Climate Prediction Center Morphing Method (CMORPH), and HydroEstimator (HE) at the pixel-station level; and these SPPs are used as meteorological inputs for the hourly hydrological modeling. [16] This study aims to evaluate and compare the satellite rainfall estimates (SREs) derived from tropical rainfall measuring mission (TRMM 3B43v7), rainfall estimation from remotely sensed information using artificial neural networks—climate data record (PERSIANN-CDR), merged satellite-gauge rainfall estimate (IMERG), and the Global Satellite Mapping of Precipitation (GSMaP) with ground-observed data over the varied terrain of hydrologically diverse central and northeastern parts of Ethiopia—Awash River Basin (ARB). [17] Two recent satellite rainfall estimates (SREs) from Global Precipitation Measurement (GPM)-era—Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG-V06) and gauge calibrated Global Satellite Mapping of Precipitation (GSMaP-V07) are evaluated for their spatiotemporal accuracy and ability to capture extreme precipitation events using 279 gauge stations from southern slope of central Himalaya, Nepal, between 2014 and 2019. [18] The gauge-adjusted version of six widely used datasets was adopted; namely, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (CDR), Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), Global Precipitation Measurement Ground Validation National Oceanic and Atmospheric Administration Climate Prediction Center (NOAA CPC) Morphing Technique (CMORPH), Integrated Multi-Satellite Retrievals for GPM (GPM), Global Satellite Mapping of Precipitation (GSMaP), the Tropical Rainfall Measuring Mission (TRMM) and Multi-satellite Precipitation Analysis (TMPA). [19] The impact of diurnal precipitation over Sumatra Island, the Indonesian Maritime Continent (MC), on synoptic disturbances over the eastern Indian Ocean is examined using high-resolution rainfall data from the Global Satellite Mapping of Precipitation project and the Japanese 55-year Reanalysis data during the rainy season from September to April for the period 2000–2014. [20] This study assessed four near-real-time satellite precipitation products (NRT SPPs) of Global Satellite Mapping of Precipitation (GSMaP)—NRT v6 (hereafter NRT6), NRT v7 (hereafter NRT7), Gauge-NRT v6 (hereafter GNRT6), and Gauge-NRT v7 (hereafter GNRT7)— in representing the daily and monthly rainfall variations over Taiwan, an island with complex terrain. [21] The performance of three satellite-based high-resolution gridded rainfall datasets, namely the gauge corrected Global Satellite Mapping of Precipitation (GSMaP), Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG), and the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) in the hot desert climate of Egypt were assessed. [22] GSMaP (Global Satellite Mapping of Precipitation) is a project aiming (1) to produce high-precision and high-resolution global precipitation map using satellite-borne microwave radiometer data, (2) to develop reliable microwave radiometer algorithms, and (3) to establish precipitation map techniques using multi-satellite data for GPM. [23] The PPV potential was estimated considering the effects of the said meteorological parameters using several satellite data including shortwave radiation from Advanced Himawari Imager 8 (AHI8), MOD04 aerosol data from Moderate Resolution Imaging Spectroradiometer (MODIS), precipitation rate from Global Satellite Mapping of Precipitation (GSMaP), air temperature from NCEP/DOE AMIP-II Reanalysis-2 data, and snow water equivalent (SWE) from Microwave Scanning Radiometer for the Earth Observing System (AMSR-E). [24] This study primarily focused on the impacts of the twelve input sources used in the latest Global Satellite Mapping of Precipitation for Global Precipitation Measurement (GPM-GSMaP) for different climatic regions, elevations, and seasons over mainland China. [25] The Global Forecast System (GFS) analysis data were utilised with INSAT-3D sounder profile and compared with Global Satellite Mapping of Precipitation (GSMaP). [26] Four SRPs, Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) - Early, - Late (IMERG-E, IMERG-L), Global Satellite Mapping of Precipitation-Near Real Time (GSMaP-NRT), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks- Cloud Classification System (PERSIANN-CCS) and rain gauge data were used as the primary input to a hydrological model, Rainfall-Runoff-Inundation (RRI) and the simulated flood level and runoff were compared with the observed data using statistical metrics. [27] The Global Satellite Mapping of Precipitation (GSMaP) was used to estimate the accumulated rainfall in May from the Mei-Yu front in Taiwan. [28] Second, satellite data for temperature (MOD11), precipitation (global satellite mapping of precipitation), dust (MOD04), and snow cover (MOD10) were processed to derive the effective solar PV efficiency. [29] This study evaluates the following GPM-era and TRMM-era SPPs based on two years (2014–2015) of reference daily precipitation data from rain gauge networks in ten mountainous regions: Integrated Multi-SatellitE Retrievals for GPM (IMERG, version 05B and version 06B), National Oceanic and Atmospheric Administration (NOAA)/Climate Prediction Center Morphing Method (CMORPH), Global Satellite Mapping of Precipitation (GSMaP), and Multi-Source Weighted-Ensemble Precipitation (MSWEP), which represents a global precipitation data-blending product. [30] Version 4 (V4) Global Satellite Mapping of Precipitation (GSMaP) products and quantitative precipitation estimates derived from a local ground-based S-band dual polarization weather radar (radar, hereafter) were used for parallel comparisons. [31] The performance of the Global Satellite Mapping of Precipitation data Microwave-Infrared Combined Reanalysis Product (GSMaP RNL), version 6, was evaluated, using northern Vietnam as the test area. [32] The study focused on investigating characteristics of horizontal and vertical precipitation structure of TCs affecting the central region of Vietnam in relation with their tracks and landing regions by using radar satellite data of TRMM (Tropical Rainfall Measuring Mission) and microwave rainfall retrieval of GSMaP (Global Satellite Mapping of Precipitation). [33] In the current work, the use of Global Satellite Mapping of Precipitation (GSMaP) data as input of ITU-R P. [34] spatial cloud propagation index occurring as well as the Global Satellite Mapping of Precipitation (GSMaP) satellite to see the spatial distribution of rainfall as a result of Kai-Tak tropical cyclone. [35] Global Satellite Mapping of Precipitation (GSMaP) is a system to produce global surface precipitation field with 0. [36] Global Satellite Mapping of Precipitation (GSMaP) product is an important satellite precipitation product of Global Precipitation Measurement (GPM) mission, which provides an alternative means to ground-based rainfall estimates, in which case a rigorous product assessment was required before implementation. [37] It evaluates six of the latest GPM-era SPPs: Integrated Multi-satellite Retrievals for GPM (IMERG) “Early”, “Late”, and “Final” run SPPs (IMERG-E, IMERG-L, and IMERG-F, respectively), and Global Satellite Mapping of Precipitation (GSMaP) near-real-time (GSMaP-NRT), standard version (GSMaP-MVK), and standard version with gauge-adjustment (GSMaP-GAUGE) SPPs, and two TRMM Multi-satellite Precipitation Analysis SPPs (3B42RT and 3B42V7). [38] A rain-gauge-adjusted algorithm for global satellite mapping of precipitation (GSMaP) that estimates the surface precipitation rate with resolutions of 0. [39] Global Precipitation Measurement (GPM), CPC Morphing Technique (CMORPH) Global Precipitation Analyses, and Global Satellite Mapping of Precipitation (GSMaP), were used to compare with the model results. [40] This can improve a quality of the multi-satellite precipitation product called Global Satellite Mapping of Precipitation (GSMaP) developed by the JAXA under the Global Precipitation Measurement (GPM) Mission if realized. [41] With the launch of the Global Precipitation Measurement (GPM) Core Observatory, two advanced high-resolution multi-satellite precipitation products namely; Integrated Multi-satellite Retrievals for GPM (IMERG) and Global Satellite Mapping of Precipitation (GSMaP) are released. [42] Methods: Time-series analysis was performed using dengue cases in four regions of the Philippines and monthly climatic variables extracted from Global Satellite Mapping of Precipitation for rainfall, and MODIS for the land surface temperature and normalized difference vegetation index from 2008-2015. [43] The rainfall distribution from Global Satellite Mapping (GSMaP) imagery depict a 800 km rain area with varied rainfall intensity which reaches 40 mm/hour. [44] 25° regular grid: Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis, Integrated MultisatellitE Retrievals for Global Precipitation Measurement (GPM) (IMERG), National Oceanic and Atmospheric Administration/Climate Prediction Center Morphing Method, Precipitation Estimation from Remote Sensing Information using Artificial Neural Network, and Global Satellite Mapping of Precipitation (GSMaP). [45] Therefore, this study aims to propose an optimal SDCI (OSDCI) using an alternative Global Satellite Mapping of Precipitation (GSMaP) as precipitation input, adding a varied lag time between precipitation and vegetation response, determining the optimal weights of variables and revising the severity classification. [46] To do so, the Integrated Multi-satellitE Retrievals for GPM (IMERG)-v5 and the Global Satellite Mapping of Precipitation (GSMaP)-v7 were evaluated at their original 0. [47] A comprehensive validation of three satellite precipitation datasets (SPDs), including (1) the Climate Prediction Center Morphing algorithm (CMORPH), (2) Global Satellite Mapping of Precipitation (GSMaP) Reanalysis, and (3) Tropical Rainfall Measuring Mission multi-satellite precipitation analysis (TRMM) 3B42, was conducted using the rain gauge-based Vietnam Gridded Precipitation dataset (VnGP) and rain gauge station data for Central Vietnam. [48] Global Satellite Mapping of Precipitation (GSMaP), integrated with the Bartlett-Lewis rectangular pulses (BLRP) model, to disaggregate the daily in situ rainfall, which is then further used to derive more reliable IDF curves. [49] In this study, three mainstream different-spatial-scale daily gridded precipitation data (GPD), including [Global Satellite Mapping of Precipitation–gauge adjusted (GSMAP_Gauge), TRMM 3B42 version-7 (TRMM 3B42V7) and Global Precipitation Climatology Project version 1. [50], 열대 강우 측정 임무(TRMM) 다중 위성 강수 분석(TMPA) 3B42V7, 전지구 강수 측정을 위한 통합 다중 위성 검색 V06 사후 실시간 최종 실행 강수 제품(IMF6), 거의 실제의 전지구 강우 지도 제품 버전 6(GNRT6)에 대한 게이지 보정 강우량 제품(GSMaP_Gauge_NRT)과 강수량 V6(GGA6)의 게이지 보정 글로벌 위성 매핑이 고려되었습니다. [1] GSMaP(Global Satellite Mapping of Precipitation) 제품 버전 8로 업그레이드하기 위해 AMSU(Advanced Microwave Sounding Unit)에 대한 해안의 강우 검색 알고리즘을 개선했습니다. [2] 선택된 SBP 제품은 GSMaP(Global Satellite Mapping of Precipitation) 및 CHIRPS(Climate Hazards Group Infrared Precipitations with Stations)였습니다. [3] 이 연구는 두 개의 최신 GPM(Global 강수량 측정) 시대 위성 강수 최종 실행 제품(통합된 다중 위성 검색 IMERG-V06B 및 Global Satellite Mapping of Precipitation GSMaP-V07)과 하나의 열대 강우 측정 임무(TRMM)의 성능을 평가하는 것을 목표로 합니다. )-시대 제품(TMPA-3B42-V07)은 티베트 고원(TP)에서 시간별, 일별 및 월별 규모로, 일일 규모에서 다양한 강우 강도, 유역 및 고도에서의 성능에 특히 중점을 둡니다. [4] 이 연구의 목적은 강수량의 글로벌 위성 매핑(GSMaP) 제품을 사용하여 최근 홍수 범람을 시뮬레이션하는 것입니다. [5] 이 연구는 0에서 GSMaP(Global Satellite Mapping of Precipitation) 제품의 세 가지 버전의 성능을 평가했습니다. [6] 본 연구에서는 GSMaP_NRT(Global Satellite Mapping of Precipitation Near-Time) 곱의 편향을 수정하려고 시도하였다. [7] 우리는 BTOP 모델뿐만 아니라 NRT SPP에 대해 GSMaP(Global Satellite Mapping of Precipitation)-NRT 및 IMERG(Integrated Multi-satellite Retrievals for GPM)-Early를 사용했습니다. 홍수 유출 시뮬레이션. [8] 2016-2018년 동안의 지표 기온(T2m)과 여름 강수는 현장 관측소 관측과 강우량의 글로벌 위성 매핑(GSMaP) 데이터 세트에 대해 평가됩니다. [9] GSMaP(Global Satellite Mapping of Precipitation) 제품은 GPM(Global Precipitation Measurement) 임무의 중요한 위성 기반 강수량 제품(SPP)으로서 수문학자들에게 게이지가 희박하거나 측정되지 않은 유역의 수문학적 응용을 위한 중요한 강수량 데이터 소스를 제공했습니다. [10] TRMM(Tropical Rainfall Measuring Mission), GSMaP(Global Satellite Mapping of Precipitation) 및 MTSAT(Multifunctional Transport Satellite)에서 파생된 일별 및 월별 강우 데이터를 분석하여 기상 가뭄을 식별했습니다. [11] 2016-2018년 동안의 지표 기온(T2m)과 여름 강수는 현장 관측소 관측과 강우량의 글로벌 위성 매핑(GSMaP) 데이터 세트에 대해 평가됩니다. [12] 0 강수량의 글로벌 위성 매핑(GSMaP) 및 버전 6. [13] , 2016년 사이에 필리핀에서 발생한 5개의 태풍 관련 호우 이벤트 동안에 인공 신경망 데이터 세트를 사용하여 원격으로 감지된 정보로부터 강수량 추정[IMERG], 글로벌 위성 매핑(GSMaP), 강수량 추정을 위한 통합 다중 위성 검색 그리고 2018. [14] 본 논문은 고해상도 강우 데이터인 GSMaP(Global Satellite Mapping of Precipitation) 데이터의 최신 알고리즘 버전과 C-band 및 X-band Multi-Parameter(MP) 레이더를 국부적인 집중호우 이벤트와 비교한 사례 연구를 제시합니다. [15] 강우량 측정소의 데이터는 GPM-조기 통합 다중 위성 검색(IMERG-E), 강수량의 글로벌 위성 매핑-거의 실시간(GSMaP-NRT), 기후 예측 센터 모핑 방법의 품질을 평가하는 데 사용됩니다. CMORPH) 및 픽셀 스테이션 수준의 HydroEstimator(HE); 그리고 이러한 SPP는 시간별 수문 모델링을 위한 기상 입력으로 사용됩니다. [16] 이 연구는 열대 강우 측정 임무(TRMM 3B43v7)에서 파생된 위성 강우 추정치(SRE), 인공 신경망을 이용한 원격 감지 정보로부터의 강우 추정치-기후 데이터 기록(PERSIANN-CDR), 위성-계기 통합 강우량을 평가 및 비교하는 것을 목적으로 합니다. 추정치(IMERG) 및 에티오피아의 수문학적으로 다양한 중앙 및 북동부의 다양한 지형(ARB)에 대한 지상 관측 데이터를 사용한 강수량의 글로벌 위성 매핑(GSMaP). [17] GPM(Global Precipitation Measurement) 시대의 2개의 최근 위성 강우 추정치(SRE) - IMERG-V06(Integrated Multi-Satellite Retrievals for Global Precipitation Measurement) 및 게이지 보정된 Global Satellite Mapping of Precipitation(GSMaP-V07)은 시공간에 대해 평가됩니다. 2014년과 2019년 사이에 네팔 히말라야 중부의 남쪽 경사면에서 279개의 게이지 스테이션을 사용하여 극한 강수 현상을 포착하는 정확도와 능력. [18] 널리 사용되는 6개 데이터 세트의 게이지 조정 버전이 채택되었습니다. 즉, 인공 신경망–기후 데이터 기록(CDR)을 사용한 원격 감지 정보로부터의 강수량 추정, 관측소를 통한 기후 위험 그룹 적외선 강수(CHIRPS), 전지구 강수 측정 지상 검증 국립해양대기청 기후예측센터(NOAA CPC) 모핑 기법 (CMORPH), GPM(GPM)을 위한 통합 다중 위성 검색, 강수량의 글로벌 위성 매핑(GSMaP), 열대 강우 측정 임무(TRMM) 및 다중 위성 강수 분석(TMPA). [19] <p>동인도양의 종관 교란에 대한 인도네시아 해양대륙(MC) 수마트라 섬의 일강수 영향은 Global Satellite Mapping of Precipitation 프로젝트와 일본 55년 연도의 고해상도 강우 데이터를 사용하여 조사되었습니다. 2000~2014년 9월부터 4월까지 장마철 재분석 자료. [20] 이 연구는 GSMaP(Global Satellite Mapping of Precipitation)의 NRT v6(이하 NRT6), NRT v7(이하 NRT7), Gauge-NRT v6(이하 GNRT6) 및 Gauge-NRT v7(이하 GNRT7) - 복잡한 지형을 가진 섬인 대만의 일별 및 월별 강우량 변화를 나타냅니다. [21] 3개의 위성 기반 고해상도 격자형 강우 데이터 세트의 성능, 즉 게이지 수정 전 지구 강수량 매핑(GSMaP), 전 지구 강수 측정을 위한 통합 다중 위성 검색(IMERG) 및 관측소가 있는 기후 위험 그룹 적외선 강수( CHIRPS) 이집트의 뜨거운 사막 기후에서 평가되었습니다. [22] GSMaP(Global Satellite Mapping of Precipitation)는 (1) 위성 기반 마이크로파 복사계 데이터를 사용하여 고정밀 및 고해상도 지구 강수 지도를 생성하고, (2) 신뢰할 수 있는 마이크로파 복사계 알고리즘을 개발하고, (3) 다음을 목표로 하는 프로젝트입니다. GPM에 대한 다중 위성 데이터를 사용하여 강수량 지도 기술을 설정합니다. [23] nan [24] nan [25] nan [26] nan [27] nan [28] nan [29] nan [30] nan [31] nan [32] nan [33] nan [34] nan [35] nan [36] nan [37] 최신 GPM 시대 SPP 중 6개를 평가합니다. GPM(IMERG) "Early", "Late" 및 "Final" 실행 SPP(각각 IMERG-E, IMERG-L 및 IMERG-F용 통합 다중 위성 검색) ), 그리고 GSMaP(Global Satellite Mapping of Precipitation) 근실시간(GSMaP-NRT), 표준 버전(GSMaP-MVK), 게이지 조정이 있는 표준 버전(GSMaP-GAUGE) SPP 및 2개의 TRMM 다중 위성 강수 분석 SPP(3B42RT 및 3B42V7). [38] nan [39] nan [40] 이것은 실현된다면 JAXA가 GPM(Global Precipitation Measurement) 임무에 따라 개발한 GSMaP(Global Satellite Mapping of Precipitation)라는 다중 위성 강수 제품의 품질을 향상시킬 수 있습니다. [41] nan [42] nan [43] nan [44] nan [45] nan [46] nan [47] nan [48] nan [49] nan [50]
satellite mapping technology
However, detecting such changes through current Landsat satellite mapping technologies remains challenging, highlighting the need for new mapping methods to aid in future management. [1] Zhaohui Xue 1,* , Sirui Yang 1, Hongyan Zhang 2,3 and Peijun Du 4,5,6 1 School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China 2 State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430072, China 3 Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430072, China 4 Key Laboratory for Satellite Mapping Technology and Applications of National Administration of Surveying, Mapping and Geoinformation of China, Nanjing University, Nanjing 210023, China 5 Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China 6 Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing University, Nanjing 210023, China * Correspondence: zhaohui. [2]그러나 현재 Landsat 위성 매핑 기술을 통해 이러한 변화를 감지하는 것은 여전히 어려운 일이며 향후 관리에 도움이 되는 새로운 매핑 방법의 필요성을 강조합니다. [1] nan [2]