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A Comparison of Different Regression Algorithms for Downscaling Monthly Satellite-Based Precipitation over North China

Environmental monitoring of Earth from space has provided invaluable information for understanding land-atmosphere water and energy exchanges. However, the use of satellite-based precipitation observations in hydrologic and environmental applications is often limited by their coarse spatial resoluti... Full description

Journal Title: Remote Sensing 0, 2016, Vol.8(10), p.835
Main Author: Jing, Wenlong
Other Authors: Yang, Yaping , Yue, Xiafang , Zhao, Xiaodan
Format: Electronic Article Electronic Article
Language: English
Subjects:
ID: E-ISSN: 2072-4292 ; DOI: 10.3390/rs8100835
Link: http://search.proquest.com/docview/1855380463/?pq-origsite=primo
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recordid: proquest1855380463
title: A Comparison of Different Regression Algorithms for Downscaling Monthly Satellite-Based Precipitation over North China
format: Article
creator:
  • Jing, Wenlong
  • Yang, Yaping
  • Yue, Xiafang
  • Zhao, Xiaodan
subjects:
  • Mathematical Models
  • Land Surface Temperature
  • Regression
  • Algorithms
  • Support Vector Machines
  • Accuracy
  • Digital Elevation Models
  • Errors
  • Satellite Communications, Topography, and Cartography (CE)
  • Earth Resources and Remote Sensing (Ah)
ispartof: Remote Sensing, 0, 2016, Vol.8(10), p.835
description: Environmental monitoring of Earth from space has provided invaluable information for understanding land-atmosphere water and energy exchanges. However, the use of satellite-based precipitation observations in hydrologic and environmental applications is often limited by their coarse spatial resolutions. In this study, we propose a downscaling approach based on precipitation-land surface characteristics. Daytime land surface temperature, nighttime land surface temperature, and day-night land surface temperature differences were introduced as variables in addition to the Normalized Difference Vegetation Index (NDVI), the Digital Elevation Model (DEM), and geolocation (longitude, latitude). Four machine learning regression algorithms, the classification and regression tree (CART), the k-nearest neighbors (k-NN), the support vector machine (SVM), and random forests (RF), were implemented to downscale monthly TRMM 3B43 V7 precipitation data from 25 km to 1 km over North...
language: eng
source:
identifier: E-ISSN: 2072-4292 ; DOI: 10.3390/rs8100835
fulltext: fulltext
issn:
  • 20724292
  • 2072-4292
url: Link


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subjectMathematical Models ; Land Surface Temperature ; Regression ; Algorithms ; Support Vector Machines ; Accuracy ; Digital Elevation Models ; Errors ; Satellite Communications, Topography, and Cartography (CE) ; Earth Resources and Remote Sensing (Ah)
descriptionEnvironmental monitoring of Earth from space has provided invaluable information for understanding land-atmosphere water and energy exchanges. However, the use of satellite-based precipitation observations in hydrologic and environmental applications is often limited by their coarse spatial resolutions. In this study, we propose a downscaling approach based on precipitation-land surface characteristics. Daytime land surface temperature, nighttime land surface temperature, and day-night land surface temperature differences were introduced as variables in addition to the Normalized Difference Vegetation Index (NDVI), the Digital Elevation Model (DEM), and geolocation (longitude, latitude). Four machine learning regression algorithms, the classification and regression tree (CART), the k-nearest neighbors (k-NN), the support vector machine (SVM), and random forests (RF), were implemented to downscale monthly TRMM 3B43 V7 precipitation data from 25 km to 1 km over North...
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descriptionEnvironmental monitoring of Earth from space has provided invaluable information for understanding land-atmosphere water and energy exchanges. However, the use of satellite-based precipitation observations in hydrologic and environmental applications is often limited by their coarse spatial resolutions. In this study, we propose a downscaling approach based on precipitation-land surface characteristics. Daytime land surface temperature, nighttime land surface temperature, and day-night land surface temperature differences were introduced as variables in addition to the Normalized Difference Vegetation Index (NDVI), the Digital Elevation Model (DEM), and geolocation (longitude, latitude). Four machine learning regression algorithms, the classification and regression tree (CART), the k-nearest neighbors (k-NN), the support vector machine (SVM), and random forests (RF), were implemented to downscale monthly TRMM 3B43 V7 precipitation data from 25 km to 1 km over North...
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abstractEnvironmental monitoring of Earth from space has provided invaluable information for understanding land-atmosphere water and energy exchanges. However, the use of satellite-based precipitation observations in hydrologic and environmental applications is often limited by their coarse spatial resolutions. In this study, we propose a downscaling approach based on precipitation-land surface characteristics. Daytime land surface temperature, nighttime land surface temperature, and day-night land surface temperature differences were introduced as variables in addition to the Normalized Difference Vegetation Index (NDVI), the Digital Elevation Model (DEM), and geolocation (longitude, latitude). Four machine learning regression algorithms, the classification and regression tree (CART), the k-nearest neighbors (k-NN), the support vector machine (SVM), and random forests (RF), were implemented to downscale monthly TRMM 3B43 V7 precipitation data from 25 km to 1 km over North...
doi10.3390/rs8100835
urlhttp://search.proquest.com/docview/1855380463/
date2016-01-01