Estimation of daily maximum and minimum air temperatures in urban landscapes using MODIS time series satellite data

Yoo, Cheolhee; Im, Jungho; Park, Seonyoung; Quackenbush, Lindi J.

doi:10.1016/j.isprsjprs.2018.01.018

Cited by 131 publications

(98 citation statements)

References 82 publications

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“…RF generates a multitude of CARTs (typically 500–1000 trees) through bootstrapping‐based randomization approaches for the selection of both training samples for a tree and predictors at each node of the tree (Im et al, 2016; Richardson et al, 2017). This approach alleviates any existing problems in the CART such as overfitting and sensitivity to training samples (Forkuor et al, 2018; Yoo et al, 2018). R software with the ‘randomForest’ package was used to develop and apply the statistical models using default model parameter settings (Ho et al, 2014; Yoo et al, 2019).…”

Section: Methodsmentioning

confidence: 99%

Comparative Assessment of Various Machine Learning‐Based Bias Correction Methods for Numerical Weather Prediction Model Forecasts of Extreme Air Temperatures in Urban Areas

Cho

Yoo

et al. 2020

Earth and Space Science

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123

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Forecasts of maximum and minimum air temperatures are essential to mitigate the damage of extreme weather events such as heat waves and tropical nights. The Numerical Weather Prediction (NWP) model has been widely used for forecasting air temperature, but generally it has a systematic bias due to its coarse grid resolution and lack of parametrizations. This study used random forest (RF), support vector regression (SVR), artificial neural network (ANN) and a multi-model ensemble (MME) to correct the Local Data Assimilation and Prediction System (LDAPS; a local NWP model over Korea) model outputs of next-day maximum and minimum air temperatures (T maxtþ1 and T mintþ1 ) in Seoul, South Korea. A total of 14 LDAPS model forecast data, the daily maximum and minimum air temperatures of in-situ observations, and five auxiliary data were used as input variables. The results showed that the LDAPS model had an R 2 of 0.69, a bias of −0.85°C and an RMSE of 2.08°C for T maxtþ1 forecast, whereas the proposed models resulted in the improvement with R 2 from 0.75 to 0.78, bias from −0.16 to −0.07°C and RMSE from 1.55 to 1.66°C by hindcast validation. For forecasting T mintþ1 , the LDAPS model had an R 2 of 0.77, a bias of 0.51°C and an RMSE of 1.43°C by hindcast, while the bias correction models showed R 2 values ranging from 0.86 to 0.87, biases from −0.03 to 0.03°C, and RMSEs from 0.98 to 1.02°C. The MME model had better generalization performance than the three single machine learning models by hindcast validation and leave-one-station-out cross-validation.

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Section: Methodsmentioning

confidence: 99%

Comparative Assessment of Various Machine Learning‐Based Bias Correction Methods for Numerical Weather Prediction Model Forecasts of Extreme Air Temperatures in Urban Areas

Cho

Yoo

et al. 2020

Earth and Space Science

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123

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“…Machine learning approaches have been widely used in various remote-sensing studies thanks to their flexibility with classification and regression (Im et al, 2009;Lu et al, 2011a, Liu et al, 2015Ke et al, 2016;Pham et al, 2017;Forkuor et al, 2018). In particular, random forest (RF) has proved to be useful for remote-sensing-based regression tasks (Yoo et al, 2012(Yoo et al, , 2018Jang et al, 2017;Richardson et al, 2017). To estimate daily PM 2.5 concentrations over the United States, Hu et al (2017b) incorporated MODIS AOD, simulated GEOS-Chem AOD, meteorological data, and land use information in an RF model.…”

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confidence: 99%

Estimation of ground-level particulate matter concentrations through the synergistic use of satellite observations and process-based models over South Korea

Park

Shin

et al. 2019

Atmos. Chem. Phys.

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Abstract. Long-term exposure to particulate matter (PM) with aerodynamic diameters < 10 (PM10) and 2.5 µm (PM2.5) has negative effects on human health. Although station-based PM monitoring has been conducted around the world, it is still challenging to provide spatially continuous PM information for vast areas at high spatial resolution. Satellite-derived aerosol information such as aerosol optical depth (AOD) has been frequently used to investigate ground-level PM concentrations. In this study, we combined multiple satellite-derived products including AOD with model-based meteorological parameters (i.e., dew-point temperature, wind speed, surface pressure, planetary boundary layer height, and relative humidity) and emission parameters (i.e., NO, NH3, SO2, primary organic aerosol (POA), and HCHO) to estimate surface PM concentrations over South Korea. Random forest (RF) machine learning was used to estimate both PM10 and PM2.5 concentrations with a total of 32 parameters for 2015–2016. The results show that the RF-based models produced good performance resulting in R2 values of 0.78 and 0.73 and root mean square errors (RMSEs) of 17.08 and 8.25 µg m−3 for PM10 and PM2.5, respectively. In particular, the proposed models successfully estimated high PM concentrations. AOD was identified as the most significant for estimating ground-level PM concentrations, followed by wind speed, solar radiation, and dew-point temperature. The use of aerosol information derived from a geostationary satellite sensor (i.e., Geostationary Ocean Color Imager, GOCI) resulted in slightly higher accuracy for estimating PM concentrations than that from a polar-orbiting sensor system (i.e., the Moderate Resolution Imaging Spectroradiometer, MODIS). The proposed RF models yielded better performance than the process-based approaches, particularly in improving on the underestimation of the process-based models (i.e., GEOS-Chem and the Community Multiscale Air Quality Modeling System, CMAQ).

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“…RF is based on Classification and Regression Tree (CART) methodology [40], which is a rule-based decision tree. RF adopts two randomization strategies to produce many independent CARTs: a random selection of training samples for each tree, and a random selection of input variables at each node of a tree [41][42][43]. Final output from RF is achieved through an ensemble of individual CARTs.…”

Section: Random Forestmentioning

confidence: 99%

Detection and Monitoring of Forest Fires Using Himawari-8 Geostationary Satellite Data in South Korea

Jang

Kang

et al. 2019

Remote Sensing

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Geostationary satellite remote sensing systems are a useful tool for forest fire detection and monitoring because of their high temporal resolution over large areas. In this study, we propose a combined 3-step forest fire detection algorithm (i.e., thresholding, machine learning-based modeling, and post processing) using Himawari-8 geostationary satellite data over South Korea. This threshold-based algorithm filtered the forest fire candidate pixels using adaptive threshold values considering the diurnal cycle and seasonality of forest fires while allowing a high rate of false alarms. The random forest (RF) machine learning model then effectively removed the false alarms from the results of the threshold-based algorithm (overall accuracy~99.16%, probability of detection (POD) 93.08%, probability of false detection (POFD)~0.07%, and 96% reduction of the false alarmed pixels for validation), and the remaining false alarms were removed through post-processing using the forest map. The proposed algorithm was compared to the two existing methods. The proposed algorithm (POD~93%) successfully detected most forest fires, while the others missed many small-scale forest fires (POD~50-60%). More than half of the detected forest fires were detected within 10 min, which is a promising result when the operational real-time monitoring of forest fires using more advanced geostationary satellite sensor data (i.e., with higher spatial and temporal resolutions) is used for rapid response and management of forest fires.

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Estimation of daily maximum and minimum air temperatures in urban landscapes using MODIS time series satellite data

Cited by 131 publications

References 82 publications

Comparative Assessment of Various Machine Learning‐Based Bias Correction Methods for Numerical Weather Prediction Model Forecasts of Extreme Air Temperatures in Urban Areas

Comparative Assessment of Various Machine Learning‐Based Bias Correction Methods for Numerical Weather Prediction Model Forecasts of Extreme Air Temperatures in Urban Areas

Estimation of ground-level particulate matter concentrations through the synergistic use of satellite observations and process-based models over South Korea

Detection and Monitoring of Forest Fires Using Himawari-8 Geostationary Satellite Data in South Korea

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