Drought monitoring using high resolution soil moisture through multi-sensor satellite data fusion over the Korean peninsula

Park, Seonyoung; Im, Jungho; Park, Sumin; Rhee, Jinyoung

doi:10.1016/j.agrformet.2017.02.022

Cited by 127 publications

(57 citation statements)

References 77 publications

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“…Final output from RF is achieved through an ensemble of individual CARTs. This ensemble approach can mitigate overfitting and the sensitivity to training data configurations, which are major limitations of CART [44][45][46]. Using many independent decision trees, RF makes a final decision by (weighted) averaging and majority voting approaches for regression and classification, respectively.…”

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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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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“…ERT was implemented using the add-on package named "ExtraTrees" in R with default parameters, but does not provide variable importance measures. The tree-based machine learning techniques described above have been widely used in various remote sensing classification and regression applications [38][39][40][41][42][43][44][45][46][47][48][49][50]. Both methods can produce the matrix of class probabilities ranging from 0 to 1 in R. The matrix is calculated as the proportion of vote counts of the trees for each class [51].…”

Section: Tree-based Ensemble Models: Random Forest and Extremely Randmentioning

confidence: 99%

Detection of Tropical Overshooting Cloud Tops Using Himawari-8 Imagery

Kim

Park

et al. 2017

Remote Sensing

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Overshooting convective cloud Top (OT)-accompanied clouds can cause severe weather conditions, such as lightning, strong winds, and heavy rainfall. The distribution and behavior of OTs can affect regional and global climate systems. In this paper, we propose a new approach for OT detection by using machine learning methods with multiple infrared images and their derived features. Himawari-8 satellite images were used as the main input data, and binary detection (OT or nonOT) with class probability was the output of the machine learning models. Three machine learning techniques-random forest (RF), extremely randomized trees (ERT), and logistic regression (LR)-were used to develop OT classification models to distinguish OT from non-OT. The hindcast validation over the Southeast Asia and West Pacific regions showed that RF performed best, resulting in a mean probabilities of detection (POD) of 77.06% and a mean false alarm ratio (FAR) of 36.13%. Brightness temperature at 11.2 µm (Tb11) and its standard deviation (STD) in a 3 × 3 window size were identified as the most contributing variables for discriminating OT and nonOT classes. The proposed machine learning-based OT detection algorithms produced promising results comparable to or even better than the existing approaches, which are the infrared window (IRW)-texture and water vapor (WV) minus IRW brightness temperature difference (BTD) methods.

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“…Cubist regression trees generate the optimum We adopted a modified regression tree from Cubist after considering the performance and operational use of the approach based on our previous study [32]. Although random forest proved to be very robust in many remote sensing applications [82][83][84][85] and produced slightly better performance in Im et al [32], it requires a much longer processing time than a modified regression tree, i.e., Cubist, which is not appropriate for operational use. Cubist regression trees developed by RuleQuest Research have been widely used in the remote sensing field [32,49,[86][87][88].…”

Section: Methodsmentioning

confidence: 99%

Downscaling GLDAS Soil Moisture Data in East Asia through Fusion of Multi-Sensors by Optimizing Modified Regression Trees

Park

et al. 2017

Water

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Soil moisture is a key part of Earth's climate systems, including agricultural and hydrological cycles. Soil moisture data from satellite and numerical models is typically provided at a global scale with coarse spatial resolution, which is not enough for local and regional applications. In this study, a soil moisture downscaling model was developed using satellite-derived variables targeting Global Land Data Assimilation System (GLDAS) soil moisture as a reference dataset in East Asia based on the optimization of a modified regression tree. A total of six variables, Advanced Microwave Scanning Radiometer 2 (AMSR2) and Advanced SCATterometer (ASCAT) soil moisture products, Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), and MODerate resolution Imaging Spectroradiometer (MODIS) products, including Land Surface Temperature, Normalized Difference Vegetation Index, and land cover, were used as input variables. The optimization was conducted through a pruning approach for operational use, and finally 59 rules were extracted based on root mean square errors (RMSEs) and correlation coefficients (r). The developed downscaling model showed a good modeling performance (r = 0.79, RMSE = 0.056 m 3 ·m −3 , and slope = 0.74). The 1 km downscaled soil moisture showed similar time series patterns with both GLDAS and ground soil moisture and good correlation with ground soil moisture (average r = 0.47, average RMSD = 0.038 m 3 ·m −3 ) at 14 ground stations. The spatial distribution of 1 km downscaled soil moisture reflected seasonal and regional characteristics well, although the model did not result in good performance over a few areas such as Southern China due to very high cloud cover rates. The results of this study are expected to be helpful in operational use to monitor soil moisture throughout East Asia since the downscaling model produces daily high resolution (1 km) real time soil moisture with a low computational demand. This study yielded a promising result to operationally produce daily high resolution soil moisture data from multiple satellite sources, although there are yet several limitations. In future research, more variables including Global Precipitation Measurement (GPM) precipitation, Soil Moisture Active Passive (SMAP) soil moisture, and other vegetation indices will be integrated to improve the performance of the proposed soil moisture downscaling model.

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Drought monitoring using high resolution soil moisture through multi-sensor satellite data fusion over the Korean peninsula

Cited by 127 publications

References 77 publications

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

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

Detection of Tropical Overshooting Cloud Tops Using Himawari-8 Imagery

Downscaling GLDAS Soil Moisture Data in East Asia through Fusion of Multi-Sensors by Optimizing Modified Regression Trees

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