Development of Flood/Drought Monitoring System Using Remote Sensing and Water Hazard Information Platform

2021 ◽  
Vol 17 (6) ◽  
pp. 77-88
Author(s):  
Jin Gyeom Kim ◽  
Yong Hyeon Lee ◽  
Wan Sik Yu ◽  
Eui Ho Hwang
Keyword(s):  
Remote Sensing ◽  
Monitoring System ◽  
Drought Monitoring ◽  
Information Platform ◽  
Water Hazard

Developing Land-surface Evapotranspiration Remote Sensing Monitoring System Using the Component Technology

2011 ◽  
Vol 13 (5) ◽  
pp. 679-686
Author(s):  
Zhiqi QIAN ◽  
Youjing ZHANG ◽  
Shizan DENG ◽  
Yingying FANG ◽  
Chen CHEN
Keyword(s):  
Remote Sensing ◽  
Monitoring System ◽  
Land Surface ◽  
Component Technology ◽  
Remote Sensing Monitoring

Design of Macro-ecological Environment Remote Sensing Monitoring System and the Key Technologies

2011 ◽  
Vol 13 (5) ◽  
pp. 672-678
Author(s):  
Yong WANG ◽  
Dafang ZHUANG ◽  
Xinliang XU ◽  
Dong JIANG
Keyword(s):  
Remote Sensing ◽  
Monitoring System ◽  
Ecological Environment ◽  
Remote Sensing Monitoring ◽  
Key Technologies

Review of Drought Monitoring by Remote Sensing

2012 ◽  
Vol 14 (2) ◽  
pp. 232-239 ◽  
Author(s):  
Huan LIU ◽  
Ronggao LIU ◽  
Shiyang LIU
Keyword(s):  
Remote Sensing ◽  
Drought Monitoring

scholarly journals Integration of Remote Sensing and Mexican Water Quality Monitoring System Using an Extreme Learning Machine

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4118
Author(s):  
Leonardo F. Arias-Rodriguez ◽  
Zheng Duan ◽  
José de Jesús Díaz-Torres ◽  
Mónica Basilio Hazas ◽  
Jingshui Huang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Extreme Learning Machine ◽  
Monitoring System ◽  
Field Measurements ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Chl A ◽  
Learning Machine ◽  
Level 2

Remote Sensing, as a driver for water management decisions, needs further integration with monitoring water quality programs, especially in developing countries. Moreover, usage of remote sensing approaches has not been broadly applied in monitoring routines. Therefore, it is necessary to assess the efficacy of available sensors to complement the often limited field measurements from such programs and build models that support monitoring tasks. Here, we integrate field measurements (2013–2019) from the Mexican national water quality monitoring system (RNMCA) with data from Landsat-8 OLI, Sentinel-3 OLCI, and Sentinel-2 MSI to train an extreme learning machine (ELM), a support vector regression (SVR) and a linear regression (LR) for estimating Chlorophyll-a (Chl-a), Turbidity, Total Suspended Matter (TSM) and Secchi Disk Depth (SDD). Additionally, OLCI Level-2 Products for Chl-a and TSM are compared against the RNMCA data. We observed that OLCI Level-2 Products are poorly correlated with the RNMCA data and it is not feasible to rely only on them to support monitoring operations. However, OLCI atmospherically corrected data is useful to develop accurate models using an ELM, particularly for Turbidity (R2=0.7). We conclude that remote sensing is useful to support monitoring systems tasks, and its progressive integration will improve the quality of water quality monitoring programs.

Drought monitoring in North China using HJ-1 satellite remote sensing data

2009 ◽  
Author(s):  
Bingfeng Yang ◽  
Qiao Wang ◽  
Changzuo Wang ◽  
Huawei Wan ◽  
Yipeng Yang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
North China ◽  
Remote Sensing Data ◽  
Drought Monitoring ◽  
Sensing Data
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