High-resolution Surface Relative Humidity Computation Using MODIS Image in Peninsular Malaysia

PENG Guangxiong; LI Jing; CHEN Yunhao; Abdul Patah NORIZAN; Liphong TAY

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High-resolution Surface Relative Humidity Computation Using MODIS Image in Peninsular Malaysia

PENG Guangxiong, LI Jing, CHEN Yunhao, Abdul Patah NORIZAN, Liphong TAY

文章导航 > Chinese Geographical Science > 2006 > 16(3): 260-264

PENG Guangxiong, LI Jing, CHEN Yunhao, Abdul Patah NORIZAN, Liphong TAY. High-resolution Surface Relative Humidity Computation Using MODIS Image in Peninsular Malaysia[J]. 中国地理科学, 2006, 16(3): 260-264.

引用本文:

Citation:

High-resolution Surface Relative Humidity Computation Using MODIS Image in Peninsular Malaysia

1.
College of Resources Science & Technology, Beijing Normal University, Beijing 100875, China;
2.
Malaysian Centre for Remote Sensing, Kuala Lumpur 50480, Malaysia;
3.
Technology Park Malaysia, Kuala Lumpur 57000, Malaysia

基金项目: Under the auspices of the Airborne Remote Sensing(MARS) Program of Malaysia(No.KSTAS/MACRES/T/2/2004)

详细信息

作者简介:
PENG Guangxiong(1978- ),male,a native of Yongzhou of Hunan Province,Ph.D.candidate,specialized in environmental change and application of remote sensing.E-mail:pgx457600@gmail.com.

High-resolution Surface Relative Humidity Computation Using MODIS Image in Peninsular Malaysia

1.
College of Resources Science & Technology, Beijing Normal University, Beijing 100875, China;
2.
Malaysian Centre for Remote Sensing, Kuala Lumpur 50480, Malaysia;
3.
Technology Park Malaysia, Kuala Lumpur 57000, Malaysia

摘要: Forest fire is a serious disaster all over the world. The Fire Weather Index (FWI) System can be used in ap- plied forestry as a tool to investigate and manage all types of fire. Relative humidity (RH) is a very important parameter to calculate FWI. However, RH interpolated from meteorological data may not be able to provide precise and confident values for areas between far separated stations. The principal objective of this study is to provide high-resolution RH for FWI using MODIS data. The precipitable water vapor (PW) can be retrieved from MODIS using split window techniques. Four-year-time-series (2000-2003) of 8-day mean PW and specific humidity (Q) of Peninsular Malaysia were analyzed and the statistic expression between PW and Q was developed. The root-mean-square-error (RMSE) of Q estimated by PW is generally less than 0.0004 and the correlation coefficient is 0.90. Based on the experiential formula between PW and Q, surface RH can be computed with combination of auxiliary data such as DEM and air temperature (T_a). The mean absolute errors of the estimated RH in Peninsular Malaysia are less than 5% compared to the measured RH and the correlation coefficient is 0.8219. It is proven to be a simple and feasible model to compute high-resolution RH using remote sensing data.
- relative humidity /
- precipitable water vapor /
- specific humidity /
- MODIS
Abstract: Forest fire is a serious disaster all over the world. The Fire Weather Index (FWI) System can be used in ap- plied forestry as a tool to investigate and manage all types of fire. Relative humidity (RH) is a very important parameter to calculate FWI. However, RH interpolated from meteorological data may not be able to provide precise and confident values for areas between far separated stations. The principal objective of this study is to provide high-resolution RH for FWI using MODIS data. The precipitable water vapor (PW) can be retrieved from MODIS using split window techniques. Four-year-time-series (2000-2003) of 8-day mean PW and specific humidity (Q) of Peninsular Malaysia were analyzed and the statistic expression between PW and Q was developed. The root-mean-square-error (RMSE) of Q estimated by PW is generally less than 0.0004 and the correlation coefficient is 0.90. Based on the experiential formula between PW and Q, surface RH can be computed with combination of auxiliary data such as DEM and air temperature (T_a). The mean absolute errors of the estimated RH in Peninsular Malaysia are less than 5% compared to the measured RH and the correlation coefficient is 0.8219. It is proven to be a simple and feasible model to compute high-resolution RH using remote sensing data.
- relative humidity /
- precipitable water vapor /
- specific humidity /
- MODIS

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High-resolution Surface Relative Humidity Computation Using MODIS Image in Peninsular Malaysia

1. College of Resources Science & Technology, Beijing Normal University, Beijing 100875, China;

2. Malaysian Centre for Remote Sensing, Kuala Lumpur 50480, Malaysia;

3. Technology Park Malaysia, Kuala Lumpur 57000, Malaysia

基金项目: Under the auspices of the Airborne Remote Sensing(MARS) Program of Malaysia(No.KSTAS/MACRES/T/2/2004)

作者简介:
PENG Guangxiong(1978- ),male,a native of Yongzhou of Hunan Province,Ph.D.candidate,specialized in environmental change and application of remote sensing.E-mail:pgx457600@gmail.com.

收稿日期: 2006-05-04
修回日期: 2006-07-17
刊出日期: 2006-09-20

关键词:

relative humidity /

precipitable water vapor /

specific humidity /

MODIS

摘要: Forest fire is a serious disaster all over the world. The Fire Weather Index (FWI) System can be used in ap- plied forestry as a tool to investigate and manage all types of fire. Relative humidity (RH) is a very important parameter to calculate FWI. However, RH interpolated from meteorological data may not be able to provide precise and confident values for areas between far separated stations. The principal objective of this study is to provide high-resolution RH for FWI using MODIS data. The precipitable water vapor (PW) can be retrieved from MODIS using split window techniques. Four-year-time-series (2000-2003) of 8-day mean PW and specific humidity (Q) of Peninsular Malaysia were analyzed and the statistic expression between PW and Q was developed. The root-mean-square-error (RMSE) of Q estimated by PW is generally less than 0.0004 and the correlation coefficient is 0.90. Based on the experiential formula between PW and Q, surface RH can be computed with combination of auxiliary data such as DEM and air temperature (T_a). The mean absolute errors of the estimated RH in Peninsular Malaysia are less than 5% compared to the measured RH and the correlation coefficient is 0.8219. It is proven to be a simple and feasible model to compute high-resolution RH using remote sensing data.

English Abstract

High-resolution Surface Relative Humidity Computation Using MODIS Image in Peninsular Malaysia

1. College of Resources Science & Technology, Beijing Normal University, Beijing 100875, China;

2. Malaysian Centre for Remote Sensing, Kuala Lumpur 50480, Malaysia;

3. Technology Park Malaysia, Kuala Lumpur 57000, Malaysia

Received Date: 2006-05-04
Rev Recd Date: 2006-07-17
Publish Date: 2006-09-20

Keywords:

Abstract: Forest fire is a serious disaster all over the world. The Fire Weather Index (FWI) System can be used in ap- plied forestry as a tool to investigate and manage all types of fire. Relative humidity (RH) is a very important parameter to calculate FWI. However, RH interpolated from meteorological data may not be able to provide precise and confident values for areas between far separated stations. The principal objective of this study is to provide high-resolution RH for FWI using MODIS data. The precipitable water vapor (PW) can be retrieved from MODIS using split window techniques. Four-year-time-series (2000-2003) of 8-day mean PW and specific humidity (Q) of Peninsular Malaysia were analyzed and the statistic expression between PW and Q was developed. The root-mean-square-error (RMSE) of Q estimated by PW is generally less than 0.0004 and the correlation coefficient is 0.90. Based on the experiential formula between PW and Q, surface RH can be computed with combination of auxiliary data such as DEM and air temperature (T_a). The mean absolute errors of the estimated RH in Peninsular Malaysia are less than 5% compared to the measured RH and the correlation coefficient is 0.8219. It is proven to be a simple and feasible model to compute high-resolution RH using remote sensing data.

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High-resolution Surface Relative Humidity Computation Using MODIS Image in Peninsular Malaysia

作者简介: PENG Guangxiong(1978- ),male,a native of Yongzhou of Hunan Province,Ph.D.candidate,specialized in environmental change and application of remote sensing.E-mail:pgx457600@gmail.com.

High-resolution Surface Relative Humidity Computation Using MODIS Image in Peninsular Malaysia

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出版历程

High-resolution Surface Relative Humidity Computation Using MODIS Image in Peninsular Malaysia

1. College of Resources Science & Technology, Beijing Normal University, Beijing 100875, China; 2. Malaysian Centre for Remote Sensing, Kuala Lumpur 50480, Malaysia; 3. Technology Park Malaysia, Kuala Lumpur 57000, Malaysia

作者简介: PENG Guangxiong(1978- ),male,a native of Yongzhou of Hunan Province,Ph.D.candidate,specialized in environmental change and application of remote sensing.E-mail:pgx457600@gmail.com.

English Abstract

High-resolution Surface Relative Humidity Computation Using MODIS Image in Peninsular Malaysia

1. College of Resources Science & Technology, Beijing Normal University, Beijing 100875, China; 2. Malaysian Centre for Remote Sensing, Kuala Lumpur 50480, Malaysia; 3. Technology Park Malaysia, Kuala Lumpur 57000, Malaysia

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目录

作者简介:
PENG Guangxiong(1978- ),male,a native of Yongzhou of Hunan Province,Ph.D.candidate,specialized in environmental change and application of remote sensing.E-mail:pgx457600@gmail.com.

1. College of Resources Science & Technology, Beijing Normal University, Beijing 100875, China;

2. Malaysian Centre for Remote Sensing, Kuala Lumpur 50480, Malaysia;

3. Technology Park Malaysia, Kuala Lumpur 57000, Malaysia

作者简介:
PENG Guangxiong(1978- ),male,a native of Yongzhou of Hunan Province,Ph.D.candidate,specialized in environmental change and application of remote sensing.E-mail:pgx457600@gmail.com.

1. College of Resources Science & Technology, Beijing Normal University, Beijing 100875, China;

2. Malaysian Centre for Remote Sensing, Kuala Lumpur 50480, Malaysia;

3. Technology Park Malaysia, Kuala Lumpur 57000, Malaysia