Estimation of Green Water Footprint of Rice Paddies in taitung area using MODIS data

Chih-Da Wu, Tzu Yu Huang, Shih Yuan Lin, Chun Te Lin

Research output: Contribution to conferencePaper

Abstract

Green Water Footprint (GWF) is a recently developed indicator to identify the utilization and availability of the fresh water resource provided for agricultural products. Based on it accounting for rainwater evapotranspiration, a MODIS Global Terrestrial Evapotranspiration Data Set (MOD16) is applied to estimate GWF for its capable presenting global evapotranspiration status with high accuracy, wide coverage and long-term monitoring. Although the MOD16 product offers aforementioned advantages, current drawback is mainly on its data available time is too slow, for which announced data is about one-year late. This paper therefore aims to overcome the drawbacks and develop a regression method considering multiple variables including weather parameters and Normalized Difference Vegetation Index (NDVI) values in order to improve that the current MOD16 cannot reflect a more near real-time GWF. To demonstrate feasibility of the method we proposed, three representative agricultural areas in Taitung County mainly used for rice planting were selected as the studied sites. The analyzing data covered a prolonged period (from 2003 to 2012) and our regression model further distinguished the first and second cropping seasons in the ten years. The results of stepwise regression analysis reported that temperature and NDVI were significant variables related to MOD16. R2 values of models derived from the 1st and 2nd cropping data were 0.65 and 0.64, respectively. The regression models were also verified by 10 years' out-of-samples, and the results indicated that overall accuracy of the prediction was above 85%. Since the modelled evapotranspiration value was reliable, it was then used to compute the rice green water footprint of the two cropping seasons. Based on the model, even without values of the latest MOD16, the GWF of rice producing over the same area in 2013 and 2014 were estimated. The potential of a near real-time estimation of GWF of rice was demonstrated.

Original languageEnglish
Publication statusPublished - 2015 Jan 1
Event36th Asian Conference on Remote Sensing: Fostering Resilient Growth in Asia, ACRS 2015 - Quezon City, Metro Manila, Philippines
Duration: 2015 Oct 242015 Oct 28

Other

Other36th Asian Conference on Remote Sensing: Fostering Resilient Growth in Asia, ACRS 2015
CountryPhilippines
CityQuezon City, Metro Manila
Period15-10-2415-10-28

Fingerprint

Evapotranspiration
Water
Agricultural products
Water resources
Regression analysis
Availability
Monitoring
Temperature

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

Cite this

Wu, C-D., Huang, T. Y., Lin, S. Y., & Lin, C. T. (2015). Estimation of Green Water Footprint of Rice Paddies in taitung area using MODIS data. Paper presented at 36th Asian Conference on Remote Sensing: Fostering Resilient Growth in Asia, ACRS 2015, Quezon City, Metro Manila, Philippines.
Wu, Chih-Da ; Huang, Tzu Yu ; Lin, Shih Yuan ; Lin, Chun Te. / Estimation of Green Water Footprint of Rice Paddies in taitung area using MODIS data. Paper presented at 36th Asian Conference on Remote Sensing: Fostering Resilient Growth in Asia, ACRS 2015, Quezon City, Metro Manila, Philippines.
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Wu, C-D, Huang, TY, Lin, SY & Lin, CT 2015, 'Estimation of Green Water Footprint of Rice Paddies in taitung area using MODIS data' Paper presented at 36th Asian Conference on Remote Sensing: Fostering Resilient Growth in Asia, ACRS 2015, Quezon City, Metro Manila, Philippines, 15-10-24 - 15-10-28, .

Estimation of Green Water Footprint of Rice Paddies in taitung area using MODIS data. / Wu, Chih-Da; Huang, Tzu Yu; Lin, Shih Yuan; Lin, Chun Te.

2015. Paper presented at 36th Asian Conference on Remote Sensing: Fostering Resilient Growth in Asia, ACRS 2015, Quezon City, Metro Manila, Philippines.

Research output: Contribution to conferencePaper

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AU - Wu, Chih-Da

AU - Huang, Tzu Yu

AU - Lin, Shih Yuan

AU - Lin, Chun Te

PY - 2015/1/1

Y1 - 2015/1/1

N2 - Green Water Footprint (GWF) is a recently developed indicator to identify the utilization and availability of the fresh water resource provided for agricultural products. Based on it accounting for rainwater evapotranspiration, a MODIS Global Terrestrial Evapotranspiration Data Set (MOD16) is applied to estimate GWF for its capable presenting global evapotranspiration status with high accuracy, wide coverage and long-term monitoring. Although the MOD16 product offers aforementioned advantages, current drawback is mainly on its data available time is too slow, for which announced data is about one-year late. This paper therefore aims to overcome the drawbacks and develop a regression method considering multiple variables including weather parameters and Normalized Difference Vegetation Index (NDVI) values in order to improve that the current MOD16 cannot reflect a more near real-time GWF. To demonstrate feasibility of the method we proposed, three representative agricultural areas in Taitung County mainly used for rice planting were selected as the studied sites. The analyzing data covered a prolonged period (from 2003 to 2012) and our regression model further distinguished the first and second cropping seasons in the ten years. The results of stepwise regression analysis reported that temperature and NDVI were significant variables related to MOD16. R2 values of models derived from the 1st and 2nd cropping data were 0.65 and 0.64, respectively. The regression models were also verified by 10 years' out-of-samples, and the results indicated that overall accuracy of the prediction was above 85%. Since the modelled evapotranspiration value was reliable, it was then used to compute the rice green water footprint of the two cropping seasons. Based on the model, even without values of the latest MOD16, the GWF of rice producing over the same area in 2013 and 2014 were estimated. The potential of a near real-time estimation of GWF of rice was demonstrated.

AB - Green Water Footprint (GWF) is a recently developed indicator to identify the utilization and availability of the fresh water resource provided for agricultural products. Based on it accounting for rainwater evapotranspiration, a MODIS Global Terrestrial Evapotranspiration Data Set (MOD16) is applied to estimate GWF for its capable presenting global evapotranspiration status with high accuracy, wide coverage and long-term monitoring. Although the MOD16 product offers aforementioned advantages, current drawback is mainly on its data available time is too slow, for which announced data is about one-year late. This paper therefore aims to overcome the drawbacks and develop a regression method considering multiple variables including weather parameters and Normalized Difference Vegetation Index (NDVI) values in order to improve that the current MOD16 cannot reflect a more near real-time GWF. To demonstrate feasibility of the method we proposed, three representative agricultural areas in Taitung County mainly used for rice planting were selected as the studied sites. The analyzing data covered a prolonged period (from 2003 to 2012) and our regression model further distinguished the first and second cropping seasons in the ten years. The results of stepwise regression analysis reported that temperature and NDVI were significant variables related to MOD16. R2 values of models derived from the 1st and 2nd cropping data were 0.65 and 0.64, respectively. The regression models were also verified by 10 years' out-of-samples, and the results indicated that overall accuracy of the prediction was above 85%. Since the modelled evapotranspiration value was reliable, it was then used to compute the rice green water footprint of the two cropping seasons. Based on the model, even without values of the latest MOD16, the GWF of rice producing over the same area in 2013 and 2014 were estimated. The potential of a near real-time estimation of GWF of rice was demonstrated.

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M3 - Paper

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Wu C-D, Huang TY, Lin SY, Lin CT. Estimation of Green Water Footprint of Rice Paddies in taitung area using MODIS data. 2015. Paper presented at 36th Asian Conference on Remote Sensing: Fostering Resilient Growth in Asia, ACRS 2015, Quezon City, Metro Manila, Philippines.