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Quantifying On-Farm Reservoir Dynamics : = An Earth Observation and Hydrological Modeling Approach.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Quantifying On-Farm Reservoir Dynamics :/
其他題名:	An Earth Observation and Hydrological Modeling Approach.
作者:	Perin, Vinicius.
面頁冊數:	1 online resource (227 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-06, Section: A.
Contained By:	Dissertations Abstracts International84-06A.
標題:	Agriculture. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30167941click for full text (PQDT)
ISBN:	9798358422902

Quantifying On-Farm Reservoir Dynamics : = An Earth Observation and Hydrological Modeling Approach.
Perin, Vinicius.

Quantifying On-Farm Reservoir Dynamics :An Earth Observation and Hydrological Modeling Approach. - 1 online resource (227 pages)

Source: Dissertations Abstracts International, Volume: 84-06, Section: A.

Thesis (Ph.D.)--North Carolina State University, 2022.

Includes bibliographical references

On-farm reservoirs (OFRs) are artificial water bodies that enable farmers to store water during the raining season to support irrigation activities during the crop growing season. Although the OFRs have a critical role in irrigated food production, these water bodies impact the hydrology of the watersheds where they occur by decreasing peak flow and overall streamflow. Nonetheless, OFRs are poorly monitored or not monitored in many countries, due to the OFRs dynamic nature, their occurrence in high numbers (i.e., thousands), and their location mostly on private properties. Aiming to improve OFRs monitoring, and to quantify their impact on surface water availability, this dissertation explores various remote sensing technologies, including novel approaches and imagery datasets, in combination with hydrological modeling to further understand the OFRs occurrence and surface area changes. The study region is in eastern Arkansas, the third most irrigated region in the USA with a high occurrence of OFRs.Chapter 2 explores the use of Landsat-based inundation datasets, the U.S. Geological Survey Dynamic Surface Water Extent (DSWE) and the European Commission's Joint Research Centre (JRC) Global Monthly Water History, to assess the OFRs surface area changes. This study aimed to compare the performance of both datasets when monitoring OFRs of different sizes. Our results showed that both datasets allow us to estimate the seasonality of the OFRs surface area changes, their frequency of inundation, and their maximum extent. Nonetheless, these datasets are limited to a few observations a year, due to sensor related issues and the 16-day repeat cycle, and the spatial resolution (30 m) of the datasets is unsuitable to monitor OFRs smaller than 5 ha.Chapter 3 presents a multi sensor satellite imagery approach based on the Kalman filter to improve OFRs monitoring. The algorithm was used to assimilate data from freely available (i.e., Sentinel 1 and 2) and commercial (i.e., PlanetScope and RapidEye) satellite imagery to monitor the OFRs sub-weekly surface area changes. Given that all sensors tended to underestimate the OFRs surface area changes, the Kalman filter approach also underestimated the OFRs surface area; however, the mean percent error was smaller than 12%. The algorithm allows monitoring the spatial and temporal variability of a network of OFRs, and it can be used to monitor water use trends. Results of this work can be employed to improve water management recommendations to increase water use efficiency.Chapter 4 explores the use of Planet Fusion and Planet Basemap-the next generation of daily high-resolution (3 m) analysis ready datasets-to monitor OFRs. These datasets offer an unprecedented opportunity to improve OFRs monitoring. Although both datasets are based on PlanetScope imagery, they are generated using different algorithms and data sources. In general, Planet Basemap presented higher surface area variability and it was more susceptible to the presence of clouds and haze when compared to Planet fusion, which had a smoother time series with less variability and fewer abrupt changes in the time series. Both datasets can help improve fresh water management by allowing better assessment of the OFRs dynamics.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798358422902Subjects--Topical Terms:

518588
Agriculture.
Index Terms--Genre/Form:

542853
Electronic books.

Quantifying On-Farm Reservoir Dynamics : = An Earth Observation and Hydrological Modeling Approach.
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504 $a Includes bibliographical references
520 $a On-farm reservoirs (OFRs) are artificial water bodies that enable farmers to store water during the raining season to support irrigation activities during the crop growing season. Although the OFRs have a critical role in irrigated food production, these water bodies impact the hydrology of the watersheds where they occur by decreasing peak flow and overall streamflow. Nonetheless, OFRs are poorly monitored or not monitored in many countries, due to the OFRs dynamic nature, their occurrence in high numbers (i.e., thousands), and their location mostly on private properties. Aiming to improve OFRs monitoring, and to quantify their impact on surface water availability, this dissertation explores various remote sensing technologies, including novel approaches and imagery datasets, in combination with hydrological modeling to further understand the OFRs occurrence and surface area changes. The study region is in eastern Arkansas, the third most irrigated region in the USA with a high occurrence of OFRs.Chapter 2 explores the use of Landsat-based inundation datasets, the U.S. Geological Survey Dynamic Surface Water Extent (DSWE) and the European Commission's Joint Research Centre (JRC) Global Monthly Water History, to assess the OFRs surface area changes. This study aimed to compare the performance of both datasets when monitoring OFRs of different sizes. Our results showed that both datasets allow us to estimate the seasonality of the OFRs surface area changes, their frequency of inundation, and their maximum extent. Nonetheless, these datasets are limited to a few observations a year, due to sensor related issues and the 16-day repeat cycle, and the spatial resolution (30 m) of the datasets is unsuitable to monitor OFRs smaller than 5 ha.Chapter 3 presents a multi sensor satellite imagery approach based on the Kalman filter to improve OFRs monitoring. The algorithm was used to assimilate data from freely available (i.e., Sentinel 1 and 2) and commercial (i.e., PlanetScope and RapidEye) satellite imagery to monitor the OFRs sub-weekly surface area changes. Given that all sensors tended to underestimate the OFRs surface area changes, the Kalman filter approach also underestimated the OFRs surface area; however, the mean percent error was smaller than 12%. The algorithm allows monitoring the spatial and temporal variability of a network of OFRs, and it can be used to monitor water use trends. Results of this work can be employed to improve water management recommendations to increase water use efficiency.Chapter 4 explores the use of Planet Fusion and Planet Basemap-the next generation of daily high-resolution (3 m) analysis ready datasets-to monitor OFRs. These datasets offer an unprecedented opportunity to improve OFRs monitoring. Although both datasets are based on PlanetScope imagery, they are generated using different algorithms and data sources. In general, Planet Basemap presented higher surface area variability and it was more susceptible to the presence of clouds and haze when compared to Planet fusion, which had a smoother time series with less variability and fewer abrupt changes in the time series. Both datasets can help improve fresh water management by allowing better assessment of the OFRs dynamics.
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