A multi-sensor satellite imagery approach to monitor on-farm reservoirs

Authors: PERIN, VINICIUS - North Carolina State University; TULBURE, MIRELA - North Carolina State University; REICH, BRIAN - North Carolina State University; GAINES, MOLLIE - North Carolina State University; Reba, Michele; YAEGER, MARY - University Of Memphis

Submitted to: Remote Sensing of Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/6/2021
Publication Date: 11/19/2021
Citation: Perin, V., Tulbure, M., Reich, B., Gaines, M.D., Reba, M.L., Yaeger, M. 2021. A multi-sensor satellite imagery approach to monitor on-farm reservoirs. Remote Sensing of Environment. 270:112796. https://doi.org/10.1016/j.rse.2021.112796.
DOI: https://doi.org/10.1016/j.rse.2021.112796

Interpretive Summary: On-farm reservoirs are important to help farmers irrigate their crops. Farmers use these reservoirs to store water during the rainy season for crop irrigation during the dry season. Despite their importance, these reservoirs can impact river discharge where they are built, and because of that, we need to understand how the water stored in these reservoirs varies throughout the year, with the aim to support more efficient management of reservoirs and mitigation of their impacts. In this study, we developed a method that uses more than a million satellite images and combines data from several satellites to monitor 731 reservoirs in eastern Arkansas. With the new method, scientists, water authorities, and policymakers can now monitor the reservoirs’ sub-weekly water storage changes. Therefore, the method has the potential to enhance water conservation plans by allowing better assessment and management of water quantity. This study is relevant in the context of climate change, as there will be a likely increase in the occurrence of extreme drought events and water stress issues.

Technical Abstract: Fresh water stored by on-farm reservoirs (OFRs) is a fundamental component of surface hydrology and is critical for meeting global irrigation needs. Farmers use OFRs to store water during the wet season for crop irrigation during the dry season. However, OFRs can contribute to downstream water stress by decreasing stream discharge and peak flow in the watersheds where they are built. Therefore, understanding OFRs' sub-weekly surface area changes is critical to mitigate their downstream impacts; nonetheless, prior to recent development and availability of satellite imagery, monitoring OFRs’ sub-weekly surface area changes across space and time was challenging due satellite observation latency and spatial resolution. We propose a novel multi-sensor approach to monitor OFRs surface area that leverages the use of PlanetScope (PS), RapidEye (RE), Sentinel (S1), and Sentinel 2 (S2). First, we estimate the uncertainties in surface area for each sensor by comparing the surface area estimates with a ground-truth dataset, and comparing RE, S1 and S2 to PS—the sensor with the highest spatial resolution (i.e., 3.125 m). Second, we use the uncertainties of each sensor with a data assimilation algorithm based on the Kalman filter to create a unique harmonized sub-weekly OFRs’ surface area time series. Our results show that the highest uncertainties are found for PS, RE, S2, and S1, respectively, and the uncertainties vary according to OFRs of different size and shape complexities. The Kalman filter approach allowed us to create harmonized OFRs’ surface area time series by using multiple sensors and their uncertainties. The Kalman filter performance analysis shows that the algorithm performs well for most of the OFRs in the ground-truth dataset, and the methods presented in the study has the potential to enhance water conservation plans by allowing better assessment and management of the OFRs’ water quantity.