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ARS Home » Southeast Area » Jonesboro, Arkansas » Delta Water Management Research » Research » Publications at this Location » Publication #389954
Research Project: Preserving Water Availability and Quality for Agriculture in the Lower Mississippi River Basin

Location: Delta Water Management Research

Title: Monitoring small water bodies using high spatial and temporal resolution analysis ready datasets

Author
item PERIN, VINICIUS - North Carolina State University
item ROY, SAMAPRIA - Planet Labs Inc
item KINGTON, JOE - Planet Labs Inc
item HARRIS, THOMAS - Planet Labs Inc
item TALBURE, MIRELA - North Carolina State University
item STONE, NOAH - Planet Labs Inc
item BARBALLE, TORBEN - Planet Labs Inc
item Reba, Michele
item YAEGER, MARY - University Of Memphis

Submitted to: Journal of Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/13/2021
Publication Date: 12/20/2021
Citation: Perin, V., Roy, S., Kington, J., Harris, T., Talbure, M., Stone, N., Barballe, T., Reba, M.L., Yaeger, M.A. 2021. Monitoring small water bodies using high spatial and temporal resolution analysis ready datasets. Journal of Remote Sensing. 13(24):5176. https://doi.org/10.3390/rs13245176.
DOI: https://doi.org/10.3390/rs13245176

Interpretive Summary: The development of new remote sensing algorithms allows us to combine data from different satellites to improve the monitoring of Earth’s surface processes. With these new algorithms we are able to create datasets that have higher spatial and temporal resolution than the regular satellite images. In this study, we used two state-of-the-art datasets—Basemap and Planet Fusion—to monitor daily changes of on-farm reservoirs’ surface areas. We found that both datasets can be used to monitor the on-farm reservoirs’ daily and seasonal surface area changes, therefore, these datasets can help improve freshwater management by allowing better assessment and management of the reservoirs.

Technical Abstract: Basemap and Planet fusion—derived from PlanetScope imagery—represent the next generation of analysis ready datasets that are less affected by the presence of clouds. These datasets have high spatial (< 4.77 m) and temporal (daily) resolution which provide an unprecedented opportunity to improve the monitoring of on-farm reservoirs (OFRs)—small water bodies that play an important role in surface hydrology by storing fresh water and being an essential component of global irrigation activities. In this study, we assessed the usefulness of both datasets to monitor sub- weekly surface area changes of 340 OFRs in eastern Arkansas, USA, and we evaluated the datasets’ main differences when used to monitor OFRs. When comparing the OFRs’ surface area derived from Basemap and Planet fusion to an independent validation dataset, both datasets had high agreement (r2 >= 0.87), and small uncertainties with a mean absolute percent error (MAPE) between 7.05–10.08%. Pairwise surface area comparisons between the two datasets and the PlanetScope imagery showed that 61% of the OFRs had r2 >= 0.55, and 70% of the OFRs had MAPE < 5%. In general, both datasets can be employed to monitor the OFRs’ sub-weekly surface area changes, and Basemap had higher surface area variability and it was more susceptible to the presence of cloud shadows and haze when compared to Planet fusion—which had a smoother time series with less variability and fewer abrupt changes throughout the year. The surface area classification uncertainties decreased as the OFRs increased in size. In addition, the surface area time series can have high variability depending on the OFR environmental conditions (e.g. presence of vegetation inside the OFR). Our findings suggest that both datasets can be used to monitor the OFRs’ sub-weekly, seasonal, and inter-annual surface area changes, therefore, these datasets can help improve freshwater management by allowing better assessment and management of the OFRs.