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

Perin, Vinicius; Tulbure, Mirela G.; Gaines, Mollie D.; Reba, M. L.; Yaeger, Mary

doi:10.1016/j.rse.2021.112796

Cited by 16 publications

(24 citation statements)

References 85 publications

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“…The number of cloud-free observations offered by Basemap and Planet Fusion enlightens the potential of these datasets to monitor OFR surface area changes (Figure 4). Both datasets pose advantages when compared to a single sensor approach-employing Plan-etScope alone (Figure 4), or other sensors, for example, Landsat [23,31,56], Sentinel 1 [57], and Sentinel-2 [58][59][60]-or a multi-sensor approach [4,33,34]. Briefly, the use of a single sensor is limited to a few observations a month, and in some periods of the year in eastern Arkansas, there could be weeks without a cloud-free image [33].…”

Section: Discussionmentioning

confidence: 99%

“…OFR surface water classification is impacted by the OFRs' environmental conditions and their shape geometry. OFRs with complex geometries (e.g., not circular or square and shapes with a large number of edges) tend to have higher surface area classification uncertainties [33]. For example, Figure 8, OFR D, shows a multi-part OFR that may not have all parts inundated at the same time, which can explain part of the variability in the surface area time series for PlanetScope, Basemap, and Planet Fusion.…”

Section: Ofr Surface Area Time Seriesmentioning

confidence: 99%

“…The surface area time series from OFR E and OFR F (Figure 8) are influenced by the presence of vegetation within the OFRs. The presence of vegetation impacts surface water classification [5,33,55], leading to noisy surface area time series and abrupt changes (e.g., OFR E between 20 September and 21 January). In addition, the high variability in surface area for OFRs E and F is related to the presence of adjacent water bodies, which can inundate during flood events and contribute to changes on OFR boundary limits.…”

Section: Ofr Surface Area Time Seriesmentioning

confidence: 99%

“…Nonetheless, these datasets are limited to a few annual observations-due to clouds, sensor issues, and the 16-day repeat cycle-and Landsat's spatial resolution (30 m) limits the applications of these datasets to monitor OFRs smaller than 5 ha (i.e., high surface area uncertainties ~20%). Aiming to overcome these limitations, other studies [4,33,34] have applied a multi-sensor satellite imagery approach, including sensors of higher spatial and temporal resolution (e.g., PlanetScope [3 m] and Sentinel-2 [10 m]) when compared to Landsat. However, a multi-sensor approach requires processing of satellite imagery of different spatial resolution from multiple platforms, which can be time-consuming and a limiting factor if it is necessary to process, download, and move the satellite imagery across multiple platforms [33].…”

Section: Introductionmentioning

confidence: 99%

“…Aiming to overcome these limitations, other studies [4,33,34] have applied a multi-sensor satellite imagery approach, including sensors of higher spatial and temporal resolution (e.g., PlanetScope [3 m] and Sentinel-2 [10 m]) when compared to Landsat. However, a multi-sensor approach requires processing of satellite imagery of different spatial resolution from multiple platforms, which can be time-consuming and a limiting factor if it is necessary to process, download, and move the satellite imagery across multiple platforms [33]. In this study, we propose a novel use of the analysis ready datasets Basemap and Planet Fusion, and we aim (1) to assess the usefulness of both datasets to monitor OFRs sub-weekly surface area changes and (2) to compare the two datasets and describe their differences when used to monitor OFRs.…”

Section: Introductionmentioning

confidence: 99%

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Monitoring Small Water Bodies Using High Spatial and Temporal Resolution Analysis Ready Datasets

et al. 2021

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Basemap and Planet Fusion—derived from PlanetScope imagery—represent the next generation of analysis ready datasets that minimize the effects of the presence of clouds. These datasets have high spatial (3 m) and temporal (daily) resolution, which provides an unprecedented opportunity to improve the monitoring of on-farm reservoirs (OFRs)—small water bodies that store freshwater and play important role in surface hydrology and 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% and 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 OFRs sub-weekly surface area changes, and Basemap had higher surface area variability and 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 uncertainties in surface area classification 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 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.

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