On-farm reservoir monitoring using Landsat inundation datasets

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

doi:10.1016/j.agwat.2020.106694

Cited by 17 publications

(16 citation statements)

References 57 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%

“…The surface area time series show that different OFRs have different surface area change patterns. In general, the OFR surface area decreased between 07/20 and 11/20 (e.g., Figure 8, OFRs A-D), period of the year when farmers are irrigating their crops [23], and it increased between 01/21 and 05/21, which are the months when the study region receives most of its annual precipitation [23].…”

Section: Ofr Surface Area Time Seriesmentioning

confidence: 93%

“…Eastern Arkansas is one of the largest irrigated regions in the USA that has seen a rapid increase in the number of OFRs during the last 40 years [35][36][37]. The region has a humid subtropical climate with an average annual precipitation of 1300 mm, mostly distributed between March and May and November and January [23]. Recent studies [35,36] mapped the spatial distribution of 340 OFRs with surface area <30 ha and distributed across three sub-watersheds in the study region (Figure 1).…”

Section: Study Regionmentioning

confidence: 99%

“…Given that these datasets are cloud-free and processed to have daily cadence at high spatial resolution-both Basemap and Planet Fusion have 3 m pixel size-they provide an unprecedented opportunity to improve the monitoring of dynamic small water bodies, for instance, on-farm reservoirs (OFRs) that are used by farmers to store water during the wet season and for crop irrigation during the dry season. OFRs have a dynamic surface area time series, especially during the crop-growing season, when farmers are irrigating their crops and may pump water from nearby streams [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, monitoring OFRs sub-weekly surface area changes is critical to the assessment of their seasonal and inter-annual variability, as well as to mitigation of their downstream impacts, with implications concerning how OFRs are managed and where they are built. Previous research assessed the spatial and temporal variability of OFRs by leveraging the long-term (≥25 years) Landsat-based inundation datasets [23,31,32]. 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%).…”

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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