On-farm reservoirs

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

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.

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

We propose a novel multi-sensor approach to monitor OFRs surface areas, developed based on 736 OFRs in eastern Arkansas, USA, which leverages the use of PlanetScope (PS), RapidEye (RE), Sentinel 2 (S2), and Sentinel 1 (S1). First, we estimate the uncertainties in surface area for each sensor by comparing the surface area estimates to a validation dataset, and by comparing RE, S2 and S1 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 obtain sub-weekly surface area time series for all OFRs.

Quantifying on-farm reservoirs’ impacts on surface hydrology using a multi-sensor approach

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. There are more than 2.6 million OFRs in the US alone, and many of these OFRs were constructed during the last 40 years.