Estimation of Infiltration Volumes and Rates in Seasonally Water-Filled Topographic Depressions Based on Remote-Sensing Time Series

Abstract

In semi-arid ecoregions of temperate zones, focused snowmelt water infiltration in topographic depressions is a key, but imperfectly understood, groundwater recharge mechanism. Routine monitoring is precluded by the abundance of depressions. We have used remote-sensing data to construct mass balances and estimate volumes of temporary ponds in the Tambov area of Russia. First, small water bodies were automatically recognized in each of a time series of high-resolution Planet Labs images taken in April and May 2021 by object-oriented supervised classification. A training set of water pixels defined in one of the latest images using a small unmanned aerial vehicle enabled high-confidence predictions of water pixels in the earlier images (Cohen’s Κ = 0.99). A digital elevation model was used to estimate the ponds’ water volumes, which decreased with time following a negative exponential equation. The power of the exponent did not systematically depend on the pond size. With adjustment for estimates of daily Penman evaporation, function-based interpolation of the water bodies’ areas and volumes allowed calculation of daily infiltration into the depression beds. The infiltration was maximal (5–40 mm/day) at onset of spring and decreased with time during the study period. Use of the spatially variable infiltration rates improved steady-state shallow groundwater simulations.

Advances in the Monitoring, Diagnosis and Optimisation of Water Systems

“…Further work in this area is included in the Special Issue, e.g., in [ 9 ], where remote sensing (RS) image-based time series are considered to obtain mass balances and estimate the unfiltered volumes in topographic depressions which are seasonally filled with water in a real area; or in [ 10 ], where a soil-moisture monitoring technique in precision agriculture—which is becoming key to providing food sustainably in the context of world’s increasing population and natural resource scarcity—is provided using a low-cost wireless sensor network in order to help farmers optimise the irrigation process, and is tested in a real plot of land. Finally, a comprehensive review of AI and computer-vision methods for intelligent water monitoring—namely, water-body extraction and water-quality monitoring—using RS techniques is presented in [ 11 ], discussing the main challenges of using AI and RS for water-information extraction, as well as pointing out research priorities in this area.…”