V. Dokuchaev Soil Science Institute, 109017 Moscow, Russia.
Department of Geography, Lomonosov Moscow State University, 119991 Moscow, Russia.
Sensors (Basel). 2021 Nov 7;21(21):7403. doi: 10.3390/s21217403.
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.
在温带半干旱生态区，地形洼地中集中的融雪水渗透是一种重要但了解不充分的地下水补给机制。由于洼地数量众多，常规监测受到限制。我们使用遥感数据构建了俄罗斯坦波夫地区的质量平衡，并估算了临时池塘的体积。首先，通过面向对象的监督分类，自动识别了 2021 年 4 月和 5 月拍摄的一系列高分辨率 Planet Labs 图像中的小水体。使用小型无人机在最新图像之一中定义的水体像素训练集可实现对早期图像中水体像素的高置信度预测（Cohen 的 Κ = 0.99）。数字高程模型用于估算池塘的水量，水量随时间呈负指数方程递减。指数的幂没有系统地依赖于池塘的大小。通过对每日彭曼蒸发量的估计进行调整，基于函数的水体面积和体积插值可计算出每日渗透到洼地床的水量。在春季开始时，渗透量最大（5-40mm/d），在研究期间随时间逐渐减少。使用空间变化的渗透速率可改善稳定态浅层地下水模拟。
