Evidence map›Paper›PMID 34770708›Full record

ArticleSensors (Basel, Switzerland)2021

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

Pavel P Fil, Alla Yu Yurova, Alexey Dobrokhotov, Daniil Kozlov

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.3field-weighted citation impact, top 42% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 3 institutions in 1 country.

Pavel P FilV. Dokuchaev Soil Science Institute, 109017 Moscow, Russia.
Alla Yu YurovaV. Dokuchaev Soil Science Institute, 109017 Moscow, Russia.
Alexey DobrokhotovAgrophysical Research Institute, 195220 St. Petersburg, Russia.
Daniil KozlovV. Dokuchaev Soil Science Institute, 109017 Moscow, Russia.ORCID 0000-0003-0103-0300
V.V. Dokuchaev Soil Science Institute · RUAgrophysical Research Institute · RULomonosov Moscow State University · RU

Funding

Russian Foundation for Basic Research 19-29-05277
6 · The paper itself

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.

Indexed as

DepressionGroundwaterPondsSeasonsWaterWaterclosed depressionsinfiltrationremote sensingtemporary water bodies

Identifiers

PMID34770708
PMCPMC8588343
OpenAlexW3211577252

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.