Evidence map›Paper›PMID 36093264›Full record

ArticleInternational journal of applied earth observation and geoinformation : ITC journal2022

Estimating soil moisture content under grassland with hyperspectral data using radiative transfer modelling and machine learning.

Veronika Döpper, Alby Duarte Rocha, Katja Berger, Tobias Gränzig, Jochem Verrelst, Birgit Kleinschmit, Michael Förster

Abstract read
In one paragraph

Article in International journal of applied earth observation and geoinformation : ITC journal, 2022. 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
–field-weighted citation impact
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.

  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

7 authors.

Veronika DöpperGeoinformation in Environmental Planning Lab, Technische Universität Berlin (TUB), Berlin, Germany.
Alby Duarte RochaGeoinformation in Environmental Planning Lab, Technische Universität Berlin (TUB), Berlin, Germany.
Katja BergerDepartment of Geography, Ludwig-Maximilians-Universität München (LMU), Munich, Munich, Germany.
Tobias GränzigGeoinformation in Environmental Planning Lab, Technische Universität Berlin (TUB), Berlin, Germany.
Jochem VerrelstImage Processing Laboratory (IPL), Universitat de València, València, Spain.
Birgit KleinschmitGeoinformation in Environmental Planning Lab, Technische Universität Berlin (TUB), Berlin, Germany.
Michael FörsterGeoinformation in Environmental Planning Lab, Technische Universität Berlin (TUB), Berlin, Germany.

Funding

European Research Council 755617
6 · The paper itself

Abstract

The monitoring of soil moisture content (SMC) at very high spatial resolution (<10m) using unmanned aerial systems (UAS) is of high interest for precision agriculture and the validation of large scale SMC products. Data-driven approaches are the most common method to retrieve SMC with UAS-borne data at water limited sites over non-disturbed agricultural crops. A major disadvantage of data-driven algorithms is the limited transferability in space and time and the need of a high number of ground reference samples. Physically-based approaches are less dependent on the amount of samples and are transferable in space and time. This study explores the potential of (1) a hybrid method targeting the soil brightness factor of the PROSAIL model using a variational heteroscedastic Gaussian Processes regression (VHGPR) algorithm, and (2) a data-driven method employing VHGPR for the retrieval of SMC over three grassland sites based on UAS-borne VIS-NIR (399-1001 nm) hyperspectral data. The sites were managed by mowing (Fendt), grazing (Grosses Bruch) and irrigation (Marquardt). With these distinct local pre-conditions we aimed to identify factors that favor and limit the retrieval of SMC. The hybrid approach presented encouraging results in Marquardt (RMSE = 1.5 Vol_%, R

Indexed as

Anthropogenic InfluenceGaussian ProcessesGrazingIrrigationLAILeaf Area IndexMowingPROSAILTime LagUnmanned Aerial Systems

Identifiers

PMID36093264
PMCPMC7613374

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.