Evidence map›Paper›PMID 36644377›Full record

ArticleRemote sensing2022

Quantifying Irrigated Winter Wheat LAI in Argentina Using Multiple Sentinel-1 Incidence Angles.

Gabriel Caballero, Alejandro Pezzola, Cristina Winschel, Alejandra Casella, Paolo Sanchez Angonova, Luciano Orden, Katja Berger, Jochem Verrelst, Jesús Delegido

Abstract read
In one paragraph

Article in Remote sensing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
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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

9 authors.

Gabriel CaballeroAgri-Environmental Engineering, Technological University of Uruguay (UTEC), Av. Italia 6201, 11500 Montevideo, Uruguay.ORCID https://orcid.org/0000-0003-2268-2674
Alejandro PezzolaRemote Sensing and SIG Laboratory, Hilario Ascasubi Agricultural Experimental Station, National Institute of Agricultural Technology (INTA), Hilario Ascasubi 8142, Argentina.
Cristina WinschelRemote Sensing and SIG Laboratory, Hilario Ascasubi Agricultural Experimental Station, National Institute of Agricultural Technology (INTA), Hilario Ascasubi 8142, Argentina.
Alejandra CasellaPermanent Observatory of Agro-Ecosystems, Climate and Water Institute-National Agricultural Research Centre (ICyA-CNIA), National Institute of Agricultural Technology (INTA), Nicolás Repetto s/n, Hurlingham, Buenos Aires 1686, Argentina.
Paolo Sanchez AngonovaRemote Sensing and SIG Laboratory, Hilario Ascasubi Agricultural Experimental Station, National Institute of Agricultural Technology (INTA), Hilario Ascasubi 8142, Argentina.
Luciano OrdenRemote Sensing and SIG Laboratory, Hilario Ascasubi Agricultural Experimental Station, National Institute of Agricultural Technology (INTA), Hilario Ascasubi 8142, Argentina.
Katja BergerImage Processing Laboratory (IPL), University of Valencia, C/Catedrático José Beltrán 2, Paterna, 46980 Valencia, Spain.
Jochem VerrelstImage Processing Laboratory (IPL), University of Valencia, C/Catedrático José Beltrán 2, Paterna, 46980 Valencia, Spain.
Jesús DelegidoImage Processing Laboratory (IPL), University of Valencia, C/Catedrático José Beltrán 2, Paterna, 46980 Valencia, Spain.

Funding

European Research Council 755617
6 · The paper itself

Abstract

Synthetic aperture radar (SAR) data provides an appealing opportunity for all-weather day or night Earth surface monitoring. The European constellation Sentinel-1 (S1) consisting of S1-A and S1-B satellites offers a suitable revisit time and spatial resolution for the observation of croplands from space. The C-band radar backscatter is sensitive to vegetation structure changes and phenology as well as soil moisture and roughness. It also varies depending on the local incidence angle (LIA) of the SAR acquisition's geometry. The LIA backscatter dependency could therefore be exploited to improve the retrieval of the crop biophysical variables. The availability of S1 radar time-series data at distinct observation angles holds the feasibility to retrieve leaf area index (LAI) evolution considering spatiotemporal coverage of intensively cultivated areas. Accordingly, this research presents a workflow merging multi-date S1 smoothed data acquired at distinct LIA with a Gaussian processes regression (GPR) and a cross-validation (CV) strategy to estimate cropland LAI of irrigated winter wheat. The GPR-S1-LAI model was tested against in situ data of the 2020 winter wheat campaign in the irrigated valley of Colorador river, South of Buenos Aires Province, Argentina. We achieved adequate validation results for LAI with

Indexed as

Gaussian processes regressionleaf area indexlocal incidence angleSentinel-1time-seriesWhittaker smoother

Identifiers

PMID36644377
PMCPMC7614051

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LicenceCC BY
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Registered trials

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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.