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Analysis of institutional authors

Simeon, RubenAuthorRubio, ConstanzaAuthorUris, AntonioCorresponding AuthorAgenjos-Moreno, AlbaAuthorBautista, Alberto SanAuthor

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August 28, 2025
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Assessment of Water Depth Variability and Rice Farming Using Remote Sensing

Publicated to: SENSORS. 25 (15): 4860- - 2025-01-01 25(15), DOI: 10.3390/s25154860

Authors:

Simeon, Ruben; Rubio Michavila, Constanza; Uris Martínez, Antonio; Coronado, Javier; Agenjos-Moreno, Alba; San Bautista Primo, Alberto
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Affiliations

Univ Politecn Valencia, Ctr Tecnol Fis, Cami Vera S-N, Valencia 46022, Spain - Author
Univ Politecn Valencia, Ctr Valenciano Estudios Riego CVER, Cami Vera S-N, Valencia 46022, Spain - Author

Abstract

Remote sensing is a widely used tool for crop monitoring to improve water management. Rice, a crop traditionally grown under flooded conditions, requires farmers to understand the relationship between crop reflectance, water depth and final yield. This study focused on seven commercial rice fields in 2022 and six in 2023, analyzing the correlations between water depth and Sentinel-2 reflectance over two growing seasons in Valencia, Spain. During the tillering stage across both seasons, water depth showed positive correlations with visible bands and negative correlations with NIR and SWIR bands. There were no correlations with the indices NDVI, GNDVI, NDRE and NDWI. The NIR band showed significant correlations across both seasons, with R2 values of 0.69 and 0.71, respectively. In addition, the calculation of NIR anomalies for each field proved to be a good indicator of final yield anomalies. In 2022, anomalies above 10% corresponded to yield deviations above 500 kgha-1, while in 2023, anomalies above 15% were associated with yield deviations above 1000 kgha-1. The response of final yield to water level was positive up to average values of 9 cm. The use of the NIR band during the rice crop tillering stage can support farmers in improving irrigation management.
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Keywords

BiomassCropsGrowthIndexIrrigationMoistureNir banNir bandReflectanceRemote sensingRiceSentinel-2VegetationVegetation indicesYiel

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal SENSORS due to its progression and the good impact it has achieved in recent years, according to the agency Scopus (SJR), it has become a reference in its field. In the year of publication of the work, 2025, it was in position , thus managing to position itself as a Q1 (Primer Cuartil), in the category Instrumentation.

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Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-03:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 4 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

    It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

    • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
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    Leadership analysis of institutional authors

    There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (Simeon Brocal, Ruben) and Last Author (San Bautista Primo, Alberto).

    the author responsible for correspondence tasks has been Uris Martínez, Antonio.

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    Awards linked to the item

    This research has been funded by the PREDIC-PRO project SCPP2100C008733XV0 (CPP2021-008733) of the State Research Agency of the Ministry of Science, Innovation and Universities.
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