Fecha/hora: 16/06/2024 07:26:49

Biblioteca(s): | INIA La Estanzuela.
Fecha corriente: | 03/08/2021
Fecha de la última actualización: | 02/09/2022
Autoria: | GASO, D.; DE WIT, A.; BERGER, A.; KOOISTRA, L.
Afiliación: | DEBORAH VIVIANA GASO MELGAR, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ALLARD DE WIT, Wageningen Environmental Research, Wageningen 6708 PB, The Netherlands.; ANDRES GUSTAVO BERGER RICCA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; LAMMERT KOOISTRA, Laboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, Wageningen 6708 PB, Netherlands.
Título: | Predicting within-field soybean yield variability by coupling Sentinel-2 leaf area index with a crop growth model.
Año de publicación: | 2021
Fuente/Imprenta: | Agricultural and Forest Meteorology, 2021, Volumes 308-309, article 108553. OPEN ACCESS. Doi: https://doi.org/10.1016/j.agrformet.2021.108553
DOI: | 10.1016/j.agrformet.2021.108553
Idioma: | Inglés
Notas: | Article history: Received 15 February 2021, Revised 3 June 2021, Accepted 9 July 2021, Available online 22 July 2021.  Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Contenido:

ABSTRACT: Accurate within-field yield estimation is an essential step to conduct yield gap analysis and steer crop management towards more efficient use of resources. This study aims to develop and validate a process-based soybean model and to predict within-field yield variability by coupling leaf area index (LAI) retrieval from Sentinel-2  into the crop model. First, a soybean model is presented, which was successfully validated with field observations of total aboveground biomass, LAI and yield from seven contrasting field campaigns with strongly varying conditions. Within-field yield predictions were achieved by combining the model and the observations of LAI through an assimilation strategy. Four model parameters were chosen to optimize against the LAI curve: soil 
depth, field capacity, initial LAI and nitrogen translocated from leaves to seed. Six fields were used to evaluate the methodology (21175 pixels). The accuracy assessment was conducted on a pixel-by-pixel basis using high density of information from the yield monitor. The overall accuracy quantified by the relative root mean square error (rRMSE) ranged from 28 to 51% (overall rRMSE 35.8%) across the studied fields. The Lee statistics index ranged from 0.61 to 0.71, confirming a high level of similarity between observed and simulated yield maps. Therefore, the methodology was capable of representing the observed spatial patterns of yield. Furthermore, the high consistency of the optimized WHC reflects the value of the assimilation data strategy to spatialize this relevant characteristic. Some challenges were identified for further study to reduce the sources of uncertainty and improve accuracy: i) the inability of the model to reallocate biomass by simulating plant response to source limitation, ii) the generalization of empirical algorithms to retrieve LAI, and iii) the exploration of an updating method as an assimilation strategy to overcome discrepancy between simulated and retrieved LAI.

Palabras-Clave: | Crop growth model; Data assimilation; Sentinel-2; Soybean; Yield prediction.
Thesagro: | Soja.
Categoría del tema: | F01 Cultivo

URL:

http://www.ainfo.inia.uy/digital/bitstream/item/16670/1/1-s2.0-S0168192321002379-main.pdf
https://www.sciencedirect.com/science/article/pii/S0168192321002379/pdfft?md5=c4c224be450c53eba73eada140b04cc2&pid=1-s2.0-S0168192321002379-main.pdf

Registro original: | INIA La Estanzuela (LE)
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