DATA ASSIMILATION OF REMOTELY SENSED SOIL MOISTURE TO DETECT WATER STRESS PERIODS IN AGRICULTURAL AREAS

Héctor Ernesto Huerta-Batiz; Daniel Enrique Constantino-Recillas; Alejandro Monsiváis-Huertero; Ramón Sidonio Aparicio-García; Eduardo Arizmendi-Vasconcelos; José Carlos Jiménez-Escalona; Cira Francisca Zambrano-Gallardo; Jasmeet Judge

doi:10.1109/IGARSS47720.2021.9553228

DATA ASSIMILATION OF REMOTELY SENSED SOIL MOISTURE TO DETECT WATER STRESS PERIODS IN AGRICULTURAL AREAS

Héctor Ernesto Huerta-Batiz, Daniel Enrique Constantino-Recillas, Alejandro Monsiváis-Huertero, Ramón Sidonio Aparicio-García, Eduardo Arizmendi-Vasconcelos, José Carlos Jiménez-Escalona, Cira Francisca Zambrano-Gallardo, Jasmeet Judge

Escuela Superior de Ingeniería Mecánica y Eléctrica (ESIME), Unidad Ticomán

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

Resumen

In this study, a data assimilation framework based on the Ensemble Kalman Filter was implemented including a soil-vegetation-atmosphere energy transfer (SVAT) model. The SVAT model has been calibrated with in-situ data in the central region of Mexico, with temperate subhumid climate. The soil moisture information from ten locations was scaled within a 36km satellite pixel. Both synthetic observations and SMAP SM retrieval were assimilated and they improved by 29% compared to open-loop simulations. Particularly, the assimilated soil moisture allows us to have a better characterization of periods of water stress for corn cultivation.

Idioma original	Inglés
Título de la publicación alojada	IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
Editorial	Institute of Electrical and Electronics Engineers Inc.
Páginas	1331-1334
Número de páginas	4
ISBN (versión digital)	9781665403696
DOI	https://doi.org/10.1109/IGARSS47720.2021.9553228
Estado	Publicada - 2021
Evento	2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021 - Brussels, Bélgica Duración: 12 jul. 2021 → 16 jul. 2021

Serie de la publicación

Nombre	International Geoscience and Remote Sensing Symposium (IGARSS)
Volumen	2021-July

Conferencia

Conferencia	2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021
País/Territorio	Bélgica
Ciudad	Brussels
Período	12/07/21 → 16/07/21

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

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10.1109/IGARSS47720.2021.9553228

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Huerta-Batiz, H. E., Constantino-Recillas, D. E., Monsiváis-Huertero, A., Aparicio-García, R. S., Arizmendi-Vasconcelos, E., Jiménez-Escalona, J. C., Zambrano-Gallardo, C. F., & Judge, J. (2021). DATA ASSIMILATION OF REMOTELY SENSED SOIL MOISTURE TO DETECT WATER STRESS PERIODS IN AGRICULTURAL AREAS. En IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings (pp. 1331-1334). (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2021-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS47720.2021.9553228

Huerta-Batiz, Héctor Ernesto ; Constantino-Recillas, Daniel Enrique ; Monsiváis-Huertero, Alejandro et al. / DATA ASSIMILATION OF REMOTELY SENSED SOIL MOISTURE TO DETECT WATER STRESS PERIODS IN AGRICULTURAL AREAS. IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 1331-1334 (International Geoscience and Remote Sensing Symposium (IGARSS)).

@inproceedings{d7675853b3b5409899fb6424ed32cc41,

title = "DATA ASSIMILATION OF REMOTELY SENSED SOIL MOISTURE TO DETECT WATER STRESS PERIODS IN AGRICULTURAL AREAS",

abstract = "In this study, a data assimilation framework based on the Ensemble Kalman Filter was implemented including a soil-vegetation-atmosphere energy transfer (SVAT) model. The SVAT model has been calibrated with in-situ data in the central region of Mexico, with temperate subhumid climate. The soil moisture information from ten locations was scaled within a 36km satellite pixel. Both synthetic observations and SMAP SM retrieval were assimilated and they improved by 29% compared to open-loop simulations. Particularly, the assimilated soil moisture allows us to have a better characterization of periods of water stress for corn cultivation.",

keywords = "Data assimilation, EnKF, In-situ campaign, SMAP, SVAT, Soil moisture, THEx-Mex",

author = "Huerta-Batiz, {H{\'e}ctor Ernesto} and Constantino-Recillas, {Daniel Enrique} and Alejandro Monsiv{\'a}is-Huertero and Aparicio-Garc{\'i}a, {Ram{\'o}n Sidonio} and Eduardo Arizmendi-Vasconcelos and Jim{\'e}nez-Escalona, {Jos{\'e} Carlos} and Zambrano-Gallardo, {Cira Francisca} and Jasmeet Judge",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021 ; Conference date: 12-07-2021 Through 16-07-2021",

year = "2021",

doi = "10.1109/IGARSS47720.2021.9553228",

language = "Ingl{\'e}s",

series = "International Geoscience and Remote Sensing Symposium (IGARSS)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1331--1334",

booktitle = "IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings",

address = "Estados Unidos",

}

Huerta-Batiz, HE, Constantino-Recillas, DE, Monsiváis-Huertero, A, Aparicio-García, RS, Arizmendi-Vasconcelos, E, Jiménez-Escalona, JC, Zambrano-Gallardo, CF & Judge, J 2021, DATA ASSIMILATION OF REMOTELY SENSED SOIL MOISTURE TO DETECT WATER STRESS PERIODS IN AGRICULTURAL AREAS. En IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings. International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2021-July, Institute of Electrical and Electronics Engineers Inc., pp. 1331-1334, 2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021, Brussels, Bélgica, 12/07/21. https://doi.org/10.1109/IGARSS47720.2021.9553228

DATA ASSIMILATION OF REMOTELY SENSED SOIL MOISTURE TO DETECT WATER STRESS PERIODS IN AGRICULTURAL AREAS. / Huerta-Batiz, Héctor Ernesto; Constantino-Recillas, Daniel Enrique; Monsiváis-Huertero, Alejandro et al.
IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. p. 1331-1334 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2021-July).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

TY - GEN

T1 - DATA ASSIMILATION OF REMOTELY SENSED SOIL MOISTURE TO DETECT WATER STRESS PERIODS IN AGRICULTURAL AREAS

AU - Huerta-Batiz, Héctor Ernesto

AU - Constantino-Recillas, Daniel Enrique

AU - Monsiváis-Huertero, Alejandro

AU - Aparicio-García, Ramón Sidonio

AU - Arizmendi-Vasconcelos, Eduardo

AU - Jiménez-Escalona, José Carlos

AU - Zambrano-Gallardo, Cira Francisca

AU - Judge, Jasmeet

PY - 2021

Y1 - 2021

N2 - In this study, a data assimilation framework based on the Ensemble Kalman Filter was implemented including a soil-vegetation-atmosphere energy transfer (SVAT) model. The SVAT model has been calibrated with in-situ data in the central region of Mexico, with temperate subhumid climate. The soil moisture information from ten locations was scaled within a 36km satellite pixel. Both synthetic observations and SMAP SM retrieval were assimilated and they improved by 29% compared to open-loop simulations. Particularly, the assimilated soil moisture allows us to have a better characterization of periods of water stress for corn cultivation.

AB - In this study, a data assimilation framework based on the Ensemble Kalman Filter was implemented including a soil-vegetation-atmosphere energy transfer (SVAT) model. The SVAT model has been calibrated with in-situ data in the central region of Mexico, with temperate subhumid climate. The soil moisture information from ten locations was scaled within a 36km satellite pixel. Both synthetic observations and SMAP SM retrieval were assimilated and they improved by 29% compared to open-loop simulations. Particularly, the assimilated soil moisture allows us to have a better characterization of periods of water stress for corn cultivation.

KW - Data assimilation

KW - EnKF

KW - In-situ campaign

KW - SMAP

KW - SVAT

KW - Soil moisture

KW - THEx-Mex

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U2 - 10.1109/IGARSS47720.2021.9553228

DO - 10.1109/IGARSS47720.2021.9553228

M3 - Contribución a la conferencia

AN - SCOPUS:85126069699

T3 - International Geoscience and Remote Sensing Symposium (IGARSS)

SP - 1331

EP - 1334

BT - IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021

Y2 - 12 July 2021 through 16 July 2021

ER -

Huerta-Batiz HE, Constantino-Recillas DE, Monsiváis-Huertero A, Aparicio-García RS, Arizmendi-Vasconcelos E, Jiménez-Escalona JC et al. DATA ASSIMILATION OF REMOTELY SENSED SOIL MOISTURE TO DETECT WATER STRESS PERIODS IN AGRICULTURAL AREAS. En IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2021. p. 1331-1334. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS47720.2021.9553228

DATA ASSIMILATION OF REMOTELY SENSED SOIL MOISTURE TO DETECT WATER STRESS PERIODS IN AGRICULTURAL AREAS

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