Downscaling smap soil moisture retrievals over an agricultural region in central mexico using machine learning

Juan Carlos Hernández-Sánchez; Alejandro Monsiváis-Huertero; Jasmeet Judge; José Carlos Jiménez-Escalona

doi:10.1109/IGARSS.2019.8900285

Downscaling smap soil moisture retrievals over an agricultural region in central mexico using machine learning

Juan Carlos Hernández-Sánchez, Alejandro Monsiváis-Huertero, Jasmeet Judge, José Carlos Jiménez-Escalona

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

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3 Citas (Scopus)

Resumen

Soil moisture (SM) is an important land surface variable for understanding the water cycle, ecosystem productivity, and linkages between water-carbon cycles. For agricultural applications, SM information is needed at higher resolutions (about 1km). In this study, coarse-scale remotely sensed SM at 36 km from NASA-SMAP was disaggregated to 1 km using high resolution auxiliary information such as land cover, precipitation, land surface temperature, NDVI for a growing season of corn in 2018 in Central Mexico (CM). The main objective is to evaluate a machine-learning based downscaling algorithm over an agricultural area with very limited in-situ observations of SM obtained during THExMEX-18. We found that overall, the downscaled moisture captured the dynamics during the growing season observed by the in-situ measurements.

Idioma original	Inglés
Páginas	7049-7052
Número de páginas	4
DOI	https://doi.org/10.1109/IGARSS.2019.8900285
Estado	Publicada - 2019
Evento	39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Yokohama, Japón Duración: 28 jul. 2019 → 2 ago. 2019

Conferencia

Conferencia	39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019
País/Territorio	Japón
Ciudad	Yokohama
Período	28/07/19 → 2/08/19

ODS de las Naciones Unidas

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10.1109/IGARSS.2019.8900285

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Hernández-Sánchez, J. C., Monsiváis-Huertero, A., Judge, J., & Jiménez-Escalona, J. C. (2019). Downscaling smap soil moisture retrievals over an agricultural region in central mexico using machine learning. 7049-7052. Papel presentado en 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019, Yokohama, Japón. https://doi.org/10.1109/IGARSS.2019.8900285

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abstract = "Soil moisture (SM) is an important land surface variable for understanding the water cycle, ecosystem productivity, and linkages between water-carbon cycles. For agricultural applications, SM information is needed at higher resolutions (about 1km). In this study, coarse-scale remotely sensed SM at 36 km from NASA-SMAP was disaggregated to 1 km using high resolution auxiliary information such as land cover, precipitation, land surface temperature, NDVI for a growing season of corn in 2018 in Central Mexico (CM). The main objective is to evaluate a machine-learning based downscaling algorithm over an agricultural area with very limited in-situ observations of SM obtained during THExMEX-18. We found that overall, the downscaled moisture captured the dynamics during the growing season observed by the in-situ measurements.",

keywords = "Agriculture, Central Mexico, Machine learning, Passive microwave observations, SMAP downscaling",

author = "Hern{\'a}ndez-S{\'a}nchez, {Juan Carlos} and Alejandro Monsiv{\'a}is-Huertero and Jasmeet Judge and Jim{\'e}nez-Escalona, {Jos{\'e} Carlos}",

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year = "2019",

doi = "10.1109/IGARSS.2019.8900285",

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Hernández-Sánchez, JC, Monsiváis-Huertero, A, Judge, J & Jiménez-Escalona, JC 2019, 'Downscaling smap soil moisture retrievals over an agricultural region in central mexico using machine learning', Papel presentado en 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019, Yokohama, Japón, 28/07/19 - 2/08/19 pp. 7049-7052. https://doi.org/10.1109/IGARSS.2019.8900285

Downscaling smap soil moisture retrievals over an agricultural region in central mexico using machine learning. / Hernández-Sánchez, Juan Carlos; Monsiváis-Huertero, Alejandro; Judge, Jasmeet et al.
2019. 7049-7052 Papel presentado en 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019, Yokohama, Japón.

Producción científica: Contribución a una conferencia › Artículo › revisión exhaustiva

TY - CONF

T1 - Downscaling smap soil moisture retrievals over an agricultural region in central mexico using machine learning

AU - Hernández-Sánchez, Juan Carlos

AU - Monsiváis-Huertero, Alejandro

AU - Judge, Jasmeet

AU - Jiménez-Escalona, José Carlos

PY - 2019

Y1 - 2019

N2 - Soil moisture (SM) is an important land surface variable for understanding the water cycle, ecosystem productivity, and linkages between water-carbon cycles. For agricultural applications, SM information is needed at higher resolutions (about 1km). In this study, coarse-scale remotely sensed SM at 36 km from NASA-SMAP was disaggregated to 1 km using high resolution auxiliary information such as land cover, precipitation, land surface temperature, NDVI for a growing season of corn in 2018 in Central Mexico (CM). The main objective is to evaluate a machine-learning based downscaling algorithm over an agricultural area with very limited in-situ observations of SM obtained during THExMEX-18. We found that overall, the downscaled moisture captured the dynamics during the growing season observed by the in-situ measurements.

AB - Soil moisture (SM) is an important land surface variable for understanding the water cycle, ecosystem productivity, and linkages between water-carbon cycles. For agricultural applications, SM information is needed at higher resolutions (about 1km). In this study, coarse-scale remotely sensed SM at 36 km from NASA-SMAP was disaggregated to 1 km using high resolution auxiliary information such as land cover, precipitation, land surface temperature, NDVI for a growing season of corn in 2018 in Central Mexico (CM). The main objective is to evaluate a machine-learning based downscaling algorithm over an agricultural area with very limited in-situ observations of SM obtained during THExMEX-18. We found that overall, the downscaled moisture captured the dynamics during the growing season observed by the in-situ measurements.

KW - Agriculture

KW - Central Mexico

KW - Machine learning

KW - Passive microwave observations

KW - SMAP downscaling

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U2 - 10.1109/IGARSS.2019.8900285

DO - 10.1109/IGARSS.2019.8900285

M3 - Artículo

AN - SCOPUS:85084531392

SP - 7049

EP - 7052

T2 - 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019

Y2 - 28 July 2019 through 2 August 2019

ER -

Downscaling smap soil moisture retrievals over an agricultural region in central mexico using machine learning

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