Calibration of volumetric soil moisture using Landsat-8 and Sentinel-2 satellite imagery by Google Earth Engine
Water scarcity for agriculture is increasingly evident due to climatic alterations and inadequate management of this resource. Therefore, developing digital models that help improve water resource management to provide solutions to agronomic problems in northern Mexico is necessary. In this context,...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Universitat Politécnica de Valencia
2023-07-01
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| Series: | Revista de Teledetección |
| Subjects: | |
| Online Access: | https://polipapers.upv.es/index.php/raet/article/view/19368 |
| _version_ | 1797770489596215296 |
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| author | José Rodolfo Quintana-Molina Ignacio Sánchez-Cohen Sergio Iván Jiménez-Jiménez Mariana de Jesús Marcial-Pablo Ricardo Trejo-Calzada Emilio Quintana-Molina |
| author_facet | José Rodolfo Quintana-Molina Ignacio Sánchez-Cohen Sergio Iván Jiménez-Jiménez Mariana de Jesús Marcial-Pablo Ricardo Trejo-Calzada Emilio Quintana-Molina |
| author_sort | José Rodolfo Quintana-Molina |
| collection | DOAJ |
| description | Water scarcity for agriculture is increasingly evident due to climatic alterations and inadequate management of this resource. Therefore, developing digital models that help improve water resource management to provide solutions to agronomic problems in northern Mexico is necessary. In this context, the objective of the present research is to calibrate the Optical Trapezoid (OPTRAM) and Thermal-Optical Trapezoid (TOTRAM) models to estimate the volumetric soil moisture at different depths through vegetation indices derived from Landsat-8 and Sentinel-2 satellite images using Google Earth Engine (GEE). Agricultural areas under gravity irrigation and rainfed runoff in the Comarca Lagunera, the lower part of the Hydrological Region No. 36 of the Nazas and Aguanaval rivers were selected for in-situ measurements. The OPTRAM and TOTRAM normalized moisture content (W) estimates were compared with in-situ volumetric soil moisture (Ɵ) data. Results indicate that the predictions of OPTRAM errors using Sentinel-2 images showed RMSE between 0.033 to 0.043 cm3 cm-3 and R2 between 0.66 to 0.75, whereas Landsat-8 errors showed RSME from 0.036 to from 0.036 to 0.057 cm3 cm-3 and R2 between 0.70 to 0.81. On the other hand, TOTRAM errors showed RMSE between 0.045 to 0.053 cm3 cm-3 and R2 between 0.62 to 0.85 through calibrations. This study made it possible to evaluate the most accurate combinations of the pixel distributions of each model and vegetation indices for the estimation of volumetric soil moisture within the different phenological stages of the crops. |
| first_indexed | 2024-03-12T21:23:58Z |
| format | Article |
| id | doaj.art-ba5386aedf4f4befac206db5ee30a78c |
| institution | Directory Open Access Journal |
| issn | 1133-0953 1988-8740 |
| language | English |
| last_indexed | 2024-03-12T21:23:58Z |
| publishDate | 2023-07-01 |
| publisher | Universitat Politécnica de Valencia |
| record_format | Article |
| series | Revista de Teledetección |
| spelling | doaj.art-ba5386aedf4f4befac206db5ee30a78c2023-07-28T12:04:39ZengUniversitat Politécnica de ValenciaRevista de Teledetección1133-09531988-87402023-07-0162213810.4995/raet.2023.1936818560Calibration of volumetric soil moisture using Landsat-8 and Sentinel-2 satellite imagery by Google Earth EngineJosé Rodolfo Quintana-Molina0https://orcid.org/0000-0003-1654-4849Ignacio Sánchez-Cohen1https://orcid.org/0000-0002-9063-7114Sergio Iván Jiménez-Jiménez2https://orcid.org/0000-0001-9776-475XMariana de Jesús Marcial-Pablo3https://orcid.org/0000-0002-4921-7492Ricardo Trejo-Calzada4https://orcid.org/0000-0003-1670-7847Emilio Quintana-Molina5https://orcid.org/0000-0001-8930-8519Chapingo Autonomous UniversityINIFAP-CENID RASPA Centro Nacional de Investigación Disciplinaria en Relación Agua-Suelo-Planta-AtmósferaINIFAP-CENID RASPA Centro Nacional de Investigación Disciplinaria en Relación Agua-Suelo-Planta-AtmósferaINIFAP-CENID RASPA Centro Nacional de Investigación Disciplinaria en Relación Agua-Suelo-Planta-AtmósferaChapingo Autonomous University Wageningen University & Research Water scarcity for agriculture is increasingly evident due to climatic alterations and inadequate management of this resource. Therefore, developing digital models that help improve water resource management to provide solutions to agronomic problems in northern Mexico is necessary. In this context, the objective of the present research is to calibrate the Optical Trapezoid (OPTRAM) and Thermal-Optical Trapezoid (TOTRAM) models to estimate the volumetric soil moisture at different depths through vegetation indices derived from Landsat-8 and Sentinel-2 satellite images using Google Earth Engine (GEE). Agricultural areas under gravity irrigation and rainfed runoff in the Comarca Lagunera, the lower part of the Hydrological Region No. 36 of the Nazas and Aguanaval rivers were selected for in-situ measurements. The OPTRAM and TOTRAM normalized moisture content (W) estimates were compared with in-situ volumetric soil moisture (Ɵ) data. Results indicate that the predictions of OPTRAM errors using Sentinel-2 images showed RMSE between 0.033 to 0.043 cm3 cm-3 and R2 between 0.66 to 0.75, whereas Landsat-8 errors showed RSME from 0.036 to from 0.036 to 0.057 cm3 cm-3 and R2 between 0.70 to 0.81. On the other hand, TOTRAM errors showed RMSE between 0.045 to 0.053 cm3 cm-3 and R2 between 0.62 to 0.85 through calibrations. This study made it possible to evaluate the most accurate combinations of the pixel distributions of each model and vegetation indices for the estimation of volumetric soil moisture within the different phenological stages of the crops.https://polipapers.upv.es/index.php/raet/article/view/19368satellite imagesmodelsvegetation indicespixel distributions |
| spellingShingle | José Rodolfo Quintana-Molina Ignacio Sánchez-Cohen Sergio Iván Jiménez-Jiménez Mariana de Jesús Marcial-Pablo Ricardo Trejo-Calzada Emilio Quintana-Molina Calibration of volumetric soil moisture using Landsat-8 and Sentinel-2 satellite imagery by Google Earth Engine Revista de Teledetección satellite images models vegetation indices pixel distributions |
| title | Calibration of volumetric soil moisture using Landsat-8 and Sentinel-2 satellite imagery by Google Earth Engine |
| title_full | Calibration of volumetric soil moisture using Landsat-8 and Sentinel-2 satellite imagery by Google Earth Engine |
| title_fullStr | Calibration of volumetric soil moisture using Landsat-8 and Sentinel-2 satellite imagery by Google Earth Engine |
| title_full_unstemmed | Calibration of volumetric soil moisture using Landsat-8 and Sentinel-2 satellite imagery by Google Earth Engine |
| title_short | Calibration of volumetric soil moisture using Landsat-8 and Sentinel-2 satellite imagery by Google Earth Engine |
| title_sort | calibration of volumetric soil moisture using landsat 8 and sentinel 2 satellite imagery by google earth engine |
| topic | satellite images models vegetation indices pixel distributions |
| url | https://polipapers.upv.es/index.php/raet/article/view/19368 |
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