NDVI Response to Satellite-Estimated Antecedent Precipitation in Dryland Pastures
Precipitation is a critical driver of vegetation productivity and dynamics in dryland environments, especially in areas with intense livestock farming. Availability and access to accurate, reliable, and timely rainfall data are essential for natural resources management, environmental monitoring, an...
| Autores principales: | , , , , |
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| Formato: | Artículo |
| Lenguaje: | Inglés |
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MDPI
2024
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| Materias: | |
| Acceso en línea: | http://hdl.handle.net/20.500.12123/16440 https://www.mdpi.com/2072-4292/15/14/3615 https://doi.org/10.3390/rs15143615 |
| _version_ | 1855485762320990208 |
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| author | Brieva, Carlos Alberto Saco, Patricia M. Sandi, Steven G. Mora, Sebastian Rodríguez, José F. |
| author_browse | Brieva, Carlos Alberto Mora, Sebastian Rodríguez, José F. Saco, Patricia M. Sandi, Steven G. |
| author_facet | Brieva, Carlos Alberto Saco, Patricia M. Sandi, Steven G. Mora, Sebastian Rodríguez, José F. |
| author_sort | Brieva, Carlos Alberto |
| collection | INTA Digital |
| description | Precipitation is a critical driver of vegetation productivity and dynamics in dryland environments, especially in areas with intense livestock farming. Availability and access to accurate, reliable, and timely rainfall data are essential for natural resources management, environmental monitoring, and informing hydrological rainfall-runoff models. Gauged precipitation data in drylands are often scarce, fragmented, and with low spatial resolution; therefore, satellite-estimated precipitation becomes a valuable dataset for overcoming this constraint. Using statistical indices, we compared satellite-derived precipitation data from four products (CHIRPS, GPM, TRMM, and PERSIANN-CDR) against gauged data at different temporal scales (daily, monthly, and yearly). Spatial correlations were calculated for GPM and CHIRPS estimates against interpolated gauged precipitation. We then estimated NDVI response to Antecedent Accumulated Precipitation (AAP) for 1, 3, 6, 9, and 12 months of four major vegetation types typical of the region. Statistical metrics varied with temporal scales being highest and acceptable for periods of 1 month or 1 year. At monthly scale GPM presented the best Pearson’s Correlation Coefficient (r), Root Mean Square Error (RMSE) and RMSE-observations standard deviation ratio (RSR) and CHIRPS resulted in lower Mean Error (ME) and Bias. On an annual basis CHIRPS showed the best adjustment for all indicators except for r. NDVI responses to 3 months of AAP were significant for all vegetation types in the study area. The findings of this study show that estimated precipitation data from GPM and CHIRPS satellites are accurate and valuable as a tool for analysing the relationships between precipitation and vegetation in the drylands of Mendoza |
| format | Artículo |
| id | INTA16440 |
| institution | Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina) |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | INTA164402024-01-03T14:28:59Z NDVI Response to Satellite-Estimated Antecedent Precipitation in Dryland Pastures Brieva, Carlos Alberto Saco, Patricia M. Sandi, Steven G. Mora, Sebastian Rodríguez, José F. Pastizales Teledetección Indice Normalizado Diferencial de la Vegetación Pastures Remote Sensing Normalized Difference Vegetation Index NDVI Precipitation is a critical driver of vegetation productivity and dynamics in dryland environments, especially in areas with intense livestock farming. Availability and access to accurate, reliable, and timely rainfall data are essential for natural resources management, environmental monitoring, and informing hydrological rainfall-runoff models. Gauged precipitation data in drylands are often scarce, fragmented, and with low spatial resolution; therefore, satellite-estimated precipitation becomes a valuable dataset for overcoming this constraint. Using statistical indices, we compared satellite-derived precipitation data from four products (CHIRPS, GPM, TRMM, and PERSIANN-CDR) against gauged data at different temporal scales (daily, monthly, and yearly). Spatial correlations were calculated for GPM and CHIRPS estimates against interpolated gauged precipitation. We then estimated NDVI response to Antecedent Accumulated Precipitation (AAP) for 1, 3, 6, 9, and 12 months of four major vegetation types typical of the region. Statistical metrics varied with temporal scales being highest and acceptable for periods of 1 month or 1 year. At monthly scale GPM presented the best Pearson’s Correlation Coefficient (r), Root Mean Square Error (RMSE) and RMSE-observations standard deviation ratio (RSR) and CHIRPS resulted in lower Mean Error (ME) and Bias. On an annual basis CHIRPS showed the best adjustment for all indicators except for r. NDVI responses to 3 months of AAP were significant for all vegetation types in the study area. The findings of this study show that estimated precipitation data from GPM and CHIRPS satellites are accurate and valuable as a tool for analysing the relationships between precipitation and vegetation in the drylands of Mendoza EEA Rama Caída Fil: Brieva, Carlos. University of Newcastle. School of Engineering. Centre for Water Security and Environmental Sustainability; Australia Fil: Brieva, Carlos. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Rama Caída; Argentina Fil: Saco, Patricia, M. University of Newcastle. School of Engineering. Centre for Water Security and Environmental Sustainability; Australia Fil: Sandi, Steven G. University of Newcastle. School of Engineering. Centre for Water Security and Environmental Sustainability; Australia Fil: Sandi, Steven G. Deakin University. School of Engineering; Australia Fil: Mora, Sebastián. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Rama Caída; Argentina Fil: Rodríguez, José F. University of Newcastle. School of Engineering. Centre for Water Security and Environmental Sustainability; Australia 2024-01-03T14:11:56Z 2024-01-03T14:11:56Z 2023-07 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://hdl.handle.net/20.500.12123/16440 https://www.mdpi.com/2072-4292/15/14/3615 2072-4292 https://doi.org/10.3390/rs15143615 eng info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) application/pdf MDPI Remote Sensing 15 (14) : 3615 (July 2023) |
| spellingShingle | Pastizales Teledetección Indice Normalizado Diferencial de la Vegetación Pastures Remote Sensing Normalized Difference Vegetation Index NDVI Brieva, Carlos Alberto Saco, Patricia M. Sandi, Steven G. Mora, Sebastian Rodríguez, José F. NDVI Response to Satellite-Estimated Antecedent Precipitation in Dryland Pastures |
| title | NDVI Response to Satellite-Estimated Antecedent Precipitation in Dryland Pastures |
| title_full | NDVI Response to Satellite-Estimated Antecedent Precipitation in Dryland Pastures |
| title_fullStr | NDVI Response to Satellite-Estimated Antecedent Precipitation in Dryland Pastures |
| title_full_unstemmed | NDVI Response to Satellite-Estimated Antecedent Precipitation in Dryland Pastures |
| title_short | NDVI Response to Satellite-Estimated Antecedent Precipitation in Dryland Pastures |
| title_sort | ndvi response to satellite estimated antecedent precipitation in dryland pastures |
| topic | Pastizales Teledetección Indice Normalizado Diferencial de la Vegetación Pastures Remote Sensing Normalized Difference Vegetation Index NDVI |
| url | http://hdl.handle.net/20.500.12123/16440 https://www.mdpi.com/2072-4292/15/14/3615 https://doi.org/10.3390/rs15143615 |
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