Quantifying Irrigated Winter Wheat LAI in Argentina Using Multiple Sentinel-1 Incidence Angles
Synthetic aperture radar (SAR) data provides an appealing opportunity for all-weather day or night Earth surface monitoring. The European constellation Sentinel-1 (S1) consisting of S1-A and S1-B satellites offers a suitable revisit time and spatial resolution for the observation of croplands from s...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | info:ar-repo/semantics/artículo |
| Language: | Inglés |
| Published: |
MDPI
2022
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.12123/13525 https://www.mdpi.com/2072-4292/14/22/5867 https://doi.org/10.3390/rs14225867 |
| _version_ | 1855036953267798016 |
|---|---|
| author | Caballero, Gabriel Pezzola, Nestor Alejandro Winschel, Cristina Ines Casella, Alejandra An Sanchez Angonova, Paolo Andres Orden, Luciano Berger, Katja Verrelst, Jochem Delegido, Jesús |
| author_browse | Berger, Katja Caballero, Gabriel Casella, Alejandra An Delegido, Jesús Orden, Luciano Pezzola, Nestor Alejandro Sanchez Angonova, Paolo Andres Verrelst, Jochem Winschel, Cristina Ines |
| author_facet | Caballero, Gabriel Pezzola, Nestor Alejandro Winschel, Cristina Ines Casella, Alejandra An Sanchez Angonova, Paolo Andres Orden, Luciano Berger, Katja Verrelst, Jochem Delegido, Jesús |
| author_sort | Caballero, Gabriel |
| collection | INTA Digital |
| description | Synthetic aperture radar (SAR) data provides an appealing opportunity for all-weather day or night Earth surface monitoring. The European constellation Sentinel-1 (S1) consisting of S1-A and S1-B satellites offers a suitable revisit time and spatial resolution for the observation of croplands from space. The C-band radar backscatter is sensitive to vegetation structure changes and phenology as well as soil moisture and roughness. It also varies depending on the local incidence angle (LIA) of the SAR acquisition’s geometry. The LIA backscatter dependency could therefore be exploited to improve the retrieval of the crop biophysical variables. The availability of S1 radar time-series data at distinct observation angles holds the feasibility to retrieve leaf area index (LAI) evolution considering spatiotemporal coverage of intensively cultivated areas. Accordingly, this research presents a workflow merging multi-date S1 smoothed data acquired at distinct LIA with a Gaussian processes regression (GPR) and a cross-validation (CV) strategy to estimate cropland LAI of irrigated winter wheat. The GPR-S1-LAI model was tested against in situ data of the 2020 winter wheat campaign in the irrigated valley of Colorador river, South of Buenos Aires Province, Argentina. We achieved adequate validation results for LAI with R2CV = 0.67 and RMSECV = 0.88 m2 m−2. The trained model was further applied to a series of S1 stacked images, generating temporal LAI maps that well reflect the crop growth cycle. The robustness of the retrieval workflow is supported by the associated uncertainties along with the obtained maps. We found that processing S1 smoothed imagery with distinct acquisition geometries permits accurate radar-based LAI modeling throughout large irrigated areas and in consequence can support agricultural management practices in cloud-prone agri-environments. |
| format | info:ar-repo/semantics/artículo |
| id | INTA13525 |
| institution | Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina) |
| language | Inglés |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | INTA135252022-12-02T14:34:19Z Quantifying Irrigated Winter Wheat LAI in Argentina Using Multiple Sentinel-1 Incidence Angles Caballero, Gabriel Pezzola, Nestor Alejandro Winschel, Cristina Ines Casella, Alejandra An Sanchez Angonova, Paolo Andres Orden, Luciano Berger, Katja Verrelst, Jochem Delegido, Jesús Índice de Superficie Foliar Trigo Invierno Imágenes por Satélites Riego Leaf Area Index Wheat Winter Satellite Imagery Irrigation Sentinel-1 Synthetic aperture radar (SAR) data provides an appealing opportunity for all-weather day or night Earth surface monitoring. The European constellation Sentinel-1 (S1) consisting of S1-A and S1-B satellites offers a suitable revisit time and spatial resolution for the observation of croplands from space. The C-band radar backscatter is sensitive to vegetation structure changes and phenology as well as soil moisture and roughness. It also varies depending on the local incidence angle (LIA) of the SAR acquisition’s geometry. The LIA backscatter dependency could therefore be exploited to improve the retrieval of the crop biophysical variables. The availability of S1 radar time-series data at distinct observation angles holds the feasibility to retrieve leaf area index (LAI) evolution considering spatiotemporal coverage of intensively cultivated areas. Accordingly, this research presents a workflow merging multi-date S1 smoothed data acquired at distinct LIA with a Gaussian processes regression (GPR) and a cross-validation (CV) strategy to estimate cropland LAI of irrigated winter wheat. The GPR-S1-LAI model was tested against in situ data of the 2020 winter wheat campaign in the irrigated valley of Colorador river, South of Buenos Aires Province, Argentina. We achieved adequate validation results for LAI with R2CV = 0.67 and RMSECV = 0.88 m2 m−2. The trained model was further applied to a series of S1 stacked images, generating temporal LAI maps that well reflect the crop growth cycle. The robustness of the retrieval workflow is supported by the associated uncertainties along with the obtained maps. We found that processing S1 smoothed imagery with distinct acquisition geometries permits accurate radar-based LAI modeling throughout large irrigated areas and in consequence can support agricultural management practices in cloud-prone agri-environments. EEA Hilario Ascasubi Fil: Caballero, Gabriel. Technological University of Uruguay (UTEC). Agri-Environmental Engineering; Uruguay Fil: Caballero, Gabriel. University of Valencia. Image Processing Laboratory (IPL); España Fil: Pezzola, Alejandro. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; Argentina Fil: Winschel, Cristina Ines. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; Argentina Fil: Casella, Alejandra. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina Fil: Sanchez Angonova, Paolo Andres. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; Argentina Fil: Orden, Luciano. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; Argentina Fil: Orden, Luciano. Universidad Miguel Hernández. Centro de Investigación e Innovación Agroalimentaria y Agroambiental. GIAAMA Reseach Group; España Fil: Berger, Katja. University of Valencia. Image Processing Laboratory (IPL); España Fil: Berger, Katja. Mantle Labs GmbH; Austria Fil: Verrelst, Jochem. University of Valencia. Image Processing Laboratory (IPL); España Fil: Delegido, Jesús. Universidad de Valencia. Image Processing Laboratory (IPL); España 2022-12-02T14:29:02Z 2022-12-02T14:29:02Z 2022-11 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://hdl.handle.net/20.500.12123/13525 https://www.mdpi.com/2072-4292/14/22/5867 2072-4292 https://doi.org/10.3390/rs14225867 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 Argentina .......... (nation) (World, South America) 7006477 MDPI Remote Sensing 14 (22) : 5867. (November 2022) |
| spellingShingle | Índice de Superficie Foliar Trigo Invierno Imágenes por Satélites Riego Leaf Area Index Wheat Winter Satellite Imagery Irrigation Sentinel-1 Caballero, Gabriel Pezzola, Nestor Alejandro Winschel, Cristina Ines Casella, Alejandra An Sanchez Angonova, Paolo Andres Orden, Luciano Berger, Katja Verrelst, Jochem Delegido, Jesús Quantifying Irrigated Winter Wheat LAI in Argentina Using Multiple Sentinel-1 Incidence Angles |
| title | Quantifying Irrigated Winter Wheat LAI in Argentina Using Multiple Sentinel-1 Incidence Angles |
| title_full | Quantifying Irrigated Winter Wheat LAI in Argentina Using Multiple Sentinel-1 Incidence Angles |
| title_fullStr | Quantifying Irrigated Winter Wheat LAI in Argentina Using Multiple Sentinel-1 Incidence Angles |
| title_full_unstemmed | Quantifying Irrigated Winter Wheat LAI in Argentina Using Multiple Sentinel-1 Incidence Angles |
| title_short | Quantifying Irrigated Winter Wheat LAI in Argentina Using Multiple Sentinel-1 Incidence Angles |
| title_sort | quantifying irrigated winter wheat lai in argentina using multiple sentinel 1 incidence angles |
| topic | Índice de Superficie Foliar Trigo Invierno Imágenes por Satélites Riego Leaf Area Index Wheat Winter Satellite Imagery Irrigation Sentinel-1 |
| url | http://hdl.handle.net/20.500.12123/13525 https://www.mdpi.com/2072-4292/14/22/5867 https://doi.org/10.3390/rs14225867 |
| work_keys_str_mv | AT caballerogabriel quantifyingirrigatedwinterwheatlaiinargentinausingmultiplesentinel1incidenceangles AT pezzolanestoralejandro quantifyingirrigatedwinterwheatlaiinargentinausingmultiplesentinel1incidenceangles AT winschelcristinaines quantifyingirrigatedwinterwheatlaiinargentinausingmultiplesentinel1incidenceangles AT casellaalejandraan quantifyingirrigatedwinterwheatlaiinargentinausingmultiplesentinel1incidenceangles AT sanchezangonovapaoloandres quantifyingirrigatedwinterwheatlaiinargentinausingmultiplesentinel1incidenceangles AT ordenluciano quantifyingirrigatedwinterwheatlaiinargentinausingmultiplesentinel1incidenceangles AT bergerkatja quantifyingirrigatedwinterwheatlaiinargentinausingmultiplesentinel1incidenceangles AT verrelstjochem quantifyingirrigatedwinterwheatlaiinargentinausingmultiplesentinel1incidenceangles AT delegidojesus quantifyingirrigatedwinterwheatlaiinargentinausingmultiplesentinel1incidenceangles |