| 000 | 03110 am a22003013u 4500 | ||
|---|---|---|---|
| 042 | _adc | ||
| 100 | 1 | 0 |
_aCaballero, Gabriel _eauthor _92182 |
| 700 | 1 | 0 |
_aPezzola, Alejandro _eauthor _92183 |
| 700 | 1 | 0 |
_aWinschel, Cristina _eauthor _92184 |
| 700 | 1 | 0 |
_aCasella, Alejandra _eauthor _92185 |
| 700 | 1 | 0 |
_aAngonova, Paolo Sanchez _eauthor _92186 |
| 700 | 1 | 0 |
_aOrden, Luciano _eauthor _92187 |
| 700 | 1 | 0 |
_aBerger, Katja _eauthor _91301 |
| 700 | 1 | 0 |
_aVerrelst, Jochem _eauthor |
| 700 | 1 | 0 |
_aDelegido, Jesús _eauthor _92188 |
| 245 | 0 | 0 | _aQuantifying Irrigated Winter Wheat LAI in Argentina Using Multiple Sentinel-1 Incidence Angles |
| 260 | _c2022-11-19. | ||
| 500 | _a/pmc/articles/PMC7614051/ | ||
| 500 | _a/pubmed/36644377 | ||
| 520 | _aSynthetic 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 [Formula: see text] and RMSE(CV) = 0.88 m(2) 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 cloudprone agri-environments. | ||
| 540 | _a | ||
| 540 | _ahttps://creativecommons.org/licenses/by/4.0/This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Licensee MDPI, Basel, Switzerland. | ||
| 546 | _aen | ||
| 690 | _aArticle | ||
| 655 | 7 |
_aText _2local |
|
| 786 | 0 | _nRemote Sens (Basel) | |
| 856 | 4 | 1 |
_uhttp://dx.doi.org/10.3390/rs14225867 _zConnect to this object online. |
| 999 |
_c2031 _d2031 |
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