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Descripción
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| The availability of high frequency remote sensing time series allows the assessment of crop evolution at different scales. The Spectral Shape Indexes (SSI) represent the shape of a multispectral (MODIS in this case) reflectance profile in a set of three consecutive bands. The aims of this research were: (1) to assess AS1 and AS2 behavior over a cotton crop growing period, (2) to test whether function fitting procedures can be used to model MODIS AS1 and AS2 and NDVI time series and (3) to derive objective AS1 and AS2 phenological metrics that can be used to monitor cotton phenological stages. Phenological stages of a cotton crop in the San Joaquin Valley (CA) were identified by linking local climatic data and producer recorded management practices. An asymmetric Gaussian function was fitted to the time series of each index using TIMESAT software. Then, specific dates in the fitted functions have been related to phenological stages and dates of agricultural practices. Results show that AS1 and AS2 exhibit a consistent pattern clearly different from the temporal evolution of NDVI. The AS2 index shows high values during periods when vegetation is dominant, either photosynthetically active or dry, and lowvalueswhen soil dominates the pixel. The AS1 index time series showed two minima during the cotton growth period.Minima and inflection points derived from the fitted functions are coincident in time with significant crop management dates during the growing period. These results show that function fitting procedures applied to AS1 and AS2 can be used to derive phenological metrics, illustrating the potential for using Spectral Shape Indexes for crop monitoring. | |
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Internacional
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JCR del ISI
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Título de la revista
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Remote Sensing of Environment |
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ISSN
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0034-4257 |
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Factor de impacto JCR
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5,103 |
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Información de impacto
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Volumen
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DOI
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http://dx.doi.org/10.1016/j.rse.2012.08.002 |
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Número de revista
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126 (2012) |
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Desde la página
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148 |
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Hasta la página
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159 |
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Mes
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SIN MES |
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Ranking
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nº 9 en ENVIRONMENTAL SCIENCES nº 1 en IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY nº 1 en REMOTE SENSING |