2020
DOI: 10.3390/rs12183038
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Mapping of Cotton Fields Within-Season Using Phenology-Based Metrics Derived from a Time Series of Landsat Imagery

Abstract: A phenology-based crop type mapping approach was carried out to map cotton fields throughout the cotton-growing areas of eastern Australia. The workflow was implemented in the Google Earth Engine (GEE) platform, as it is time efficient and does not require processing in multiple platforms to complete the classification steps. A time series of Normalised Difference Vegetation Index (NDVI) imagery were generated from Landsat 8 Surface Reflectance Tier 1 (L8SR) and processed using Fourier transformation. This was… Show more

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Cited by 26 publications
(12 citation statements)
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References 54 publications
(67 reference statements)
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“…To further improve the classification accuracy, the various features of the image, such as spectral features, temporal features, texture features, digital elevation models (DEM), and other features, can be added to the classifier [38]. Al-Shammari et al [39] added the magnitude and phase features of the NDVI time series to the classification features, which improved the cotton extraction accuracy. In the existing classification research, texture features are often used in object-based image analysis (OBIA) [16,40,41], but they are rarely used in pixel-based image analysis.…”
Section: Author Study Area Smallest Unit Classifier Satellitementioning
confidence: 99%
See 1 more Smart Citation
“…To further improve the classification accuracy, the various features of the image, such as spectral features, temporal features, texture features, digital elevation models (DEM), and other features, can be added to the classifier [38]. Al-Shammari et al [39] added the magnitude and phase features of the NDVI time series to the classification features, which improved the cotton extraction accuracy. In the existing classification research, texture features are often used in object-based image analysis (OBIA) [16,40,41], but they are rarely used in pixel-based image analysis.…”
Section: Author Study Area Smallest Unit Classifier Satellitementioning
confidence: 99%
“…Previous studies have developed a series of crop classification and mapping approaches based on remote sensing images [3,4,8,12,14,18]. Many studies have focused on the acquisition period of satellite images and the performance comparison of classifiers [14,33,39,40,47]. However, in agricultural remote sensing research, the number of bands of multi-spectral imagery is limited, and the temporal and spatial resolution is low.…”
Section: Implications and Improvementsmentioning
confidence: 99%
“…The NH 3 volatilization loss rate in fertilizers was 25% and the NO 2 volatilization loss rate in fertilizers was 1.05%. The N leaching and runoff were estimated using export coefficients [53]. The leaching output rates were 16.0 kg N ha −1 yr −1 .…”
Section: N and P Input-output And Parametersmentioning
confidence: 99%
“…The proposed strategy of using the prior information derived from trend and seasonality to refine the transform coefficients accurately preserves the cop-specific features [54], [59]. Unlike the existing approaches that require a lot of actual training samples, the proposed approaches give good results even when actual training samples are pretty scarce [9], [65]. The generalization capability of the proposed approach can be attributed to the non-DL-based strategy of using entropy and transforms to model the characteristic features.…”
Section: B Improvement In Classification Resultsmentioning
confidence: 99%