Mapping of Cotton Fields Within-Season Using Phenology-Based Metrics Derived from a Time Series of Landsat Imagery

Al-Shammari, Dhahi; Fuentes, Ignacio; Whelan, Brett; Filippi, Patrick; Bishop, Thomas

doi:10.3390/rs12183038

Cited by 26 publications

(12 citation statements)

References 54 publications

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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%

“…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%

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Cotton Classification Method at the County Scale Based on Multi-Features and Random Forest Feature Selection Algorithm and Classifier

et al. 2022

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Accurate cotton maps are crucial for monitoring cotton growth and precision management. The paper proposed a county-scale cotton mapping method by using random forest (RF) feature selection algorithm and classifier based on selecting multi-features, including spectral, vegetation indices, and texture features. The contribution of texture features to cotton classification accuracy was also explored in addition to spectral features and vegetation index. In addition, the optimal classification time, feature importance, and the best classifier on the cotton extraction accuracy were evaluated. The results showed that the texture feature named the gray level co-occurrence matrix (GLCM) is effective for improving classification accuracy, ranking second in contribution among all studied spectral, VI, and texture features. Among the three classifiers, the RF showed higher accuracy and better stability than support vector machines (SVM) and artificial neural networks (ANN). The average overall accuracy (OA) of the classification combining multiple features was 93.36%, 7.33% higher than the average OA of the single-time spectrum, and 2.05% higher than the average OA of the multi-time spectrum. The classification accuracy after feature selection by RF can still reach 92.12%, showing high accuracy and efficiency. Combining multiple features and random forest methods may be a promising county-scale cotton classification method.

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Section: Author Study Area Smallest Unit Classifier Satellitementioning

confidence: 99%

Section: Implications and Improvementsmentioning

confidence: 99%

Cotton Classification Method at the County Scale Based on Multi-Features and Random Forest Feature Selection Algorithm and Classifier

et al. 2022

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“…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%

Evaluating the Impact of Crop Layout Changes on N and P Nutrient Balance: A Case Study in the West Liaohe River Basin, China

2021

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Regional crop layout has changed significantly due to climate, policy, and other factors, which has impacted farmland nutrient balance. Here, we evaluated the impact of crop layout changes on N and P nutrient balance in the West Liaohe River Basin from 2000–2015. The study area has long been in a N and P surplus state. The unit N surplus exhibited a downward trend and the unit P surplus showed an increasing trend. Significant correlations existed between planting areas and nutrient surplus. The N and P surplus layout was mainly concentrated in the West Liaohe River lower reaches basin. The planting area of wheat must be reduced and the areas of maize and soybean must be controlled to adjust the N and P balance and reduce the environmental pollution risk. Chemical fertilizer and seed inputs are the main sources of N input. Furthermore, combining farming and pastoral farming is conducive to improving N and P use efficiency. Manure can be absorbed by farmland, the ratio of organic and chemical fertilizers can be reasonably set, and chemical fertilizer application can be reduced.

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“…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%

Augmentation of Vegetation Index Curves Considering the Crop-Specific Phenological Characteristics

Arun

Karnieli

2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The state-of-the-art crop phenological classifiers use vegetation index (VI) time-series data and deep learning (DL) techniques. However, the scarcity of training samples limits the performance of these approaches. Unlike the conventional augmentation techniques, the data augmentation of VI curves should preserve the crop-specific phenological events. The DLbased augmentation approaches do not give good results when the training samples are limited. Also, the conventional approaches such as translation, rotation, scaling, and wrapping do not preserve the characteristic features of the index curves, thereby making them inappropriate for the VI-curve-based augmentations. This study proposes a non-DL-based data augmentation strategy that requires only a minimal number of actual training samples. In the proposed approach, the periodic phenological events and the underlying trend for each crop class are modeled to improve the augmentation. The trends of different crop classes are estimated by jointly maximizing the autocorrelation and variance, while the optimal subsequences are generalized as the phenological events. The proposed augmentation strategy of using Maximal Overlap Discrete Wavelet Transform (MODWT) for obtaining the surrogates that retain the crop-specific features and periodicities significantly improves the results. It may be noted that the proposed approach does not alter the wavelet coefficients that are characteristics of a given crop class. The experiments using time series VI data, covering 90 fields of wheat and 60 fields of barley, confirm better accuracy of the proposed augmentation approaches as compared to the prominent approaches.

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Mapping of Cotton Fields Within-Season Using Phenology-Based Metrics Derived from a Time Series of Landsat Imagery

Cited by 26 publications

References 54 publications

Cotton Classification Method at the County Scale Based on Multi-Features and Random Forest Feature Selection Algorithm and Classifier

Cotton Classification Method at the County Scale Based on Multi-Features and Random Forest Feature Selection Algorithm and Classifier

Evaluating the Impact of Crop Layout Changes on N and P Nutrient Balance: A Case Study in the West Liaohe River Basin, China

Augmentation of Vegetation Index Curves Considering the Crop-Specific Phenological Characteristics

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