Retrieval of phenological stages of onion fields during the first year of growth by means of C-band polarimetric SAR measurements

Mascolo, Lucio; López-Sánchez, Juan M.; Vicente-Guijalba, Fernando; Mazzarella, Giuseppe; Nunziata, Ferdinando; Migliaccio, M.

doi:10.1080/01431161.2015.1055608

Cited by 18 publications

(7 citation statements)

References 24 publications

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“…The set S k consists of those stages associated with non-zero probabilities, as shown in (14). Such a set is bounded between x k low and x k upp , with…”

Section: State Space S Kmentioning

confidence: 99%

“…In this study, S1 data are exploited for real time estimation of phenological stages of agricultural crops. In the last decade, several approaches [6,[10][11][12][13][14][15][16][17][18][19][20][21][22][23] have been proposed to estimate crop phenology with SAR data. The approaches in [10][11][12][13][14][15][16][17][18] apply classification methods (based on hierarchical trees, decision planes, the Wishart statistics and machine learning algorithms) on the PolSAR features to classify phenological intervals.…”

Section: Introductionmentioning

confidence: 99%

“…In the last decade, several approaches [6,[10][11][12][13][14][15][16][17][18][19][20][21][22][23] have been proposed to estimate crop phenology with SAR data. The approaches in [10][11][12][13][14][15][16][17][18] apply classification methods (based on hierarchical trees, decision planes, the Wishart statistics and machine learning algorithms) on the PolSAR features to classify phenological intervals. Here, the classified phenology at the current date is based only on the current image, without accounting for the temporal evolution of the crops.…”

Section: Introductionmentioning

confidence: 99%

“…Note that, in both cases, S k , i.e., the set of possible stages at time t k , is also annotated. This is determined using (14), and results in S k = {1, • • • , 13} and S k = {53, • • • , 73}, for the first and the second case, respectively.…”

mentioning

confidence: 99%

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Optimal Grid-Based Filtering for Crop Phenology Estimation with Sentinel-1 SAR Data

Mascolo¹,

Martínez-Marín²,

López-Sánchez³

2021

Remote Sensing

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In the last decade, suboptimal Bayesian filtering (BF) techniques, such as Extended Kalman Filtering (EKF) and Particle Filtering (PF), have led to great interest for crop phenology monitoring with Synthetic Aperture Radar (SAR) data. In this study, a novel approach, based on the Grid-Based Filter (GBF), is proposed to estimate crop phenology. Here, phenological scales, which consist of a finite number of discrete stages, represent the one-dimensional state space, and hence GBF provides the optimal phenology estimates. Accordingly, contrarily to literature studies based on EKF and PF, no constraints are imposed on the models and the statistical distributions involved. The prediction model is defined by the transition matrix, while Kernel Density Estimation (KDE) is employed to define the observation model. The approach is applied on dense time series of dual-polarization Sentinel-1 (S1) SAR images, collected in four different years, to estimate the BBCH stages of rice crops. Results show that 0.94 ≤ R2 ≤ 0.98, 5.37 ≤ RMSE ≤ 7.9 and 20 ≤ MAE ≤ 33.

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“…The set S k consists of those stages associated with non-zero probabilities, as shown in (14). Such a set is bounded between x k low and x k upp , with…”

Section: State Space S Kmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Optimal Grid-Based Filtering for Crop Phenology Estimation with Sentinel-1 SAR Data

Mascolo¹,

Martínez-Marín²,

López-Sánchez³

2021

Remote Sensing

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“…Such a times series is achieved by combining seven different beams (i.e., different orbit passes and different incidence angles) acquired in the framework of the European Space Agency (ESA) funded Agricultural bio/geophysical retrieval from frequent repeat pass SAR and optical imaging (AgriSAR) campaign, conducted in that area in 2009, which provided ground information about the crop phenological stages. Note that the same SAR and ground data have been used in [6], [11], and [12] for phenology retrieval and in [22] to analyze the sensitivity of the backscattering coefficients to crop growth stages and soil conditions. In this paper, four different crop types are analyzed: oat, barley, wheat, and corn.…”

mentioning

confidence: 99%

A Complete Procedure for Crop Phenology Estimation With PolSAR Data Based on the Complex Wishart Classifier

Mascolo

López-Sánchez

Vicente-Guijalba

et al. 2016

IEEE Trans. Geosci. Remote Sensing

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Abstract-A new methodology to estimate the growth stages of agricultural crops using the time series of polarimetric synthetic aperture radar (PolSAR) images is proposed. The methodology is based on the complex Wishart classifier and both phenological intervals and training areas are identified measuring the distances among polarimetric covariance matrices obtained from the time series of PolSAR data. Consequently, the computation of PolSAR features, which is the main step of state-of-the-art methods, is no longer needed, and the proposed approach can be applied in the same way to any crop type. Experiments undertaken on a dense time series of fully polarimetric C-band RADARSAT-2 images, collected at incidence angles ranging from 23• to 39• , in ascending/descending orbit passes, demonstrate that the proposed methodology can be successfully applied to retrieve the phenological stages of four different crop types. In addition, the effect of combining beams corresponding to different sensor's configurations has been evaluated, showing that it affects the retrieval accuracies. Validation with ground data shows the following: overall accuracy is between 54% and 86%; producer's accuracy (PA) and user's accuracy (UA) range between 21% and 100% and between 33% and 100%, respectively.

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Generating Virtual Training Labels for Crop Classification from Fused Sentinel-1 and Sentinel-2 Time Series

Teimouri,

Mokhtarzade,

Baghdadi

et al. 2023

PFG

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Convolutional neural networks (CNNs) have shown results superior to most traditional image understanding approaches in many fields, incl. crop classification from satellite time series images. However, CNNs require a large number of training samples to properly train the network. The process of collecting and labeling such samples using traditional methods can be both, time-consuming and costly. To address this issue and improve classification accuracy, generating virtual training labels (VTL) from existing ones is a promising solution. To this end, this study proposes a novel method for generating VTL based on sub-dividing the training samples of each crop using self-organizing maps (SOM), and then assigning labels to a set of unlabeled pixels based on the distance to these sub-classes. We apply the new method to crop classification from Sentinel images. A three-dimensional (3D) CNN is utilized for extracting features from the fusion of optical and radar time series. The results of the evaluation show that the proposed method is effective in generating VTL, as demonstrated by the achieved overall accuracy (OA) of 95.3% and kappa coefficient (KC) of 94.5%, compared to 91.3% and 89.9% for a solution without VTL. The results suggest that the proposed method has the potential to enhance the classification accuracy of crops using VTL.

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Retrieval of phenological stages of onion fields during the first year of growth by means of C-band polarimetric SAR measurements

Cited by 18 publications

References 24 publications

Optimal Grid-Based Filtering for Crop Phenology Estimation with Sentinel-1 SAR Data

Optimal Grid-Based Filtering for Crop Phenology Estimation with Sentinel-1 SAR Data

A Complete Procedure for Crop Phenology Estimation With PolSAR Data Based on the Complex Wishart Classifier

Generating Virtual Training Labels for Crop Classification from Fused Sentinel-1 and Sentinel-2 Time Series

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