The efficiency of crop recognition on ENVISAT ASAR images in two growing seasons

Stankiewicz, K.

doi:10.1109/tgrs.2006.864380

Cited by 45 publications

(27 citation statements)

References 4 publications

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“…Although previous studies demonstrated the benefits of quad-pol data compared to single or dual-pol configurations for crop classification when just one acquisition date was available [22,42], when multi-temporal configurations were tested these differences were minor [8,43]. In fact, some studies [44,45] reported high classification accuracies with just three scenes acquired in key dates in dual-pol configurations, in coincidence with our results. Therefore, it seems that rather than a single-date quad-pol dataset, a multi-date dual-pol option provides enhanced crop classification accuracies.…”

Section: Discussionsupporting

confidence: 87%

On the Added Value of Quad-Pol Data in a Multi-Temporal Crop Classification Framework Based on RADARSAT-2 Imagery

Larrañaga

Álvarez‐Mozos

2016

Remote Sensing

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Polarimetric SAR images are a rich data source for crop mapping. However, quad-pol sensors have some limitations due to their complexity, increased data rate, and reduced coverage and revisit time. The main objective of this study was to evaluate the added value of quad-pol data in a multi-temporal crop classification framework based on SAR imagery. With this aim, three RADARSAT-2 scenes were acquired between May and June 2010. Once we analyzed the separability and the descriptive analysis of the features, an object-based supervised classification was performed using the Random Forests classification algorithm. Classification results obtained with dual-pol (VV-VH) data as input were compared to those using quad-pol data in different polarization bases (linear H-V, circular, and linear 45˝), and also to configurations where several polarimetric features (Pauli and Cloude-Pottier decomposition features and co-pol coherence and phase difference) were added. Dual-pol data obtained satisfactory results, equal to those obtained with quad-pol data (in H-V basis) in terms of overall accuracy (0.79) and Kappa values (0.69). Quad-pol data in circular and linear 45˝bases resulted in lower accuracies. The inclusion of polarimetric features, particularly co-pol coherence and phase difference, resulted in enhanced classification accuracies with an overall accuracy of 0.86 and Kappa of 0.79 in the best case, when all the polarimetric features were added. Improvements were also observed in the identification of some particular crops, but major crops like cereals, rapeseed, and sunflower already achieved a satisfactory accuracy with the VV-VH dual-pol configuration and obtained only minor improvements. Therefore, it can be concluded that C-band VV-VH dual-pol data is almost ready to be used operationally for crop mapping as long as at least three acquisitions in dates reflecting key growth stages representing typical phenology differences of the present crops are available. In the near future, issues regarding the classification of crops with small field sizes and heterogeneous cover (i.e., fallow and grasslands) need to be tackled to make this application fully operational.

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Section: Discussionsupporting

confidence: 87%

On the Added Value of Quad-Pol Data in a Multi-Temporal Crop Classification Framework Based on RADARSAT-2 Imagery

Larrañaga

Álvarez‐Mozos

2016

Remote Sensing

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“…This configuration allowed us to exploit the dataset not only for X-band intensity calculation, but also for extracting repeat-pass interferometric coherence, with absolute values of perpendicular baselines ranging from 190 to 479 m for 2013 CSK dataset, and from 363 to 1020 m for 2014 CSK dataset. [28][29] that guarantees a theoretical revisiting frequency of 7-9 days over the study area. Landsat 8 OLI scenes were retained only when overall cloud cover was less than 10%.…”

Section: Satellite Datamentioning

confidence: 97%

“…C-band data have been used for mapping rice [23][24][25][26], wheat [27], and multiple crops [28][29][30][31][32]. L-band data have been used too, although with generally poorer performance [33,34].…”

Section: Introductionmentioning

confidence: 99%

In-Season Mapping of Crop Type with Optical and X-Band SAR Data: A Classification Tree Approach Using Synoptic Seasonal Features

et al. 2015

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Abstract:The work focuses on developing a classification tree approach for in-season crop mapping during early summer, by integrating optical (Landsat 8 OLI) and X-band SAR (COSMO-SkyMed) data acquired over a test site in Northern Italy. The approach is based on a classification tree scheme fed with a set of synoptic seasonal features (minimum, maximum and average, computed over the multi-temporal datasets) derived from vegetation and soil condition proxies for optical (three spectral indices) and X-band SAR (backscatter) data. Best performing input features were selected based on crop type separability and preliminary classification tests. The final outputs are crop maps identifying seven crop types, delivered during the early growing season (mid-July). Validation was carried out for two seasons (2013 and 2014), achieving overall accuracy greater than 86%. Results highlighted the contribution of the X-band backscatter (σ°) in improving mapping accuracy and promoting the transferability of the algorithm over a different year, when compared to using only optical features.

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“…The ASAR images have a nominal resolution of 30m 7 . We chose to use radar imagery because of the significant body of evidence of radar sensitivity to crop properties 8,9 and particularly wheat 10,11 , together with evidence of the value of multi-temporal radar 12 and multi-polarization radar imagery [13][14][15][16][17] currently available operationally solely from the ASAR instrument. The ASAR images were all speckle filtered using the adaptive enhanced Lee filter approach 18 .…”

Section: Satellite Data Acquiredmentioning

confidence: 99%

Crop yield prediction using multipolarization radar and multitemporal visible/infrared imagery

Davis

Wilkinson

2006

SPIE Proceedings

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This paper describes research undertaken on the improvement of within-field late season yield forecasting for crops such as wheat using multi-temporal visible/infrared satellite imagery and multi-polarization radar satellite imagery. Experiments have been carried out using ASAR imagery from Envisat combined with nine bands of ASTER imagery from the NASA Terra satellite. An experimental test site in an agricultural area in the county of Lincolnshire, UK, has been used. The satellite imagery has been integrated using artificial neural networks which have been trained as predictors of the spatial distributions of yield per unit area in a variety of fields. Ground truth data in the form of yield maps from GPS-enabled combine harvesters have been used to train the neural networks and to evaluate accuracy. The results show that the combinations of ASTER and ASAR imagery can provide enhanced yield predictions with overall correlations of up to 0.77 between predicted and actual yield patterns. The results also show that the use of dual polarization radar data alone is not sufficient to give reasonable yield predictions even in a multi-temporal mode. It has also been shown that varying the architectures of the neural networks with ensembles can improve the overall results. KeywordsAgriculture, remote sensing, yield prediction, radar imagery, multi-temporal data, multi-polarization. Disciplines Computer Sciences | Databases and Information Systems | Numerical Analysis and Scientific ComputingComments ©Copyright 2009 SPIE Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic electronic or print reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. This paper was given at the SPIE ABSTRACTThis paper describes research undertaken on the improvement of within-field late season yield forecasting for crops such as wheat using multi-temporal visible/infrared satellite imagery and multi-polarization radar satellite imagery. Experiments have been carried out using ASAR imagery from Envisat combined with nine bands of ASTER imagery from the NASA Terra satellite. An experimental test site in an agricultural area in the county of Lincolnshire, UK, has been used. The satellite imagery has been integrated using artificial neural networks which have been trained as predictors of the spatial distributions of yield per unit area in a variety of fields. Ground truth data in the form of yield maps from GPS-enabled combine harvesters have been used to train the neural networks and to evaluate accuracy. The results show that the combinations of ASTER and ASAR imagery can provide enhanced yield predictions with overall correlations of up to 0.77 between predicted and actual yield patterns. The results also show that the use of dual polarization radar data alone is not sufficient to give reasonable yield predictions even in a multi-temporal mode. It...

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The efficiency of crop recognition on ENVISAT ASAR images in two growing seasons

Cited by 45 publications

References 4 publications

On the Added Value of Quad-Pol Data in a Multi-Temporal Crop Classification Framework Based on RADARSAT-2 Imagery

On the Added Value of Quad-Pol Data in a Multi-Temporal Crop Classification Framework Based on RADARSAT-2 Imagery

In-Season Mapping of Crop Type with Optical and X-Band SAR Data: A Classification Tree Approach Using Synoptic Seasonal Features

Crop yield prediction using multipolarization radar and multitemporal visible/infrared imagery

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