Object-oriented crop mapping and monitoring using multi-temporal polarimetric RADARSAT-2 data

Jiao, Xianfeng; Kovacs, John M.; Shang, Jiali; McNairn, Heather; Walters, Dan; Ma, Bao–Luo; Geng, Xiaoyuan

doi:10.1016/j.isprsjprs.2014.06.014

Cited by 176 publications

(107 citation statements)

References 29 publications

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“…Even the classification results of random forest are higher than CART, maize indicated lower accuracies than other five crop types. Jiao et al (2014) applied object oriented classification for the multi-temporal crop monitoring using polarimetric Radarsat-2 images. Although overall accuracies are higher than 80%, polarimetric decomposition achieved higher accuracies comparing to linear polarizations.…”

Section: Introductionmentioning

confidence: 99%

Backscatter Analysis Using Multi-Temporal Sentinel-1 Sar Data for Crop Growth of Maize in Konya Basin, Turkey

Abdikan

Şekertekin

Ustunern

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Temporal monitoring of crop types is essential for the sustainable management of agricultural activities on both national and global levels. As a practical and efficient tool, remote sensing is widely used in such applications. In this study, Sentinel-1 Synthetic Aperture Radar (SAR) imagery was utilized to investigate the performance of the sensor backscatter image on crop monitoring. Multi-temporal C-band VV and VH polarized SAR images were acquired simultaneously by in-situ measurements which was conducted at Konya basin, central Anatolia Turkey. During the measurements, plant height of maize plant was collected and relationship between backscatter values and plant height was analysed. The maize growth development was described under Biologische Bundesanstalt, bundessortenamt und CHemische industrie (BBCH). Under BBCH stages, the test site was classified as leaf development, stem elongation, heading and flowering in general. The correlation coefficient values indicated high correlation for both polarimetry during the early stages of the plant, while late stages indicated lower values in both polarimetry. As a last step, multi-temporal coverage of crop fields was analysed to map seasonal land use. To this aim, object based image classification was applied following image segmentation. About 80% accuracies of land use maps were created in this experiment. As preliminary results, it is concluded that Sentinel-1 data provides beneficial information about plant growth. Dual-polarized Sentinel-1 data has high potential for multi-temporal analyses for agriculture monitoring and reliable mapping.

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Section: Introductionmentioning

confidence: 99%

Backscatter Analysis Using Multi-Temporal Sentinel-1 Sar Data for Crop Growth of Maize in Konya Basin, Turkey

Abdikan

Şekertekin

Ustunern

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Previous studies have shown that quad-pol data can successfully classify major crop types [22,23] and even monitor crop phenology [24,25] or detect crop lodging [26]. A basic requirement for this is that scenes should be acquired on dates when crops show differences apparent to the sensor, that is, they behave differently when the electromagnetic pulse impinges on them.…”

Section: Introductionmentioning

confidence: 99%

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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“…Both optical/infrared and microwave satellite data can be used for this purpose. Recently, Synthetic Aperture Radar (SAR) imagery, thanks to their potential of data collection regardless of weather and illumination conditions, has become an essential tool for crop mapping and monitoring activities (Jiao et al, 2014). Radar-based crop type classification requires earth observations with multiple polarization.…”

Section: Introductionmentioning

confidence: 99%

Clustering of Multi-Temporal Fully Polarimetric L-Band Sar Data for Agricultural Land Cover Mapping

Tamiminia

Homayouni

Safari

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Recently, the unique capabilities of Polarimetric Synthetic Aperture Radar (PolSAR) sensors make them an important and efficient tool for natural resources and environmental applications, such as land cover and crop classification. The aim of this paper is to classify multi-temporal full polarimetric SAR data using kernel-based fuzzy C-means clustering method, over an agricultural region. This method starts with transforming input data into the higher dimensional space using kernel functions and then clustering them in the feature space. Feature space, due to its inherent properties, has the ability to take in account the nonlinear and complex nature of polarimetric data. Several SAR polarimetric features extracted using target decomposition algorithms. Features from Cloude-Pottier, Freeman-Durden and Yamaguchi algorithms used as inputs for the clustering. This method was applied to multi-temporal UAVSAR L-band images acquired over an agricultural area near Winnipeg, Canada, during June and July in 2012. The results demonstrate the efficiency of this approach with respect to the classical methods. In addition, using multi-temporal data in the clustering process helped to investigate the phenological cycle of plants and significantly improved the performance of agricultural land cover mapping.

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Object-oriented crop mapping and monitoring using multi-temporal polarimetric RADARSAT-2 data

Cited by 176 publications

References 29 publications

Backscatter Analysis Using Multi-Temporal Sentinel-1 Sar Data for Crop Growth of Maize in Konya Basin, Turkey

Backscatter Analysis Using Multi-Temporal Sentinel-1 Sar Data for Crop Growth of Maize in Konya Basin, Turkey

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

Clustering of Multi-Temporal Fully Polarimetric L-Band Sar Data for Agricultural Land Cover Mapping

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