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

Abstract

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.

Multi-Temporal Dual- and Quad-Polarimetric Synthetic Aperture Radar Data for Crop-Type Mapping

“…Results showed that rapeseed achieved the best PA results (>95%) in all scenarios, whereas barley achieved the second highest PA's, ranging from 92.1% to 95.3%. The user's accuracy (UA) of both crops was above 85% for all scenarios tested and achieved 100% for rapeseed in scenarios A and D. The results reported in this study for rapeseed agree with those found by Larrañaga and Álvarez-Mozos [35]. Cereals (i.e., wheat and barley) normally show a similar behaviour during the growing season due to their very similar plant structure and phenology, which causes difficulty in separating them based on their backscatter characteristics.…”

“…When Sentinel-1 dual-pol SAR data were used as input for the classification, the second-best accuracy (87.1%) was obtained, whereas using dual-pol RADARSAT-2 data (scenario F) provided a similar overall accuracy (86%). The accuracy of the dual-pol RADARSAT-2 data in the general computation of this study showed higher accuracy than previous studies in the literature [35,39]. From RADARSAT-2 scenarios, the accuracies of 89.1% and 86.6% for scenarios D and A, respectively, were quite similar; however, D required many more SAR images and parameters than A to achieve this small 2.5% improvement of accuracy.…”

“…The contribution of polarimetry was also assessed in Reference [33] by testing the performance when input information is removed progressively. The capability of polarimetric SAR data in crop classification has also been demonstrated by applying machine learning algorithms such as Random Forest (RF) [34][35][36], Decision Tree (DT) [37,38], Neural Networks (NNs) [39] and Support Vector Machines (SVMs) [40,41].…”

Efficient Identification of Corn Cultivation Area with Multitemporal Synthetic Aperture Radar and Optical Images in the Google Earth Engine Cloud Platform

“…Earth observation satellites that monitor and regularly revisit cropland are inexpensive and excellent data sources that provide full spatial detailed information for crop mapping [8,9]. In recent decades, many studies have focused on crop monitoring with optical [10-14] and synthetic aperture radar (SAR) images [1,[15][16][17][18]. Multitemporal and time series data are powerful tools for identifying different crops and handling the problem of spectral similarity in heterogeneous underlying surfaces in single-period imagery [19][20][21][22][23].…”

Optimized Sample Selection in SVM Classification by Combining with DMSP-OLS, Landsat NDVI and GlobeLand30 Products for Extracting Urban Built-Up Areas

“…Optical remote sensing data with high resolution, such as data extracted from remote sensing image indices and other high-quality land cover data products, are the major datasets used for urban information extraction [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34], and such data are far superior to those of DMSP-OLS in terms of image resolution. However, most of these data products have limited temporal coverage and present limited usefulness for a dynamic analysis at large scales.…”