Sentinel-1 Synthetic Aperture Radar (SAR) data proves valuable in detecting crop phenological stages. This study explores the effectiveness of time series SAR indices from Sentinel-1 in monitoring cotton phenology, an aspect not extensively investigated. During the process, seasonal extrema and breakpoints of SAR indices were derived over a cotton plantation in Brazil from December 2021 to July 2023. The results indicate that the seasonal maximum and minimum of Dual-polarization Radar Vegetation Index (DpRVIc) align with cotton emergence and flowering dates, respectively. Additionally, the seasonal minimum of Cross-Ratio (CR) and Gamma0_VH aligns with cotton boll formation and square formation dates, respectively. During evaluation, most detected phenology stages are within ±12 days of field observation dates. However, some emergence and square formation detections exhibit large outliers, necessitating further investigation. This study underscores the significant potential of time-series Sentinel-1 data for cotton phenology monitoring, providing valuable insights for improved agricultural practices.

Both PlanetScope and Sentinel-1 synthetic aperture radar (SAR) data have proven valuable in detecting crop phenological stages. However, the use of time series data from Sentinel-1, particularly derived SAR indices, for phenology detection has been underexplored. Additionally, leveraging phenology information from both Sentinel-1 and PlanetScope time series data has not been extensively investigated. This study examines the time series of Sentinel-1 backscatter coefficient, interferometric coherence, and several SAR indices, integrating them with PlanetScope data to improve the accuracy of cotton phenology detection. Seasonal extrema and breakpoints of backscattering coefficient, interferometric coherence, and SAR indices from Sentinel-1 and normalized difference vegetation index (NDVI) from PlanetScope over a cotton plantation in Brazil from December 2021 to July 2023. These retrievals were directly correlated with in situ observations and applied on a large scale for phenology detection. The results indicate that time series cross-ratio (CR), coherence VV, dual-polarization RVI (DpRVIc), and Gamma0_VH are useful for detecting cotton emergence, vegetative growth, square formation, flowering, and boll formation, respectively. Furthermore, integrating PlanetScope and Sentinel-1 significantly enhances phenology detection accuracy [Bias = 0.87 and root mean square error (RMSE) = 17.3] compared to using only Sentinel-1 (Bias = 16.05 and RMSE = 71.98). Generally, the RMSE for all four phenology stages is around one or two times the revisit frequency of Sentinel-1. This study underscores the potential of integrating PlanetScope and Sentinel-1 for large-scale phenology monitoring.

This paper examines our previously proposed gravitational filter and iterative multistage model-based polarimetric decomposition for an improved scattering retrieval from multi-look RADARSAT-2 C-band and ALOS-2 PALSAR-2 L-band data over a coastal region in the tropics. The decomposition results showed that a large amount of negative power pixels over vegetated areas were reduced. The retrieved scattering properties for different land cover types, namely, mangrove forest, oil palm, and rice paddy, were also analyzed and are discussed in this paper.

As an effective tool to investigate time-series of ground de-formation resulting from human activities or natural disas-ters, persistent scatterer (PS) interferometry requires a large amount of computing resources especially for continuous rou-tine monitoring over large areas. Powered by open-source software packages including the SentiNel Application Plat-form (SNAP) from ESA and the Stanford Method of Persis-tent Scatterer (StaMPS), a fully automatic PS-InSAR frame-work is built to process large-volume Sentinel-I data. The processing time is reduced tremendously with the paralleliz-able procedures in the end-to-end processing pipeline boosted by High Performance Computing (HPC) clusters. Embedding in the web GIS developed in CRISP, the framework also provides interfaces in browsers to visualize the resulting defor-mation time-series and ground displacement rate maps. The automatic framework executable in HPC also enables routine monitoring of ground subsidence over major cities in South-east Asia.

Speckle filtering is often carried out to improve the interpretation of polarimetric synthetic aperture radar (PolSAR) data for terrain classification, retrieval of physical scattering properties etc. As inspired by Newton's law of universal gravitation, a force of attraction between two image pixels is formulated in polarimetric-spatial domain. It is directly proportional to polarimetric similarity but inversely proportional to spatial distance. By using the attractive force as a filtering weight, this paper presents another variant of bilateral filter, called gravitational filter, for speckle reduction in PolSAR data. The performance of the gravitational filter was examined with ALOS-2 PALSAR-2 polarimetric data. Four aspects of the filtering performance were investigated: 1) speckle reduction, 2) image feature retention, 3) polarimetric property preservation, and 4) computational efficiency. The experimental results confirmed the effectiveness of the gravitational filter in the first two aspects, which was archived at the cost of an increased computational load. Applying to the four-look ALOS-2 PALSAR-2 test data, a bias in radiometric estimation was observed and can be reduced by using only intensity components when computing the attractive force.

This paper examines two test statistics, namely, likelihood ratio statistic and Roy's largest eigenvalue, for detecting changes in multi-look bi-temporal ALOS-2 PALSAR-2 fully polarimetric data. The latter turns into the well-known intensity ratio when applying to single-polarization data. The experimental results showed a comparable good performance of both the statistics in change detection. A more comprehensive change map was obtained from the fully polarimetric data compared with the use of only single-polarization data.

A multi-stage four-component decomposition, which is inspired by the generalized odd-bounce and double-bounce scattering models, is presented in this paper for reducing negative power pixels. Its performance was evaluated by using NASA/JPL AIRSAR PolSAR C- and L-band data. The experimental results showed that a large amount of negative power pixels over vegetated areas were reduced.

This paper studies the applications of squared radius and trace statistic under homogeneous and texture models for modeling terrain radar clutter in polarimetric synthetic aperture radar (SAR) data. The squared radius and trace statistic involve the use of scattering vector and polarimetric covariance matrix, respectively, in their computation. The terrain radar clutter modeling was performed on TerraSAR-X single-look high-resolution spotlight data and multi-look NASA/JPL AIRSAR POLSAR data. Both the homogeneous and texture models were investigated and evaluated quantitatively through chi-squared goodness-of-fit test. The obtained results showed that the texture model provided a better fitting compared with the homogeneous model.

This paper addresses ship target detection in dual-frequency single polarization and fully polarimetric SAR data by using a global thresholding approach. The corresponding threshold values were determined from the gamma distribution (for single-polarization SAR data) and the chi-squared distribution (for fully polarimetric SAR data). Based on the experimental results, which were obtained from nine-look NASA/JPL AIRSAR POLSAR data, the complementary use of both the C- and L-band was found to remove ghost ships (due to azimuth ambiguity) and improve the resolution of ship targets. Moreover, it was observed that the use of fully polarimetric SAR information reduced undesired artifacts, which were particularly noticeable in the C-band single-polarization outputs.

In this paper the Bhattacharyya distance and the divergence are derived as two different measures of target class separability based on the central complex multivariate Gaussian and Wishart distributions with unequal covariance matrices. The derived Bhattacharyya distances for the two distributions, respectively, differ only in terms of a simple multiplier, i.e. the number of degrees of freedom (also known as number of looks in polarimetric synthetic aperture radar data). The same aspect was observed for the divergence. Furthermore, the Bhattacharyya distance was found to be proportional to the Bartlett distance, while the divergence is proportional to the symmetrized normalized log-likelihood distance. The use of the Bhattacharyya distance and the divergence as separability measures of target classes was demonstrated by using NASA/JPL AIRSAR POLSAR data and was benchmarked against the Euclidean distance. From the results, both the Bhattacharyya distance and the divergence were found to perform consistently in measuring the separability of target classes.

Preliminary work on speckle reduction and edge detection of TerraSAR-X single-look dual-polarization (HH and VV polarizations) spotlight data is reported. A speckle reduction approach is introduced based on a beta-distributed sample squared radius. The performance of the speckle reduction approach was benchmarked against the boxcar filter and assessed with respect to the degree of speckle reduction, radiometric preservation as well as image feature retention. For detecting edges in TerraSAR-X single-look dual-polarization speckled imagery, a constant false alarm rate (CFAR) detector is presented in this paper based on the Wilks' lambda. The applicability of the edge detector for TerraSAR-X single-look single-polarization intensity data is also discussed.

From the literature review, there are two constant false alarm rate detectors for detecting edges in multi-look fully polarimetric synthetic aperture radar (POLSAR) imagery, namely the likelihood ratio edge detector and the Roy's largest eigenvalue-based edge detector. In the latter approach, one major restriction is the computation complexity, i.e. in the context of the chosen C language-based implementation. Thus, in this paper, a novel hardware-based architecture is presented to improve the processing time for the Roy's largest eigenvalue-based edge detection. The algorithm was implemented in a field-programmable gate array (FPGA) with an accelerated solution targeting data rates of up to 1 Gb/s. Its performance was examined using nine-look NASA/JPL C-band data and evaluated in terms of processing speed and accuracy as compared to the C language-based implementation on a personal computer (PC) with a Core¿ 2 Duo processor clocked at 2.2 GHz.

A. spatially variant speckle filter is proposed for multi-look polarimetric synthetic aperture radar (POLSAR) imagery. The central idea is that the filtering is applied only to homogeneous areas based on the scattering properties, while for detected edges, lines and point-like textural features, the original complex covariance matrix of these features is restored in order to preserve the actual features. The capabilities of the proposed filter were examined using nine-look NASA/JPL POLSAR C-band data. Based on the obtained results, the proposed filter showed a promising performance in speckle removal and radiometric preservation. Moreover, the point-like textural features as well as structural features (i.e. edges and lines) were well-retained in the filtered outputs.

The use of C- and L-band polarimetric synthetic aperture radar (POLSAR) data for classifying land cover features in a tropical area is investigated in this study. The POLSAR data were acquired during the NASA/JPL PACRIM-1 mission over the northern part of Peninsular Malaysia on 3rd December 1996. Prior to classification, the Lee polarimetric filter was applied to the complex covariance matrix for speckle suppression. In unsupervised classification, the scattering mechanism of each pixel in the speckle-suppressed images was analyzed and grouped into one of the three categories: (1) odd-bounce, (2) even-bounce, or (3) diffuse scattering. Training samples were then generated from the outputs of the unsupervised classification, to be used in subsequent supervised classifications of various frequency and polarization combinations. The Kappa statistics computed for classification using single-frequency fully polarized C- and L-band data were 0.69 and 0.73, respectively. An improvement to 0.79 was achieved by using the dual-frequency (combined C and L bands), fully polarized data in the classification.