Polarization Impact in TanDEM-X Data Over Vertical-Oriented Vegetation: The Paddy-Rice Case Study

It has been recently shown that the TanDEM-X mission is capable of tracking the plant growth of rice paddies. The precision of the elevation measure depends on the physical interaction between the synthetic aperture radar (SAR) signal and the canopy. In this letter, this interaction is studied by considering the signal polarization. In particular, the vertical and horizontal wave polarizations are compared, and their performance in the temporal mapping of the crop height is analyzed. The temporal elevation difference analysis shows a monotonically increasing trend within the reproductive stage of the canopy, with maximum height discrepancies between polarizations of about 9 cm. From an operational point of view of InSAR-based vegetation height measurements, this letter demonstrates that the oriented structure of the canopy shall be considered not only in polarimetric InSAR studies but also in the interpretation of bistatic spaceborne interferometric elevation models

Assessment of Paddy Rice Height: Sequential Inversion of Coherent and Incoherent Model

This paper investigates the evolution of canopy height of rice fields for a complete growth cycle. For this purpose, copolar interferometric Synthetic Aperture Radar (Pol-InSAR) time series data were acquired during the large across-track baseline (>1 km) science phase of the TanDEM-X mission. The height of rice canopies is estimated by three different model-based approaches. The first approach evaluates the inversion of the Random Volume over Ground (RVoG) model. The second approach evaluates the inversion of a metamodel-driven electromagnetic backscattering model by including a priori morphological information. The third approach combines the previous two processes. The validation analysis was carried out using the Pol-InSAR and ground measurement data acquired between May and September in 2015 over rice fields located in Ipsala district of Edirne, Turkey. The results of presented height estimation algorithms demonstrated the advantage of Pol-InSAR data. The combined RvoG model and EM metamodel height estimation approach provided rice canopy heights with errors less than 20 cm for the complete growth cycle

Soil Moisture Estimation using Sentinel-1/-2 Imagery Coupled with cycleGAN for Time-series Gap Filing

Fast soil moisture content (SMC) mapping is necessary to support water resource management and to understand crops’ growth, quality and yield. Thereby, Earth Observation (EO) plays a key role due to its ability of almost real-time monitoring of large areas at a low cost. This study aimed to explore the possibility of taking advantage of freely available Sentinel-1 (S1) and Sentinel-2 (S2) EO data for the simultaneous prediction of SMC with cycle-consistent adversarial network (cycleGAN) for time-series gap filling. The proposed methodology, first, learns latent low-dimensional representation of the satellite images, then learns a simple machine learning model on top of these representations. To evaluate the methodology, a series of vineyards, located in South Australia’s Eden valley are chosen. Specifically, we presented an efficient framework for extracting latent features from S1 and S2 imagery. We showed how one could use S1 to S2 feature translation based on Cycle-GAN using S1&S2 time series when there are missing images acquired over an area of interest. The resulting data in our study is then used to fill gaps in time series data. We used the resulting latent representations to predict SMC with various ML tools. In the experiments, cycleGAN and the autoencoders were trained with data randomly chosen around the site of interest, so we could augment the existing dataset. The best performance was demonstrated with random forest algorithm, whereas linear regression model demonstrated significant overfitting. The experiments demonstrate that the proposed methodology outperforms the compared state-of-the-art methods if there are missing optical and synthetic-aperture radar (SAR) images

The worsening impacts of land reclamation assessed with Sentinel-1: The Rize (Turkey) test case

Massive amounts of land are being reclaimed to build airports, new cities, ports, and highways. Hundreds of kilometers are added each year, as coastlines are extended further out to the sea. In this paper, this urbanization approach is monitored by Persistent Scatterer Interferometry (PSI) technique with Sentinel-1 SAR data. The study aims to explore this technology in order to support local authorities to detect and evaluate subtle terrain displacements. For this purpose, a large 3-years Sentinel-1 stack composed by 92 images acquired between 07/01/2015 to 27/01/2018 is employed and stacking techniques are chosen to assess ground motion. The test site of this study, Rize, Turkey, has been declared at high risk of collapse and radical solutions such as the relocation of the entire city in another area are been taken into consideration. A media fact-checking approach, i.e. evaluating national and international press releases on the test site, is considered for the paper and this work presents many findings in different areas of the city. For instance, alerts are confirmed by inspecting several buildings reported by the press. Critical infrastructures are monitored as well. Portions of the harbor show high displacement rates, up to 1 cm/year, proving reported warnings. Rural villages belonging to the same municipality are also investigated and a mountainous village affected by landslide is considered in the study. Sentinel-1 is demonstrated to be a suitable system to detect and monitor small changes or buildings and infrastructures for these scenarios. These changes may be highly indicative of imminent damage which can lead to the loss of the structural integrity and subsequent failure of the structure in the long-term. In Rize, only a few known motion-critical structures are monitored daily with in-situ technologies. SAR interferometry can assist to save expensive inspection and monitoring services, especially in highly critical cases such as the one studied in this paper

Retrieval of vegetation height in rice fields using polarimetric SAR interferometry with TanDEM-X data

This work presents for the first time a demonstration with satellite data of polarimetric SAR interferometry (PolInSAR) applied to the retrieval of vegetation height in rice fields. Three series of dual-pol interferometric SAR data acquired with large baselines (2–3 km) by the TanDEM-X system during its science phase (April–September 2015) are exploited. A novel inversion algorithm especially suited for rice fields cultivated in flooded soil is proposed and evaluated. The validation is carried out over three test sites located in geographically different areas: Sevilla (SW Spain), Valencia (E Spain), and Ipsala (W Turkey), in which different rice types are present. Results are obtained during the whole growth cycle and demonstrate that PolInSAR is useful to produce accurate height estimates (RMSE 10–20 cm) when plants are tall enough (taller than 25–40 cm), without relying on external reference information.This work has been supported by the Spanish Ministry of Economy and Competitiveness (MINECO) and EU FEDER under project TIN2014-55413-C2-2-P. The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007–2013) under grant agreement 606983, and the Land-SAF (the EUMETSAT Network of Satellite Application Facilities) project. The in-situ measurements in the Ipsala site were conducted with the funding of The Scientific and Technological Research Council of Turkey (TUBITAK, Project No.: 113Y446)

The Performance Analysis Based on SAR Sample Covariance Matrix

Multi-channel systems appear in several fields of application in science. In the Synthetic Aperture Radar (SAR) context, multi-channel systems may refer to different domains, as multi-polarization, multi-interferometric or multi-temporal data, or even a combination of them. Due to the inherent speckle phenomenon present in SAR images, the statistical description of the data is almost mandatory for its utilization. The complex images acquired over natural media present in general zero-mean circular Gaussian characteristics. In this case, second order statistics as the multi-channel covariance matrix fully describe the data. For practical situations however, the covariance matrix has to be estimated using a limited number of samples, and this sample covariance matrix follow the complex Wishart distribution. In this context, the eigendecomposition of the multi-channel covariance matrix has been shown in different areas of high relevance regarding the physical properties of the imaged scene. Specifically, the maximum eigenvalue of the covariance matrix has been frequently used in different applications as target or change detection, estimation of the dominant scattering mechanism in polarimetric data, moving target indication, etc. In this paper, the statistical behavior of the maximum eigenvalue derived from the eigendecomposition of the sample multi-channel covariance matrix in terms of multi-channel SAR images is simplified for SAR community. Validation is performed against simulated data and examples of estimation and detection problems using the analytical expressions are as well given