Esra Erten

Glacier Velocity Monitoring by Maximum Likelihood Texture Tracking

The performance of a tracking algorithm considering remotely sensed data strongly depends on a correct statistical description of the data, i.e., its noise model. The objective of this paper is to introduce a new intensity tracking algorithm for synthetic aperture radar (SAR) data, considering its multiplicative speckle/noise model. The proposed tracking algorithm is discussed regarding the measurement of glacier velocities. Glacier monitoring exhibits complex spatial and temporal dynamics including snowfall, melting, and ice flows at a variety of spatial and temporal scales. Due to these complex characteristics, most traditional methods based on SAR suffer from speckle decorrelation that results in a low signal-to-noise ratio. The proposed tracking technique improves the accuracy of the classical intensity tracking technique by making use of the temporal speckle structure. Even though a new intensity-based matching algorithm is proposed, particularly for incoherent data sets, the analysis of the proposed technique was also performed for correlated data sets. As it is demonstrated, the velocity monitoring can be continuously performed by using the maximum likelihood (ML) texture tracking without any assumption concerning the correlation of the data set. The ML texture tracking approach was tested on ENVISAT-ASAR data acquired during summer 2004 over the Inyltshik glacier in Kyrgyzstan, representing one of the largest alpine glacier systems of the world. It will be demonstrated that the proposed technique is capable of robustly and precisely detecting the surface velocity field and velocity changes in time.

Paddy-Rice Monitoring Using TanDEM-X

This paper evaluates the potential of spaceborne bistatic interferometric synthetic aperture radar images for the monitoring of biophysical variables in wetlands, with a special interest on paddy rice. The assessment is made during the rice cultivation period, from transplanting to harvesting time (May to October) for fields around Gala lake (Turkey), one of the largest and most productive paddy rice planting area in the country. Detailed ground truth measurements describing biophysical parameters are collected in a dedicated campaign. A stack of 16 dual-pol TanDEM-X images is used for the generation of 32 digital elevation models (DEMs) over the studied area. The quality of the data allows the use of the interferometric phase as a state variable capable to estimate crop heights for almost all the growing stages. The early vegetative rice stage, which is characterized by flooded fields, cannot be represented by the interferometric phase due to a low signal-to-noise ratio but can be easily detected by amplitude and interferometric coherence thresholding. A study on the impact of the polarization in the signal backscatter is also performed. An analysis of the differences between HH and VV DEMs shows the varying signal penetration for the two polarizations at different growing stages. The validation with reference data demonstrates the capability to establish a direct relationship between interferometric phase and rice growth. The very high coherence of TanDEM-X data yields elevation estimates with root-mean-square error in a decimetric level, supporting temporal change analysis on a field-by-field basis.

A New Coherent Similarity Measure for Temporal Multichannel Scene Characterization

This paper proposes a new method for a measure of coherent similarity between temporal multichannel synthetic aperture radar (SAR) images and its implementation to change detection application. The method is based on mutual information (MI) from information theory. The MI measures the amount of information in common between coherent temporal multichannel SAR acquisitions. In order to develop an algorithm for all kinds of SAR images, such as interferometric SAR, polarimetric-interferometric SAR (PolInSAR), and partial PolInSAR, first, the joint density function of temporal multichannel images based on their second-order statistics has been derived. Then, the derived joint density function is used to calculate an analytical expression for the MI between temporal images, which is assumed to be maximal if the temporal images are identical. Although, in this paper, a new coherent similarity measure has analytically been derived for temporal polarimetric SAR images based on complex Wishart process in time, since the mathematical formulation is general, it can equally well be implemented into any kind of multivariate remote sensing data, such as multispectral optical and interferometric images after small continuation. This derived quantity has been implemented for change detection application whose aim is to characterize the temporal behavior of the acquisitions. A comparison between the proposed and the other well-known change detection methods by means of scene characterization is shown, describing the advantages due to the fact that the proposed change detector involves almost every facet of applied change detection.

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.

Rice Growth Monitoring by Means of X-Band Co-polar SAR: Feature Clustering and BBCH Scale

Precision agriculture research, which aims to monitor agricultural fields and to manage agricultural practice by considering overall environmental impacts, has gained momentum with the recent improvements in the remote sensing area. The objective of this letter, as a part of precision farming, is to implement Biologische Bundesanstalt, Bundessortenamt und CHemische Industrie (BBCH) scale assignment in plant growth monitoring by means of SAR. The proposed approach copes with structural heterogeneity in agricultural fields by grouping together similar morphologies. For this, densely cultivated paddy rice fields are analyzed using TerraSAR-X (TSX) co-polar SAR data. For generating structurally similar groups, K-means clustering is used in a polarimetric feature vector space, which is composed of backscattering intensities and polarimetric phase differences. This step is followed by a preliminary classification approach based on the temporal separability of the explanatory parameters. In the last step of the proposed methodology, assigned classes are updated based on the biological principles that are followed in rice cultivation. This letter provides the results of the proposed algorithm and compares them to the standard threshold-based approach in two independent agricultural areas. The results show the superiority of the feature-clustering-based classification compared with the standard approach in handling field heterogeneity.

Glacier Velocity Estimation by Means of a Polarimetric Similarity Measure

The contribution of polarimetric synthetic aperture radar (PolSAR) images compared with that of single-channel SAR images in terms of temporal scene characterization has been found and described to add valuable information in the literature. However, despite a number of recent studies focusing on single-polarized glacier monitoring, the potential of polarimetry to estimate the surface velocity of glaciers has not been explored due to the complex mechanism of polarization through glacier/snow. In this paper, a new approach to the problem of monitoring glacier surface velocity is proposed by means of temporal PolSAR images, using a basic concept from information theory, i.e., mutual information (MI). The proposed polarimetric tracking method applies the MI to measure the statistical dependence between temporal polarimetric images, which is assumed to be maximum if the images are geometrically aligned. Since the proposed polarimetric tracking method is very powerful and general, it can be implemented into any kind of multivariate remote sensing data such as multichannel optical and single-channel SAR images. The proposed polarimetric tracking is then used to retrieve the surface velocity of the Aletsch Glacier in Switzerland and the Inylchek Glacier in Kyrgyzstan with two different SAR sensors: the Experimental SAR airborne L-band (fully polarimetric) and Envisat C-band (single-polarized) systems, respectively. The effect of the number of channels (polarimetry) into tracking investigations demonstrated that the presence of snow, as expected, affects the location of the phase center in different polarization and frequency channels, as for the glacier tracking with temporal HH compared to temporal VV channels. In this paper, it is shown how it is possible to optimize these two different contributions, considering the multichannel SAR statistics.

Estimation of Rice Crop Height From X- and C-Band PolSAR by Metamodel-Based Optimization

Rice crops are important in global food economy and are monitored by precise agricultural methods, in which crop morphology in high spatial resolution becomes the point of interest. Synthetic aperture radar (SAR) technology is being used for such agricultural purposes. Using polarimetric SAR (PolSAR) data, plant morphology dependent electromagnetic scattering models can be used to approximate the backscattering behaviors of the crops. However, the inversion of such models for the morphology estimation is complex, ill-posed, and computationally expensive. Here, a metamodel-based probabilistic inversion algorithm is proposed to invert the morphology-based scattering model for the crop biophysical parameter mainly focusing on the crop height estimation. The accuracy of the proposed approach is tested with ground measured biophysical parameters on rice fields in two different bands (X and C) and several channel combinations. Results show that in C-band the combination of the HH and VV channels has the highest overall accuracy through the crop growth cycle. Finally, the proposed metamodel-based probabilistic biophysical parameter retrieval algorithm allows estimation of rice crop height using PolSAR data with high accuracy and low computation cost. This research provides a new perspective on the use of PolSAR data in modern precise agriculture studies.

Paddy-Rice Phenology Classification Based on Machine-Learning Methods Using Multitemporal Co-Polar X-Band SAR Images

Crop monitoring and phenology estimation based on the satellite systems have become an important research area due to high demand on crops. Satellites with synthetic aperture radar (SAR) sensor are highly preferred on such studies because of not only their day/night and all weather acquisition capabilities but also their ability to detect small morphological changes in monitored target, regarding the wavelength of signals. Besides, thanks to the high temporal resolution of new generation space-based sensors, it has been possible to monitor growth cycle of crops by classification algorithms. This paper focused on building a feasible phenology classification schema for paddy-rice using multitemporal co-polar TerraSAR-X images. Phenology classification was conducted with support vector machines (SVM) with linear and nonlinear kernel, k-nearest neighbors (kNN), and decision trees (DT). The key implementation challenges such as the number of classes, the identification of the boundaries of the classes, and the selection of textural and polarimetric features were deeply analyzed. According to all the evaluations conducted, the classification schema was finalized to be used for obtaining thematic maps for two independent rice-cultivated agricultural areas located in Spain and Turkey. The results of these experiments enable one to draw a conclusion about feasibility of machine learning (ML) algorithms in operational phenology monitoring.

Determining Rice Growth Stage with X-Band SAR: A Metamodel Based Inversion

Rice crops are important in the global food economy, and new techniques are being implemented for their effective management. These techniques rely mainly on the changes in the phenological cycle, which can be investigated by remote sensing systems. High frequency and high spatial resolution Synthetic Aperture Radar (SAR) sensors have great potential in all-weather conditions for detecting temporal phenological changes. This study focuses on a novel approach for growth stage determination of rice fields from SAR data using a parameter space search algorithm. The method employs an inversion scheme for a morphology-based electromagnetic backscattering model. Since such a morphology-based model is complicated and computationally expensive, a surrogate metamodel-based inversion algorithm is proposed for the growth stage estimation. The approach is designed to provide estimates of crop morphology and corresponding growth stage from a continuous growth scale. The accuracy of the proposed method is tested with ground measurements from Turkey and Spain using the images acquired by the TerraSAR-X (TSX) sensor during a full growth cycle of rice crops. The analysis shows good agreement for both datasets. The results of the proposed method emphasize the effectiveness of X-band PolSAR data for morphology-based growth stage determination of rice crops.

A polarimetric temporal scene parameter and its application to change detection

The contribution of Polarimetric Synthetic Aperture Radar (PolSAR) images compared with the single-channel SAR in terms of temporal scene characterization has been found and described to add valuable information in the literature. In this paper, a new PolSAR change detector which makes use of Kullback-Leibler divergence is described. Kullback-Leibler divergence (KL-divergence) measures the amount of information in common between coherent temporal multi-channel polarimetric SAR acquisitions. Although in this paper KL-divergence measure has analytically been derived for temporal polarimetric SAR images based on complex Wishart process in time, since the mathematical formulation is general, it can be implemented into any kind of multivariate remote sensing data such as multi-spectral optical and interferometric images. KL-divergence measure is also simplified to give an explicit expressions for temporal single channel SAR images. The new results are simple, easy to be used, and superior in providing detailed structure.