Synthetic Aperture Radar Data Acquisition Research Articles

An Advanced Non-Interrupted Internal Calibration Model Based on Azimuth Modulation and Waveform Diversity for SAR Systems

Internal calibration, designed for obtaining the gain and phase errors introduced by the fluctuations of the electronic instruments, is an essential operation to guarantee the imaging performance of modern synthetic aperture radar (SAR) systems. However, in most of the current internal calibration schemes, the internal calibration processes interrupt the normal SAR data acquisition, which results in a loss of azimuth samples and degradation of the SAR image quality. To this end, an advanced non-interrupted internal calibration model is proposed, performing the internal calibration during nominal data acquisitions. The calibration signals and echoes are orthogonal and are received simultaneously. Then, they can be separated by postprocessing via the waveform diversity concept. Two schemes based on the proposed model are demonstrated via theoretical analysis and simulation experiments. The proposed model can achieve a considerable signal-to-noise ratio (SNR) and reduce the influence of calibration signals on echoes, which have some potential applications for future SAR missions.

Segmented continuously varying pulse interval sequence design for extremely high-resolution spaceborne sliding spotlight SAR

ABSTRACT Synthetic aperture radar (SAR) is an active microwave remote sensing device used to obtain high-resolution SAR images.With the development of SAR, extremely high-resolution SAR (i.e. resolution reaching 0.1 m) plays an increasingly important role in applications such as target recognition. In extremely high-resolution spaceborne SAR, the variation of slant range history becomes extremely severe, resulting in echoes of interest overlapping with transmitted pulses and nadir echoes when transmitting the pulses at constant pulse intervals (PIs), further causing the loss of echo data. Although a continuously varying pulse interval (CVPI) system can expand the tolerance of slant range variation to some extent, overlap still occurs when the variation in slant range exceeds a certain threshold. Thus, this paper proposes segmented CVPI sequence design methods to solve the data acquisition problem in the situation of severe variation in slant range history. The timing diagram- and strip layering diagram-based segmented CVPI sequence design methods, which are suitable for the acquisition of spaceborne SAR data with any variation of slant range, are given. The characteristics of the two methods are analysed. Finally, several computer simulations indicate that the echoes generated by the proposed methods are not overlapped, which verifies the validity of the proposed methods.

SENTINEL-1 SAR FOR OPERATIONAL MONITORING OF SOUTHERN AFRICA OCEANS

Abstract. The introduction of Synthetic Aperture Radar (SAR) through the Oceans and Coasts Monitoring Information system (OCIMS) has therefore become a critical tool for operational coastal and ocean monitoring in South Africa. Natural/man-made ocean phenomena can be observed using satellite Synthetic Aperture Radar (SAR) techniques, making it an integral part of large ocean area monitoring for maritime domain awareness. SAR is an active instrument on-board a satellite platform that transmits electromagnetic waves and measures the returning signal to infer what is happening on the ocean surface. The objective of this paper was to provide information and present options to the decision-makers around sustainability of commercial SAR data acquisition in support of OCIMS IVT. The OCIMS IVT tool identifies ships from multiple types of SAR data, which can provide locations of vessels that have switched off their AIS and oil slicks caused by vessels. Although open-data offerings, such as Sentinel-1, can be considered alternatives to commercial sensors, they present severe limitations for an operational service. To meet OCIMS requirements, and address the imperative to reduce costs, a hybrid of commercial acquisition being used in conjunction with an open data source such as Sentinel-1 is recommended. This would ensure that an operational service with the desired latency as well as increasing the revisit time over the EEZ.

Monitoring river water level using multiple bounces of bridges in SAR images

Monitoring river water level is of great importance to understand hydrological and hydraulic processes and evaluate flood and landslide risk. This paper proposes a method for estimating river water levels under bridges using multi-temporal high-resolution synthetic aperture radar (SAR) intensity images. Due to the side-looking characteristics of SAR data acquisitions, bridges crossing over rivers usually exhibit multiple bounce echoes in SAR images caused by the multipath scattering. The range difference of the multiple returns is proportional to the clearance between the water surface and the bridge. In this study, the water level oscillation of Yangtze River under Badong Yangzte River Bridge (China) was calculated by estimating the range pixel distances of the multiple bounces in ALOS-2 PALSAR-2 images using cross correlation algorithm. Besides, a linear correlation between the range pixel distance of the multiple bounces and in situ water level measurements was found, with a coefficient of determination equal to 0.9985. This relationship allowed estimating the river water level in each SAR acquisition, with an average error equal to −0.39 m and root mean square error of 0.51 m, and the elevation of the bridge, with an error of about 0.5 m, which is smaller than the theoretical uncertainty of the method (1.24 m). The obtained results show the efficiency of the method in estimating river water levels.

Soil Moisture Estimation Using Citizen Observatory Data, Microwave Satellite Imagery, and Environmental Covariates

Soil moisture (SM) is a key variable in the climate system and a key parameter in earth surface processes. This study aimed to test the citizen observatory (CO) data to develop a method to estimate surface SM distribution using Sentinel-1B C-band Synthetic Aperture Radar (SAR) and Landsat 8 data; acquired between January 2019 and June 2019. An agricultural region of Tard in western Hungary was chosen as the study area. In situ soil moisture measurements in the uppermost 10 cm were carried out in 36 test fields simultaneously with SAR data acquisition. The effects of environmental covariates and the backscattering coefficient on SM were analyzed to perform SM estimation procedures. Three approaches were developed and compared for a continuous four-month period, using multiple regression analysis, regression-kriging and cokriging with the digital elevation model (DEM), and Sentinel-1B C-band and Landsat 8 images. CO data were evaluated over the landscape by expert knowledge and found to be representative of the major SM distribution processes but also presenting some indifferent short-range variability that was difficult to explain at this scale. The proposed models were evaluated using statistical metrics: The coefficient of determination (R2) and root mean square error (RMSE). Multiple linear regression provides more realistic spatial patterns over the landscape, even in a data-poor environment. Regression kriging was found to be a potential tool to refine the results, while ordinary cokriging was found to be less effective. The obtained results showed that CO data complemented with Sentinel-1B SAR, Landsat 8, and terrain data has the potential to estimate and map soil moisture content.

Editorial for Special Issue “Applications of Synthetic Aperture Radar (SAR) for Land Cover Analysis”

Synthetic aperture radar (SAR) imaging systems derive microwave data, from space or airborne (piloted and remote piloted), that provide opportunities for the interpretation of many characteristics of the terrain surface. The increasing number of satellites equipped with SAR data acquisition systems that are being launched with a range of wavelengths, polarizations, and operating characteristics are enabling a better understanding of the earth’s environment, for such activities as vegetation analysis, forest inventories, land subsidence, and urban analysis. In addition, airborne systems for remote piloted systems and ground-based systems are available. This Special Issue presents six quality scientific papers on typical applications of SAR technologies. They include methods for the determination of above ground biomass (AGB), crop mapping using data from an advanced X-band system developed in Japan, analysis of natural and human-induced slow-rate ground deformations in the region of Campania, in Italy, the location of landslides caused by natural phenomena based on SAR images derived from the Japanese high-resolution Advanced Land Observing Satellite-2 (ALOS-2), and monitoring the size of refugee camps and their environmental impacts caused by the displacement of people from Myanmar to the Cox’s Bazar District, around Kutupalong, in Bangladesh. The paper concludes with some comments on the future directions of developments in SAR systems.

MFFA-SARNET: Deep Transferred Multi-Level Feature Fusion Attention Network with Dual Optimized Loss for Small-Sample SAR ATR

Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR), most algorithms of which have employed and relied on sufficient training samples to receive a strong discriminative classification model, has remained a challenging task in recent years, among which the challenge of SAR data acquisition and further insight into the intuitive features of SAR images are the main concerns. In this paper, a deep transferred multi-level feature fusion attention network with dual optimized loss, called a multi-level feature attention Synthetic Aperture Radar network (MFFA-SARNET), is proposed to settle the problem of small samples in SAR ATR tasks. Firstly, a multi-level feature attention (MFFA) network is established to learn more discriminative features from SAR images with a fusion method, followed by alleviating the impact of background features on images with the following attention module that focuses more on the target features. Secondly, a novel dual optimized loss is incorporated to further optimize the classification network, which enhances the robust and discriminative learning power of features. Thirdly, transfer learning is utilized to validate the variances and small-sample classification tasks. Extensive experiments conducted on a public database with three different configurations consistently demonstrate the effectiveness of our proposed network, and the significant improvements yielded to surpass those of the state-of-the-art methods under small-sample conditions.

Potential of Recurrence Metrics from Sentinel-1 Time Series for Deforestation Mapping

The REDD+ framework requires accurate estimates of deforestation. These are derived by ground measurements supported by methods based on remote sensing data to automatically detect and delineate deforestations over large areas. In particular, in the tropics, optical data is seldom available due to cloud cover. As synthetic aperture radar (SAR) data overcomes this limitation, we performed a separability analysis of two statistical metrics based on the Sentinel-1 SAR backscatter over forested and deforested areas. We compared the range between the 5th and 95th temporal percentiles (PRange) and the recurrence quantification analysis (RQA) Trend metric. Unlike the PRange, the RQA Trend considers the temporal order of the SAR data acquisitions, thus contrasting between dropping backscatter signals and yearly seasonalities. This enables the estimation of the timing of deforestation events. We assessed the impact of polarization, acquisition orbit, and despeckling on the separability between forested and deforested areas and between different deforestation timings for two test sites in Mexico. We found that the choice of the orbit impacts the detectability of deforestation. In all cases, VH data showed a higher separability between forest and deforestations than VV data. The PRange slightly outperformed RQA Trend in the separation between forest and deforestation. However, the RQA Trend exceeded the PRange in the separation between different deforestation timings. In this study, C-Band backscatter data was used, although it is commonly not considered as the most suitable SAR dataset for forestry applications. Nevertheless, our approach shows the potential of dense C-Band backscatter time series to support the REDD+ framework.

Estimation of rice yield from a C-band radar remote sensing image by integrating a physical scattering model and an optimization algorithm

Accurate estimation of rice yield with remotely sensed data plays a role in ensuring food security at local or national scales. In this study, an estimation scheme integrating a rice canopy scattering model (RCSM) and a genetic algorithm optimization tool (GAOT) was proposed on a basis of radar remote sensing technology. A C-band Radarsat-2 synthetic aperture radar (SAR) image acquired at rice maturity stage in Northeast, China was tested to simulate three yield-related rice parameters via an integrated RCSM-GAOT scheme, with a parallel computing environment. Rice yield was then estimated via a regression analysis by linking the simulated parameters to rice ear weights. Results showed that three parameters, ear length, ear diameter and ear density, can be simulated by the RCSM-GAOT scheme with a simulation error of 1.2 cm, 0.11 cm and 29 ears/m2, respectively. Rice yield is estimated with an average error of 0.28 kg/m2 and 0.22 kg/m2 for fresh ear weight and dry ear weight, respectively. In comparison with the need of multiple SAR data acquisitions in past studies, this study demonstrates the capability of one time Radarsat-2 quad-polarized SAR image in regional rice yield estimation. At the same time, it also indicates that the proposed RCSM-GAOT scheme has a potential in future precision agriculture applications.

Flood Mapping in a Complex Environment Using Bistatic TanDEM-X/TerraSAR-X InSAR Coherence

In this paper, we assess the flood mapping capabilities of the X-band Synthetic Aperture Radar (SAR) imagery acquired by the bistatic pair TanDEM-X/TerraSAR-X (TDX/TSX). The main objective is to investigate the added value of the bistatic TDX/TSX Interferometric Synthetic Aperture Radar (InSAR) coherence in addition to the SAR backscatter in the context of inundation mapping. As a classifier, we consider a Random Forest (RF) classification scheme using TDX/TSX SAR intensities and their bistatic InSAR coherence to extract the flood extent map. To evaluate the classification results and as no “ground truth” was available at the SAR data acquisition time, we set up a LISFLOOD-FP hydraulic model for simulating the temporal evolution of the flood water. The flood map simulated by the model shows good performances with an Overall Accuracy (OA) of 97.92 % and a Critical Success Index (CSI) of 94 . 01 % . The SAR-derived flood map is then compared to the LISFLOOD-FP extent map simulated at the SAR data acquisition time. As a test case, we consider the flooding event of the Richelieu River that occurred in the Montérégie region of Quebec (Canada) from April to June 2011. Experimental results highlight the potential of the bistatic InSAR coherence for more accurate flood mapping in a complex landscape with urban and vegetation areas. The classification results of the SAR-derived flood map with respect to the LISFLOOD-FP flood map reach an OA of 78.65 % and a Precision of 82.08 % when integrating the bistatic InSAR coherence. These classification OA and Precision values are 69.63 % and 64.52 % , respectively, using only the TDX/TSX SAR intensity.

Soil Moisture Mapping Using Sentinel-1A Synthetic Aperture Radar Data

The aim of this study is to estimate and map soil moisture distribution using C-band Synthetic Aperture Radar (SAR) data. Sentinel-1A is a new generation C-band SAR satellite, and in this study Sentinel-1A data acquired on 24 April 2016 were used to retrieve soil moisture map. An agricultural region in Bergama, a district of İzmir city, was chosen as the study area. In-situ soil moisture measurements were carried out in 20 test fields simultaneously with SAR data acquisition. The effects of soil moisture and local incidence angle on backscattering coefficient were analyzed, and then a multiple regression analysis was performed to generate an empirical model. The proposed model was evaluated using statistical metrics namely coefficient of determination (R2) and Root Mean Square Error (RMSE), and the results were 0.84 and 2.46 %, respectively. The obtained results showed that Sentinel-1A SAR data presented satisfying outcomes to estimate and map soil moisture content.

Physical-based soil moisture retrieval method over bare agricultural areas by means of multi-sensor SAR data

ABSTRACTThe main objective of this research is to develop, test and validate soil moisture retrieval method based on multi-source SAR (Synthetic Aperture Radar) data for bare agricultural areas. The Radardat-2, TerraSAR-X and Sentinel-1A SAR data were applied to retrieve soil moisture content in combination with the integral equation model (IEM) or calibrated integral equation model (CIEM). A straightforward inversion scheme was developed, which does not require the prior knowledge of surface roughness. The soil moisture content can be directly estimated using a look-up table (LUT) optimization method with multi-source SAR data as inputs. For validation purpose, in situ soil moisture content was measured during the period of SAR data acquisitions. The effectiveness and reliability of the soil moisture retrieval methods were evaluated based on the in situ measurements and cost function distribution graph. The experimental results indicate that the developed approach provided accurate soil moisture estimates with root mean square errors (RMSE) ranging from 0.047 cm3 cm−3 to 0.079 cm3 cm−3 over the experimental areas. The distribution graphs of the cost function demonstrate the uniqueness and convergence of the estimated results based on multi-source SAR data. Either IEM or CIEM was employed to estimate soil moisture content, more accurate results were obtained with Radarsat-2, TerraSAR-X and Sentinel-1A data as inputs. The experimental results preliminary illustrate that the multi-source SAR data are promising for soil moisture retrieval over bare agricultural areas. The novelty of the presented research can be summarized as two aspects. Firstly, the multi-sensor SAR with different incidence angle, different frequency and different polarization were combined to estimate soil moisture content by means of the physical-based methods. The combination of the multi-sensor SAR data can effectively solve the ill-posed problem of soil moisture retrieval using physical models. Secondly, the CIEM was utilized to establish the soil moisture retrieval model, which transforms the three unknown parameters to two unknown parameters. Furthermore, the convergence and uniqueness of the estimated soil moisture were validated through distribution graphs of the cost function.

Multicarrier SAR Image Reconstruction Using Integrated MUSIC-LSE Algorithm

In synthetic aperture radar (SAR) applications, high-resolution images and effective estimation processes are vital for the reconstruction of any targets. This can be achieved by using multicarrier waveforms such as orthogonal frequency division multiplexing (OFDM) with the help of appropriate signal processing algorithms. However, the quality of the reconstructed image degrades in low signal-to-noise ratio (SNR) environments during SAR data acquisition. In this paper, an integrated multiple signal classification (MUSIC) assisted least square estimation (LSE) algorithm (MUSIC-LSE) is proposed to enhance the quality of the reconstructed SAR image in a low-SNR environment. Simulation results measured and evaluated the quality of the reconstructed image using three performance indicators of root-mean-square-error, main lobe width and cumulative side lobe levels. These indicators are also used to investigate the effect of OFDM subcarrier selection on the reconstructed image for a different number of subcarriers. Experimental validation of the approach is carried out using two steel pipes to image and detect the curvature of the steel pipes. The results show that the proposed MUSIC-LSE approach produces better-reconstructed images compared with the existing linear frequency modulated (LFM) chirp and OFDM-LSE approaches in low-SNR (−10 dB) environments and enables the radar to distinguish and detect the curvature of the pipes even below the radar range and cross-range resolution.

Tuning of level-set speed function for speckled image segmentation

The segmentation of speckled images, as the synthetic aperture radar (SAR) images, is usually recognized as a very complex problem, because of the speckle, multiplicative noise, which produces granular images. In segmentation problems, based on level set method, the evolution of the curve is determined by a speed function, which is fundamental to achieve a good segmentation. In this paper we propose a study of the new speed function obtained by the linear combination of image average intensity and image gradient speed functions. Thus the aim is tuning the combined speed in the segmentation process. We segmented synthetic images by tuning parameters of the new speed function and we evaluated the best computed results. Then we applied this experimental setup to real SAR images, which are PRecision Images, acquired during European Remote Sensing mission, and a Cosmo-SkyMed image. In particular, we are interested in monitoring complex areas with low light covered by clouds, as coastlines and polar regions may be. In Earth Observation, the acquisition of SAR data becomes fundamental, since the SAR sensor can work in the night/day and in all weather conditions.

Classification of Vessels in Single-Pol COSMO-SkyMed Images Based on Statistical and Structural Features

Vessel monitoring is one of the most important maritime applications of Synthetic Aperture Radar (SAR) data. Because of the dihedral reflections between the vessel hull and sea surface and the trihedral reflections among superstructures, vessels usually have strong backscattering in SAR images. Furthermore, in high-resolution SAR images, detailed information on vessel structures can be observed, allowing for vessel classification in high-resolution SAR images. This paper focuses on the feature analysis of merchant vessels, including bulk carriers, container ships and oil tankers, in 3 m resolution COSMO-SkyMed stripmap HIMAGE mode images and proposes a method for vessel classification. After preprocessing, a feature vector is estimated by calculating the average value of the kernel density estimation, three structural features and the mean backscattering coefficient. Support vector machine (SVM) classifier is used for the vessel classification, and the results are compared with traditional methods, such as the K-nearest neighbor algorithm (K-NN) and minimum distance classifier (MDC). In situ investigations are conducted during the SAR data acquisition. Corresponding Automatic Identification System (AIS) reports are also obtained as ground truth to evaluate the effectiveness of the classifier. The preliminary results show that the combination of the average value of the kernel density estimation and mean backscattering coefficient has good ability for classifying the three types of vessels. When adding the three structural features, the results slightly improve. The result of the SVM classifier is better than that of K-NN and MDC. However, the SVM requires more time, when the parameters of the kernel are estimated.

New SAR image interpretation method of aircraft based on joint time-frequency analysis

With the continuous improvement of Synthetic Aperture Radar (SAR) resolution, interpreting the small targets like aircraft in SAR images becomes possible and turn out to be a hot spot in SAR application research. However, due to the complexity of SAR imaging mechanism, interpreting targets in SAR images is a tough problem. This paper presents a new aircraft interpretation method based on the joint time-frequency analysis and multi-dimensional contrasting of basic structures. Moreover, SAR data acquisition experiment is designed for interpreting the aircraft. Analyzing the experiment data with our method, the result shows that the proposed method largely makes use of the SAR data information. The reasonable results can provide some auxiliary support for the SAR images manual interpretation.

Performance of the high-resolution atmospheric model HRRR-AK for correcting geodetic observations from spaceborne radars.

[1] Atmospheric phase delays are considered to be one of the main performance limitations for high-quality satellite radar techniques, especially when applied to ground deformation monitoring. Numerical weather prediction (NWP) models are widely seen as a promising tool for the mitigation of atmospheric delays as they can provide knowledge of the atmospheric conditions at the time of Synthetic Aperture Radar data acquisition. However, a thorough statistical analysis of the performance of using NWP production in radar signal correction is missing to date. This study provides a quantitative analysis of the accuracy in using operational NWP products for signal delay correction in satellite radar geodetic remote sensing. The study focuses on the temperate, subarctic, and Arctic climate regions due to a prevalence of relevant geophysical signals in these areas. In this study, the operational High Resolution Rapid Refresh over the Alaska region (HRRR-AK) model is used and evaluated. Five test sites were selected over Alaska (AK), USA, covering a wide range of climatic regimes that are commonly encountered in high-latitude regions. The performance of the HRRR-AK NWP model for correcting absolute atmospheric range delays of radar signals is assessed by comparing to radiosonde observations. The average estimation accuracy for the one-way zenith total atmospheric delay from 24 h simulations was calculated to be better than ∼14 mm. This suggests that the HRRR-AK operational products are a good data source for spaceborne geodetic radar observations atmospheric delay correction, if the geophysical signal to be observed is larger than 20 mm.

DEVELOPING TROPICAL LANDSLIDE SUSCEPTIBILITY MAP USING DINSAR TECHNIQUE OF JERS-1 SAR DATA

Comprehensive information in natural disaster area is essential to prevent and mitigate people from further damage that might occur before and after such event. Mapping this area is one way to comprehend the situation when disaster strikes. Remote sensing data have been widely used along with GIS to create a susceptibility map. The objective of this study was to develop existing landslides susceptibility map by integrating optical satellite images of Landsat ETM and ASTER with Japanese Earth Resource Satellites (JERS-1) Synthetic Aperture Radar (SAR) data complemented by ground GPS and feature measurement into a Geographical Information Systems (GIS) platform. The study area was focused on a landslide event occurred on 26 March 2004 in Jeneberang Watershed of South Sulawesi, Indonesia. Change detection analysis was used to extract thematic information and the technique of Differential SAR Interferometry (DInSAR) was employed to detect slight surface displacement before the landslide event. The DInSAR processed images would be used to add as one weighted analysis factor in creating landslide susceptibility map. The result indicated that there was a slight movement of the slope prior to the event of landslide during the JERS-1 SAR data acquisition period of 1993-1998.
 Keywords: Optical Images, JERS-1 SAR, DInSAR, Tropical Landslide, GIS, Susceptibility Map
 1. Introduction
 Recently, natural disasters increased in terms of frequency, complexity, scope, and destructive capacity. They have been particularly severe during the last few years when the world has experienced several large-scale natural disasters such as the Indian Ocean earthquake and tsunami; floods and forest fires in Europe, India and China, and drought in Africa (Sassa, 2005). Mapping such natural disaster areas is essential to prevent and mitigate people from further damage that might occur before and after such event.
 In Indonesia in particular, in these recent years natural disasters occurred more frequently compared to the last decade (BNPB, 2008). Once within a month in 2011, in three different islands, Indonesia was stricken by earthquake, tsunami, flash floods, and volcanic eruptions with severe fatalities to the people and environment. It was obvious that Indonesia was prone to natural disaster due to its position of being squeezed geologically by three major world plates and this fact makes Indonesia one of the most dangerous

A new data acquisition and processing system for UAVSAR

Synthetic Aperture Radar (SAR) is widely known as a high resolution imaging system in microwave remote sensing. Large number of frequency-modulated received echoes must be acquired in real time. A typical SAR data acquisition unit (DAQ) involves high speed analog-to-digital conversion, front-end pre-processing, and data recording. Subsequent processes consist of computationally intensive digital signal processing for image formation. In this paper, an efficient data acquisition and SAR processing method is proposed. It is based on a modified discrete Fourier Transform algorithm, which requires lesser system's computational load as compared to conventional Fast Fourier Transform. The proposed system has been implemented on an UAVSAR (Unmanned Aerial Vehicle SAR) and the flight tests have shown promising results for real-time imaging.

Classification of crop and soil homogenous zones using multipolarization C-band SAR

With the acquisition of polarimetric radar data from satellites such as RADARSAT-2 and ALOS PALSAR, this research project explored the potential of using multipolarization C-band synthetic aperture radar (SAR) data to map homogenous soil and crop characteristics within agricultural fields. Homogenous zones within crop fields were defined using coincidental apparent soil electrical conductivity (EC) measured from a soil EC mapping system, green leaf area index (LAI) derived from airborne hyperspectral CASI data, and yield converted to phytomass at harvest (Ph) derived from a yield monitor. Airborne polarimetric C-band SAR images (5.6 cm) at 4 m spatial resolution were obtained over a test site in Ottawa, Canada, on 13 June, 26 June, and 19 July 2001. Linear (HH, VV, HV) and circular (RR, LL, RL) polarization images were synthesized from these data. Once the homogenous zones were determined by EC, LAI, and Ph, the multipolarization SAR data were classified based on these zones using single polarizations and combinations of linear and circular polarizations. Within-field classification accuracies of SAR data were higher when they were based on EC and LAI homogenous zones relative to Ph delineations. The lower SAR classification accuracies based on Ph zones were likely attributed to biomass zones defined relative to harvest rather than coincident with SAR data acquisition. LAI zones were better classified using early season SAR data when greater between-zone variability was present. EC homogenous zones were better classified using SAR data later in the growing season when soil variability was manifested in the vegetation response. Within-field classification performance over the season was dependent on the crop type and the incidence angle. At larger incidence angles (55°), results were more dependent on crop type when compared with smaller incidence angles (35°). In general, a single polarization was capable of discriminating many of the within-field homogenous zones, including EC and LAI zones in corn and LAI and Ph zones in wheat. The linear (HV) and circular (LL or RR) cross-polarizations were the best single polarizations to classify these homogenous zones. Within-field classification of the SAR into EC homogenous zones in the wheat field required polarization combinations, particularly combinations that included HH and VV polarizations. Circular polarizations, either as single polarizations or as part of a combination, were necessary to classify SAR data into EC or LAI homogenous zones as the season progressed. These results suggest that multipolarization SAR data are useful in classifying within-field homogenous zones of corn and wheat, but that this application will require access to data from sensors that synthesize circular polarizations.