Jiali Shang

Compact polarimetry overview and applications assessment

A synthetic aperture radar (SAR) with hybrid-polarity (CL-pol) architecture transmits circular polarization and receives two orthogonal, mutually coherent linear polarizations, which is one manifestation of compact polarimetry. The resulting radar is relatively simple to implement and has unique self-calibration features and low susceptibility to noise. It also enables maintenance of a larger swath coverage than fully polarimetric SAR systems. A research team composed of various departments of the Government of Canada evaluated this compact polarimetry mode configuration for application to soil moisture estimation, crop identification, ship detection, and sea-ice classification. This paper presents an overview of compact polarimetry, the approach developed for evaluation, and preliminary results for applications important to the Government of Canada. The implications of the results are also discussed with respect to future SAR missions such as the Canadian RADARSAT Constellation Mission, the American DESDynI, and India’s RISAT.

The Contribution of ALOS PALSAR Multipolarization and Polarimetric Data to Crop Classification

Mapping and monitoring changes in the distribution of cropland provide information that aids sustainable approaches to agriculture and supports early warning of threats to global and regional food security. This paper tested the capability of Phased Array type L-band Synthetic Aperture Radar (SAR) (PALSAR) multipolarization and polarimetric data for crop classification. L-band results were compared with those achieved with a C-band SAR data set (ASAR and RADARSAT-1), an integrated C- and L-band data set, and a multitemporal optical data set. Using all L-band linear polarizations, corn, soybeans, cereals, and hay-pasture were classified to an overall accuracy of 70%. A more temporally rich C-band data set provided an accuracy of 80%. Larger biomass crops were well classified using the PALSAR data. C-band data were needed to accurately classify low biomass crops. With a multifrequency data set, an overall accuracy of 88.7% was reached, and many individual crops were classified to accuracies better than 90%. These results were competitive with the overall accuracy achieved using three Landsat images (88.0%). L-band parameters derived from three decomposition approaches (Cloude-Pottier, Freeman-Durden, and Krogager) produced superior crop classification accuracies relative to those achieved using the linear polarizations. Using the Krogager decomposition parameters from all three PALSAR acquisitions, an overall accuracy of 77.2% was achieved. The results reported in this paper emphasize the value of polarimetric, as well as multifrequency SAR, data for crop classification. With such a diverse capability, a SAR-only approach to crop classification becomes increasingly viable.

The Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12): Prelaunch Calibration and Validation of the SMAP Soil Moisture Algorithms

The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) satellite is scheduled for launch in January 2015. In order to develop robust soil moisture retrieval algorithms that fully exploit the unique capabilities of SMAP, algorithm developers had identified a need for long-duration combined active and passive L-band microwave observations. In response to this need, a joint Canada-U.S. field experiment (SMAPVEX12) was conducted in Manitoba (Canada) over a six-week period in 2012. Several times per week, NASA flew two aircraft carrying instruments that could simulate the observations the SMAP satellite would provide. Ground crews collected soil moisture data, crop measurements, and biomass samples in support of this campaign. The objective of SMAPVEX12 was to support the development, enhancement, and testing of SMAP soil moisture retrieval algorithms. This paper details the airborne and field data collection as well as data calibration and analysis. Early results from the SMAP active radar retrieval methods are presented and demonstrate that relative and absolute soil moisture can be delivered by this approach. Passive active L-band sensor (PALS) antenna temperatures and reflectivity, as well as backscatter, closely follow dry down and wetting events observed during SMAPVEX12. The SMAPVEX12 experiment was highly successful in achieving its objectives and provides a unique and valuable data set that will advance algorithm development.

Canadian Experiment for Soil Moisture in 2010 (CanEx-SM10): Overview and Preliminary Results

The Canadian Experiment for Soil Moisture in 2010 (CanEx-SM10) was carried out in Saskatchewan, Canada, from 31 May to 16 June, 2010. Its main objective was to contribute to Soil Moisture and Ocean Salinity (SMOS) mission validation and the prelaunch assessment of the proposed Soil Moisture Active and Passive (SMAP) mission. During CanEx-SM10, SMOS data as well as other passive and active microwave measurements were collected by both airborne and satellite platforms. Ground-based measurements of soil (moisture, temperature, roughness, bulk density) and vegetation characteristics (leaf area index, biomass, vegetation height) were conducted close in time to the airborne and satellite acquisitions. Moreover, two ground-based in situ networks provided continuous measurements of meteorological conditions and soil moisture and soil temperature profiles. Two sites, each covering 33 km × 71 km (about two SMOS pixels) were selected in agricultural and boreal forested areas in order to provide contrasting soil and vegetation conditions. This paper describes the measurement strategy, provides an overview of the data sets, and presents preliminary results. Over the agricultural area, the airborne L-band brightness temperatures matched up well with the SMOS data (prototype 346). The radio frequency interference observed in both SMOS and the airborne L-band radiometer data exhibited spatial and temporal variability and polarization dependency. The temporal evolution of the SMOS soil moisture product (prototype 307) matched that observed with the ground data, but the absolute soil moisture estimates did not meet the accuracy requirements (0.04 m3/m3) of the SMOS mission. AMSR-E soil moisture estimates from the National Snow and Ice Data Center more closely reflected soil moisture measurements.

Early season monitoring of corn and soybeans with TerraSAR-X and RADARSAT-2

Abstract Early and on-going crop production forecasts are important to facilitate food price stability for regions at risk, and for agriculture exporters, to set market value. Most regional and global efforts in forecasting rely on multiple sources of information from the field. With increased access to data from spaceborne Synthetic Aperture Radar (SAR), these sensors could contribute information on crop acreage. But these acreage estimates must be available early in the season to assist with production forecasts. This study acquired TerraSAR-X and RADARSAT-2 data over a region in eastern Canada dominated by economically important corn and soybean production. Using a supervised decision tree classifier, results determined that either sensor was capable of delivering highly accurate maps of corn and soybeans at the end of the growing season. Accuracies far exceeded 90%. Spatial and multi-temporal filtering approaches were compared and small improvements in accuracies were found by applying the multi-temporal filter to the RADARSAT-2 data. Of significant interest, this study determined that by using only three TerraSAR-X images corn could be accurately identified by the end of June, a mere six weeks after planting and at a vegetative growth stage (V6 – sixth leaf collar developed). However, soybeans required additional acquisitions given the variance in planting densities and planting dates in this region of Canada. In this case, accurate soybean classification required TerraSAR-X images until early August at the start of the reproductive stage (R5 – seed development is beginning). Also important, by applying a multi-temporal filter accurate mapping (close to 90%) of corn and soybeans from RADARSAT-2 could occur five weeks earlier (by August 19) than if a spatial filter was used. Thus application of this filtering approach could accelerate delivery of a crop inventory for this region of Canada. Corn and soybeans are important commodities both globally and within Canada. This study makes an important contribution as it demonstrates that TerraSAR-X can deliver acreage estimates of these two crops early enough to assist with in-season production forecasting.

Multiyear Crop Monitoring Using Polarimetric RADARSAT-2 Data

This paper studies the feasibility of monitoring crop growth based on a trend analysis of three elementary radar scattering mechanisms using three consecutive years (2008-2010) of RADARSAT-2 (R-2) Fine Quad Mode data. The polarimetric synthetic aperture radar analysis is based on the Pauli decomposition. Multitemporal analysis is applied to RGB images constructed using surface scattering, double-bounce, and volume scattering, along with intensity analysis of these scattering mechanisms. The test site is located in Eastern Ontario, Canada where the cropping system is dominated by corn, spring wheat, and soybeans. Each crop has unique physical structural characteristics which provide different responses for these scattering mechanisms. Significant changes occur in these scattering mechanisms as the crops move from one phenological stage to the next. By monitoring these changes over the season, the crop growth cycle from emergence to harvest can be observed. When harvest occurs, the backscatter intensities change significantly, and these changes aid in identifying crops. The temporal evaluation of the intensity of the scattering mechanisms generally track the measured leaf area index and observed phenological plant development. Changes in growth stage are crop type specific. Thus, to monitor changes in crop phenology and the occurrence of harvest activities, knowledge of the crop grown in any particular field is required. To accommodate this requirement, a maximum likelihood classification was performed on the R-2 data to produce a crop map. An overall classification accuracy of 85% was achieved.

The sensitivity of RADARSAT-2 polarimetric SAR data to corn and soybean leaf area index

In this study, quadrature-polarization (quad-pol) RADARSAT-2 data at steep (25o) and shallow (40o) incidence angles were acquired during the 2008 season, imaging 13 corn and soybean fields. The leaf area index (LAI) was derived from optical imagery, and volumetric soil moisture was measured coincident with each overpass. Many synthetic aperture radar (SAR) parameters were significantly correlated with derived corn and soybean LAI. The highest correlations were observed for parameters sensitive to volume scattering (HV, LL, and RR backscatter, pedestal height, and the Freeman–Durden volume-scattering parameter) at the steeper angle. For corn, the minimum correlation coefficient was 0.95. For soybeans, the coefficients were between 0.83 and 0.86. Sensitivity to LAI was lost late in the season, when the derived LAI exceeded 3.0 m2m−2. The derived LAI and the measured soil moisture were used to model several radar parameters (HV backscatter, pedestal height, and the Freeman–Durden volume-scattering parameter) using the water-cloud model. Early in the season, the SAR response was primarily affected by the vegetation, but soil moisture was also an important contributor. When the derived LAI exceeded 1, soil-moisture contributions became minimal. The water-cloud model adequately simulated SAR responses as the canopy developed and LAI increased, demonstrating the potential of polarimetric SAR data for monitoring indicators of crop productivity.

RADARSAT-2 Polarimetric SAR Response to Crop Biomass for Agricultural Production Monitoring

Agricultural production monitoring plays a key role in a variety of economic and environmental practices including crop yield forecasting, identifying risk of disease and application of chemicals. Remote sensing has the potential to provide accurate crop condition information across large areas and has the ability to deliver information products in a timely within-season manner. Synthetic aperture radar (SAR) frequencies are unaffected by most atmospheric conditions, making use of this technology of interest to crop monitoring. In this study, RADARSAT-2 polarimetric SAR responses of 21 parameters are compared with dry biomass of canola, corn, soybean, and spring wheat crops over a 6-week period for a site in western Canada. Dry biomass was targeted as this variable is a strong predictor of crop productivity. During the period of biomass accumulation, significant correlations with dry biomass were observed for most SAR parameters, for corn, canola, and soybeans. These findings are of interest as they could be used to target fungicide applications (canola) and to determine silage yields and resistance to disease (corn). For spring wheat, linear cross polarization and circular cross polarization backscatter, volume scattering and pedestal height were able to detect when wheat entered the milking stage which could prove useful as an indicator for the timing of spring wheat harvest. This study demonstrates that polarimetric SAR responds to accumulation of dry biomass, but as well that several radar parameters can uniquely identify changes in crop structure and phenology.

Towards operational radar-only crop type classification: comparison of a traditional decision tree with a random forest classifier

The potential of a random forest (RF) classifier for radar-only crop classifications was evaluated for an eastern and western Canadian site. Overall classification accuracies were improved by approximately 4%–5% over traditional boosted decision trees with gains of up to 7% in the accuracies of specific classes. Accuracies above 85% were obtained for key crops including canola, soybeans, corn, and wheat. Variable importance measures generated by the RF classifier showed that the most important acquisitions occurred in late August to early September at peak biomass and after wheat harvest. The least important images were acquired in May and mid-July. The HV and VV polarizations had the most significant contributions, while the HH polarization contributed little throughout the season, except in late September when the HH response was largely driven by soil conditions. The sensitivity of three RF parameters (number of training pixels, number of trees, and number of variables to select from at each split) was evaluated and shown to have negligible influence on overall accuracy. The RF classifier provided large performance gains in terms of processing time relative to the decision tree classifier. The operational potential and implementation considerations for radar-only Canada-wide crop type mapping are discussed in the context of these results.

TerraSAR-X and RADARSAT-2 for crop classification and acreage estimation

This research outlines a preliminary assessment of the use of TerraSAR-X data for classifying agricultural crop land in Canada. X-Band data were able to identify crops (pasture-forage, soybeans, corn and wheat) to accuracies of 95% once a post-classification filter was applied. These accuracies were achieved using six TerraSAR-X images from 2008 and a decision-tree classification algorithm. Acquisitions began only mid-season and consequently a second full season TerraSAR-X data set is being collected in 2009. C-Band classification accuracies were about 10% lower in comparison. These results clearly demonstrate the potential of X-Band data for crop identification.