Heather McNairn

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.

Assessment of the SMAP Passive Soil Moisture Product

The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) satellite mission was launched on January 31, 2015. The observatory was developed to provide global mapping of high-resolution soil moisture and freeze-thaw state every two to three days using an L-band (active) radar and an L-band (passive) radiometer. After an irrecoverable hardware failure of the radar on July 7, 2015, the radiometer-only soil moisture product became the only operational soil moisture product for SMAP. The product provides soil moisture estimates posted on a 36 km Earth-fixed grid produced using brightness temperature observations from descending passes. Within months after the commissioning of the SMAP radiometer, the product was assessed to have attained preliminary (beta) science quality, and data were released to the public for evaluation in September 2015. The product is available from the NASA Distributed Active Archive Center at the National Snow and Ice Data Center. This paper provides a summary of the Level 2 Passive Soil Moisture Product (L2_SM_P) and its validation against in situ ground measurements collected from different data sources. Initial in situ comparisons conducted between March 31, 2015 and October 26, 2015, at a limited number of core validation sites (CVSs) and several hundred sparse network points, indicate that the V-pol Single Channel Algorithm (SCA-V) currently delivers the best performance among algorithms considered for L2_SM_P, based on several metrics. The accuracy of the soil moisture retrievals averaged over the CVSs was 0.038 m3/m3 unbiased root-mean-square difference (ubRMSD), which approaches the SMAP mission requirement of 0.040 m3/m3.

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.

First order surface roughness correction of active microwave observations for estimating soil moisture

Surface roughness has a significant effect on the relationship between radar backscatter and soil moisture. In order to use existing radar satellite data for soil moisture, roughness effects must be corrected. A technique is presented that utilizes the data bases from soil erosion studies and soil moisture remote sensing investigations to provide first order estimates of the roughness parameters.

Validation of a hyperspectral curve-fitting model for the estimation of plant water content of agricultural canopies

Abstract The estimation of plant water content is essential to the integration of remote sensing into precision agriculture. Hyperspectral models developed to estimate plant water content have had limited application under field conditions and have not been rigorously validated. A physical model using a spectrum matching technique was applied to hyperspectral data to directly calculate the canopy equivalent water thickness (EWT) using a look-up table approach. The objective of this study was to test the validity of this algorithm using plant water content information collected under field conditions and to relate this to the needs of precision agriculture. Image data were acquired over two experimental test sites in Canada, near Clinton, Ontario and Indian Head, Saskatchewan, using the Probe-1 airborne hyperspectral sensor. Plant biomass samples were collected simultaneously from plots spanning fourteen fields of various crop types (wheat, canola, corn, beans, and peas). The model was validated against EWT estimated from biomass samples, as well as more conventional measures of crop water status. The model accurately predicts water content in the range found with all crop types pooled together, with an index of agreement ( D ) of 0.92 and a root mean squared error (RMSE) of 26.8% of the average. On an individual crop basis, the model proved to be a poor predictor for wheat crops (RMSE=69.9%). When wheat fields were removed from the overall analysis, the RMSE was 17.9% and the D was 0.87. While the model provided a reasonably accurate prediction of EWT for broadleaf crops like beans, corn, canola, and peas ( D =0.88, 0.69, 0.88, and 0.84, respectively), the error margin in the prediction was too large for to precisely detect within-crop variation for the low variability found in corn and bean crops in this study. EWT is related to plant biomass and leaf area index (LAI), both quantities of interest to precision agriculture.

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.

Mapping Soil Moisture Using RADARSAT-2 Data and Local Autocorrelation Statistics

The purpose of this study is to evaluate the capability of surface radar backscatter models to estimate soil moisture over agricultural fields from fully polarimetric RADARSAT-2 C-band synthetic aperture radar (SAR) responses. For validation purposes, ground measurements over 44 sampling sites in eastern Ontario, Canada were carried out in the spring of 2008 simultaneously with satellite data acquisitions. Soil moisture retrieval was accomplished using two semi-empirical scattering models (Dubois and Oh) and the SAR image backscatter. Discrepancies between measured radar backscatter coefficients and those predicted by the models were previously reported, requiring correction factors to reduce biases associated with these semi-empirical approaches. Soil moisture was estimated by explicitly solving the two backscatter equations of the Dubois model, and using a look-up table (LUT) approach applied to the Oh model. Results showed that the Oh model in a cross-polarization (HH-HV) and Dubois in a co-polarization (HH-VV) inversion scheme provide the best estimates. These model configurations were implemented to produce multi-date soil moisture maps for the eastern Ontario site. To expand the range of validity of these soil moisture estimates, the maps produced by the Dubois and Oh models were uniquely combined. These estimates of absolute soil moisture were then used to derive spatial patterns of near-surface moisture content using the Getis statistic. The Getis statistic maps provide meaningful spatial information, demonstrating the potential of combining the Getis statistic and RADARSAT-2 data in predicting soil moisture conditions.

A neural network integrated approach for rice crop monitoring

Within Asia, rice is a main source of nutrition and provides between 30 and 70% of the daily calories for half the worlds population. The importance of rice production demands an effective rice crop monitoring system to provide food security for this region. Recent research has proven radars capabilities in rice crop monitoring. Radar backscatter increases significantly during a short period of vegetation growth, but large spatial variations in rice crop growth occur due to shifting in the crop calendar. The significant increase in radar backscatter over a short period of time can be used to differentiate rice fields from other land covers. The inter‐field variations can be used to derive information on local farmer practices and to enhance rice field mapping and yield prediction. The rice crop monitoring system developed in this project was based on these variations as applied to a neural network classification. The system delineated rice production areas for one wet and one dry season, and was able to extract information on rice cultivation as a function of different planting dates. A minimum mapping accuracy of 96% was achieved for both seasons. This information was then used in a neural network‐based yield model to predict rice yield on a regional basis for the wet season. When the yields predicted by the neural network were compared with government statistics, the result was a prediction accuracy of 94%.

Evaluation of soil moisture derived from passive microwave remote sensing over agricultural sites in Canada using ground-based soil moisture monitoring networks

Passive microwave soil moisture datasets can be used as an input to provide an integrated assessment of climate variability as it relates to agricultural production. The objective of this research was to examine three passive microwave derived soil moisture datasets over multiple growing seasons in contrasting Canadian agricultural environments. Absolute and relative soil moisture was evaluated from two globally available datasets from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) sensor using different retrieval algorithms, as well as relative soil wetness at a weekly scale from the Special Sensor Microwave/Imager (SSM/I) sensor. At a daily scale, the Land Parameter Retrieval Model (LPRM) provides a better estimate of surface soil moisture conditions than the National Snow and Ice Data Center (NSIDC) dataset, with root mean squared errors ranging from 5 to 10% for LPRM and 12 to 18% for NSIDC soil moisture when a temporal smoothing is applied to the dataset. Both datasets provided better estimates of soil moisture over the temperate site near Elora, Ontario than the prairie site near Davidson, Saskatchewan. The LPRM dataset tends to overestimate soil moisture conditions at both sites, where the NSIDC dataset tends to underestimate absolute soil moisture. These differences in retrieval methods were independent of radiometric frequency used. At weekly scales, the LPRM dataset provides a better relative estimate of wetness conditions when compared to the NSIDC and the Basist Wetness Index (BWI) from SSM/I data, but the SSM/I dataset did provide a reasonably good relative indicator of moisture conditions. The high variability in accuracy of soil moisture estimation related to retrieval algorithms indicates that consistency is needed in these datasets if they are to be integrated in long term studies for yield estimation or data assimilation.