Justin R. Adams

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.

Sensitivity of C-band SAR polarimetric variables to unvegetated agricultural fields

The interaction of linear polarized microwaves with agricultural features is well understood. Much less is understood about polarimetric data and the potential use of these data to improve surface parameter retrieval models. This paper explores the soil surface information provided by quad-polarimetric SAR through a review of previous work and an empirical sensitivity study of RADARSAT-2 data at four incidence angles. Soil moisture, surface roughness, and crop residue data are quantitatively sampled in unvegetated fields. RADARSAT-2 variables include: linear backscatter and polarization ratios; copolarized phase difference and magnitude of the copolarized complex correlation; pedestal height; extrema of the scattered intensity, completely polarized, and completely unpolarized components; and parameters of the Cloude–Pottier scattering decomposition. Results demonstrated that sensitivities of field averaged linear backscatter were reproduced from previous reports, lending confidence to the experiment. Field averaged pedestal height and copolarized complex correlation coefficient showed significant relationships to crop residue and surface roughness, suggesting an ability to characterize volume and multiple scattering. Similarly, dynamic range of the degree of polarization showed significant relationships with crop residue cover at higher incidence angles. Target averaged or variance of copolarized phase difference did not produce a consistent relationship with the surface parameters, in contrast to qualitative based suggestions of previous experiments. Extremas of the scattered intensity and completely polarized components indicated comparable relationships to surface features as the like polarized linear intensity channels, suggesting sensitivities of these variables to surface scattering. The extrema of the completely unpolarized component showed comparable relationships to surface features as the pedestal height. Results of this paper contribute to identification of optimal SAR variables for use in agricultural monitoring and evaluate potential contributions of polarimetric data for improving surface parameter retrieval models.

Evaluating the Cloude–Pottier and Freeman–Durden scattering decompositions for distinguishing between unharvested and post-harvest agricultural fields

This study evaluates the utility of the Cloude–Pottier and Freeman–Durden scattering decompositions for providing agricultural land surface information during autumn months using C-band polarimetric RADARSAT-2 data. We applied these decompositions over 94 agricultural fields in Southern Ontario, Canada, to characterize scattering mechanisms from unharvested senesced crops and harvested fields with three generalized soil tillage practices. The decompositions were applied to RADARSAT-2 images over six dates during October and November 2010 at high (49°) and low (23°) incidence angles. Agreement was found between the decompositions for the identification of primary (volume and rough surface scatter) scattering mechanisms for the senesced unharvested crops and the harvested fields. Significant statistical separability was observed between the strengths of decomposition parameters when comparing (i) senesced unharvested crops to post-harvest conventional tillage fields and (ii) post-harvest no tillage fields to post-harvest conventional tillage fields. These results suggest that high accuracy classifications may be possible with these data; however, weak separability was observed when comparing fields with conservation tillage. The strongest separability was observed with Entropy and α-angle of the Cloude–Pottier decomposition and the rough surface scattering component of the Freeman–Durden decomposition, suggesting sensitivity of these parameters to surface roughness and crop residue. Results also demonstrated that superior separability was found with the data at the higher 49° incidence angle in contrast to data acquired at the lower 23° incidence angle imagery.

Contributions of C-Band SAR Data and Polarimetric Decompositions to Subarctic Boreal Peatland Mapping

The objective of this paper is to assess the accuracy of C-band synthetic aperture radar (SAR) datasets in mapping peatland types over a region of Canadas subarctic boreal zone. This paper assessed contributions of quad-polarization linear backscatter intensities (σ°HH, σ°HV, σ°VV), image textures, and two polarimetric scattering decompositions: 1) Cloude–Pottier, and 2) Freeman–Durden. Four quad-polarimetric RADADSAT-2 images were studied at incidence angles of 19.4°, 23.1°, 45.8°, and 48.1°. The influence of combining dual-angular information acquired within a short temporal span was also assessed. These C-band SAR data were used to classify peatlands according to isolated flat bogs (bogs), channel fens (fens), raised peat plateaus (plateaus), and forested uplands (uplands) using a supervised support vector machine (SVM) classifier. Numerous classifications were examined to compare the unique contributions of these variables to classification accuracy. Results suggest linear backscatter variables in isolation produce comparable classification results with those of the Freeman–Durden and Cloude–Pottier decompositions. Combining polarimetric decomposition and texture data into classifications with linear backscatter data resulted in only minor (∼1–3%) improvement. Combining classifications from small and large incidence angles (dual-angular) significantly improved classification results over those of a single image. Classification accuracy was the highest for isolated bogs and open water surfaces, whereas fens, uplands, and plateaus had lower accuracies. The highest accuracy classification (84% and kappa coefficient of 0.80) used a dual-angular approach, with additional decomposition and texture information. However, it is noted that texture information rarely improved classification results across all tests. This approach identified isolated flat bogs, channel fens, and raised peat plateaus with >76% producers accuracies.

Evaluation of L-Band passive microwave soil moisture for Canada

Passive microwave derived satellite soil moisture data was evaluated over in situ monitoring sites in Canada from two L-Band sensors. Soil moisture data from the Soil Moisture and Ocean Salinity (SMOS) and the Aquarius mission were used, which collect data at different spatial resolutions and using different retrieval models. Both sensors tend to underestimate soil moisture, with the underestimation from SMOS much more pronounced. Correlation coefficients show a reasonably good correspondence with in situ data, and this correlation tends to be better at sites where sub-grid soil moisture variability is represented in the in situ measured data. This highlights the importance of distributed in situ networks.

Method for upscaling in-situ soil moisture measurements for calibration and validation of smap soil moisture products

In order to provide a reliable source of ground-based validation data for the SMAP mission at spatial scales of 3 km, 9 km and 36 km, we have developed a new regression-based method capable of yielding highly-accurate upscaled soil moisture estimates based on sparse, irregularly-spaced soil moisture measurements.