The Canadian RADARSAT Constellation Mission (RCM) has passed its early operation phase, with the current performance evaluation. In this study, we provide results update on RCM performance for selected SAR applications. The RCM was designed to address three core applications-disaster management, maritime surveillance, and ecosystem monitoring. Our study shows a promising level of agreement between RCM and RADARSAT-2 performance in flood mapping using dual-polarized HH-HV SAR data over Red River, Manitoba. Visual analysis of coincident RCM compact polarimetric and RADARSAT-2 dual-polarized HH-HV SAR imagery over the Resolute Passage, Canadian Central Arctic, highlighted an improved contrast between sea ice classes in dry ice winter conditions. Object-oriented classification of a wetland area in Newfoundland and Labrador by fusion of RCM dual-polarized VV-VH data and Sentinel-2 optical imagery revealed promising classification results, with an overall accuracy of 91.1% and a kappa coefficient of 0.87.

Arctic environments are remote and inaccessible, making conventional field-based data collection challenging. Thus, this study describes an efficient desktop-based methodology for deriving reference data to support large-scale remote sensing classification focusing on wetland ecosystems. Our study area was Canada's Southern Arctic Ecozone. Various Earth observation (EO) datasets, including optical, multi-spectral, and topographic, were used as a base to support a photointerpretation process for collecting reference data. Ten 30-by-30-kilometer sampling plots were established across the ecozone for this activity based on a suite of minimum criteria. Reference polygons were assigned to one of the five major wetland classes of the Canadian Wetland Classification System (CWCS), along with a detailed wetland type definition. It is anticipated this methodology will be applied later to other northern ecozones to support large-scale wetland classification updates and status and trends reporting.

While deep learning models have been extensively applied to land-use land-cover (LULC) problems, it is still a relatively new and emerging topic for separating and classifying wetland types. On the other hand, ensemble learning has demonstrated promising results in improving and boosting classification accuracy. Accordingly, this study aims to develop a classification system for mapping complex wetland areas by incorporating deep ensemble learning and satellite datasets. To this end, time series of Sentinel-1 dual-polarized Synthetic Aperture Radar (SAR) dataset, alongside Sentinel-2 multispectral imagery (MSI), are used as input data to the model. In order to increase the diversity of the extracted features, the proposed model, herein called WetNet, consists of three different submodels, comprising several recurrent and convolutional layers. Furthermore, multiple ensembling sections are added to different stages of the model to increase the transferability of the model (to other areas) and the reliability of the final results. WetNet is evaluated in a complex wetland area located in Newfoundland, Canada. Experimental results indicate that WetNet outperforms the state-of-the-art deep models (e.g., InceptionResnetV2, InceptionV3, and DenseNet121) in terms of both the classification accuracy and processing time. This makes WetNet an efficient model for large-scale wetland mapping application. The python code of the proposed WetNet model is available at the following link for the sake of reproducibility:https://colab.research.google.com/drive/1pvMOd3_tFYaMYGyHNfxqDxOiwF78lKgN?usp=sharing

Wetlands are highly sensitive ecosystems that have experienced largely undocumented loss across Canada. Accurate statistics of historic loss of wetlands across many provinces is vague at best or non-existent at worst, as exemplified in Newfoundland and Labrador (NL). Thus, NL represents a perfect candidate for implementing historical remote sensing data sets and change detection methods. Given recent advancements in earth observation technology, it is now feasible to implement remote sensing-based change detection methods at scales never previously possible. As such, the goal of this work is to develop a methodology to assess wetland class change across the island of Newfoundland between 1985 and 2015 using historic and current Landsat imagery, Random Forest classification, and the Google Earth Engine (GEE) platform. The resulting accuracies ranged from 84.37% to 88.96%. The analysis reveals that wetland classes over the last 30 years have been unstable, and the biggest loss of wetlands to anthropogenic land cover occurred between the 1980's and the 1990's. Index Terms - Wetlands, Change Detection, Landsat, Geo big data

Wetlands are important natural resources which provide many benefits to the environment. Consequently, mapping and monitoring wetlands has gained a considerable attention in recent years among remote sensing experts. Wetlands undergo a considerable change within a year. Thus, it is important to study how much various wetland types are distinguishable at different dates. This will help in choosing an appropriate image for wetland classification. On the other hands, combining various satellite images acquired on different dates is a promising approach to obtain a more accurate classified map compared to the map obtained by single-date satellite imagery. In this study, wetlands within a pilot sites, located in Newfoundland were first classified using each of the several available Landsat 8 data, captured in the three seasons of Spring, Summer, and Fall. By doing this, the separability of the wetland classes in each season was analyzed. Then, these multi-temporal data were integrated to obtain a more accurate map of wetlands. The overall classification accuracy of the final map was 88%, proving that using multi-temporal remote sensing data was necessary to obtain a more reliable and accurate map of the dynamic wetlands in the province.

The RADARSAT Constellation Mission (RCM) is a future Canadian Synthetic Aperture Radar (SAR) mission to be launched in July 2018. The hybrid polarity SAR architecture will be included in the RCM mission, allowing the acquisition of compact polarimetric (CP) SAR data in wide swath imagery. In this study, we investigate the potential of the RCM CP SAR StripMap medium resolution mode for multi-temporal wetlands monitoring using a time series of simulated RCM CP data. Test site for this study is the Bay of Quinte, located on the northern shore of Lake Ontario, Canada. Results show that the tested RCM CP mode is promising for wetland monitoring through change detection.

The inundation extent is derived using brightness temperature data acquired by the L-band Soil Moisture Active Passive (SMAP) satellite, to support boreal carbon studies. Exploiting the L-band capabilities to penetrate clouds and vegetation and SMAP's 3-day revisit, the product may complement high-spatial resolution optical products in the high latitudes. The quality of the inundation extent is assessed by comparing with the following data sets: 3-m resolution maps derived using Radarsat synthetic aperture radar (SAR) data in northern Canada and multi-sensor climatology over Siberia. Initial results show encouraging comparisons. SMAP describes the seasonality of inundation more realistically compared with the climatology.

Wetland classification has always been a challenging task among remote sensing experts. Typically, wetland classes have low accuracies regardless of the applied dataset, as they have many spectral and ecological similarities. In this paper, a method is developed particularly effective for distinguishing spectrally similar classes such as wetlands. In this method, feature selection and object-based classification are not done in one step, but instead several feature selections and classifications are applied, and in each level a target class is classified and masked out. While classifying the target class, other spectrally resembling classes are merged so that feature selection is mainly concentrated on separating two classes only. Object-based features were extracted from several SAR and optical images, including RADARSAT-2, ALOS-1, ALOS-2, RapidEye and Landsat-8 images. 15 and 10 percent improvement was obtained in wetlands' average producer and user accuracies compared to the typical feature selection by using the proposed method.

SAR-based image thresholding can be used to detect and map open water body locations and extents. The selection of a reasonable and consistent thresholding approach can be challenging across many complex landcover types. Besides SAR acquisition characteristics, environmental factors (e.g., emergent vegetation and wind-induced roughening of the water) can cause water locally increased backscatter relative to the characteristic dark appearance of water in SAR imagery. This is where much attention has been recently devoted to a variety of thresholding methodologies for surface water monitoring with SAR imagery.

This paper proposed a modified Water Cloud Model (MWCM) for rice variable estimation during the whole growth season with eight RADARSAT-2 quad-pol SAR images. The improvements achieved with the MWCM include considering the heterogeneity of water content of the rice canopy in different directions and different phenologies, and applying the scattering components from an improved polarimetric decomposition in the model instead of the backscattering coefficients. With the MWCM, four rice variables were estimated through the genetic algorithm, including leaf area index (LAI), rice height (h), volumetric water content of total canopy (mv) and ear biomass (De). The validation was conducted using the field data with the average R2of each variable above 0.8. The median relative error (MRE) of the rice variables ranged from 9% to 15% in most phenological stages. The results demonstrated that the MWCM works well for the estimation of rice biophysical parameters with polarimetric SAR data, and it is significant to consider the heterogeneity of water content of the rice canopy in the horizontal direction for estimation of rice variables during the whole rice growth season.

It is commonly-accepted that the enhanced backscatter from wetlands that consist of many emergent stems over open water (swamps and marshes) is dominated by double-bounce backscatter. Recent observations with fully polarimetric data from Radarsat-2 over the extensive wetlands of the Everglades and numerous small wetlands in Ontario are not consistent with this interpretation of the backscatter physics. In this paper we use several forms of polarimetric analysis and decomposition. All of these indicate that the backscatter from small marshes and swamps in Ontario has polarimetric characteristics normally attributed to the odd-bounce mechanism. A recently-proposed form of Bragg scattering provides a conceptual model to explain these observations. However, detailed electromagnetic backscatter modelling is still needed to provide a new and reliable understanding of backscattering from wetlands with emergent vegetation.

The monitoring of wetlands and water extents plays more and more an important key role with respect to climate change. In this paper, a novel technique of SAR image preprocessing using multi-temporal, multi-polarized and multisensor data is described. This technique bases on the Kennaugh element framework which is a very versatile polarimetric descriptor on the one hand and enables an effective image enhancement technique on the other hand. The two practical examples given in this paper illustrate further advantages of the Kennaugh element framework in change detection and change characterization used for the monitoring of temporal variations of the water extent and therewith, the water level and the water quantity.

This paper shows that the m-chi decomposition, the Shannon-Entropy model and the Wishart-Chernoff distance can be used to map and monitor wetlands. Areas which changed from flooded vegetation to non-flooded vegetation were accurately mapped using the m-chi decomposition, and areas that changed from saturated soil to unsaturated soil were visible with the Shannon-Entropy model. In addition, the Wishart-Chernoff distance was able to map wetland areas which had changed to a different land cover type over time.

China is the largest rice producer in the world. Guizhou province is an important rice growing area in the southwest of China. However, rice monitoring with remote sensing data has great difficulties in this region due to its perennial cloud-coverage weather and undulating terrain. With the emergence of polarimetric SAR data and state of art methods for polarization information extraction, rice monitoring in this region is more promising. In this study, multi-temporal RADARSAT-2 polarimetric SAR data set was acquired in Guizhou, China. The Freeman-Durden, Cloude-Pottier and the Touzi decompositions were used for classification and rice scattering mechanism analysis.

The status of permafrost is important for northern environmental and infrastructure monitoring. The use of D-InSAR for large area coverage of terrain stability in permafrost regions is rapidly gaining acceptance. We present here D-InSAR derived terrain stability products for Yellowknife and Tuktoyaktuk in northern Canada and evaluate their information content. RADARSAT-2 data stacks are used to map the seasonal and year to year terrain movement. The potential complication of InSAR signals detecting changing water levels in flooded vegetation is identified. Radar polarimetry is also explored for information content. While polarimetry can characterize surface structure and hence landcover, D-InSAR, with its ability to detect active geomorphological processes, is deemed more immediately useful for monitoring permafrost environments.

A flexible method of introducing a SAR vegetation index into the Dubois model for soil moisture retrieval using polarimetric SAR data is introduced. Based on the vegetation sensitivity at each pixel, the vegetation index is incorporated on a pixel by pixel basis using the water cloud model. An approach for minimizing the need for ground measurements, by remotely estimating the parameters required for the vegetation index, is suggested. The proposed model is applied to CV-580 airborne SAR data and the vegetation correction was found to reduce the rms error in the model.

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.