Karem Chokmani

Optical diversity of thaw ponds in discontinuous permafrost: A model system for water color analysis

[1] Permafrost thaw ponds result from the irregular melting and erosion of frozen soils, and they are active sites of greenhouse gas emissions to the atmosphere throughout the circumpolar North. In the discontinuous permafrost region of Nunavik, Canada, thaw ponds show pronounced differences in color even among nearby ponds, ranging from white to green, brown and black. To quantify this optical variation and to determine its underlying controlling mechanisms, we studied the apparent and inherent optical properties and limnological characteristics of the ponds. The pond colors were well separated on a color coordinate diagram, with axis values determined from above‐water spectral reflectance measurements. Our analyses of optical properties and their empirical relationships with optically active substances showed that the differences in color could entirely be attributed to variations in the concentration of two optically active substances: dissolved organic carbon, which was a major contributor to spectral absorption, and nonalgal suspended particulate matter, which contributed to spectral scattering as well as absorption. The latter component was dominated by small sized particles that had unusually high mass‐specific absorption and scattering properties. Analysis of high spatial resolution, multispectral satellite imagery of these ponds showed that these two optically important constituents could be estimated by multivariate modeling. The results indicate that remote sensing surveys will provide valuable synoptic observations of permafrost thaw ponds across the vast subarctic region, and may allow scaling up of local greenhouse gas flux measurements to regional and circumpolar scales.

Comparative Analysis of Four Models to Estimate Chlorophyll-a Concentration in Case-2 Waters Using MODerate Resolution Imaging Spectroradiometer (MODIS) Imagery

The occurrence and extent of intense harmful algal blooms (HABs) have increased in inland waters during recent decades. Standard monitor networks, based on infrequent sampling from a few fixed observation stations, are not providing enough information on the extent and intensity of the blooms. Remote sensing has great potential to provide the spatial and temporal coverage needed. Several sensors have been designed to study water properties (AVHRR, SeaBAM, and SeaWIFS), but most lack adequate spatial resolution for monitoring algal blooms in small and medium-sized lakes. Over the last decade, satellite data with 250-m spatial resolution have become available with MODIS. In the present study, three models inspired by published approaches (Kahru, Gitelson, and Floating Algae Index (FAI)) and a new approach named APPEL (APProach by ELimination) were adapted to the specific conditions of southern Quebec and used to estimate chlorophyll-a concentration (Chl-a) using MODIS data. Calibration and validation were provided from in situ Chl-a measured in four lakes over 9 years (2000-2008) and concurrent MODIS imagery. MODIS bands 3 to 7, originally at 500-m spatial resolution, were downscaled to 250 m. The APPEL, FAI, and Kahru models yielded satisfactory results and enabled estimation of Chl-a for heavy blooming conditions (Chl-a > 50 mg∙m −3 ), with coefficients of determination reaching 0.95, 0.94, and 0.93, respectively. The model inspired from Gitelson did not provide good estimations compared to the others (R 2 = 0.77).

Spatiotemporal monitoring of soil salinization in irrigated Tadla Plain (Morocco) using satellite spectral indices

Soil salinization is major environmental issue in irrigated agricultural production. Conventional methods for salinization monitoring are time and money consuming and limited by the high spatiotemporal variability of this phenomenon. This work aims to propose a spatiotemporal monitoring method of soil salinization in the Tadla plain in central Morocco using spectral indices derived from Thematic Mapper (TM) and Operational Land Imager (OLI) data. Six Landsat TM/OLI satellite images acquired during 13 years period (2000–2013) coupled with in-situ electrical conductivity (EC) measurements were used to develop the proposed method. After radiometric and atmospheric correction of TM/OLI images, a new soil salinity index (OLI-SI) is proposed for soil EC estimation. Validation shows that this index allowed a satisfactory EC estimation in the Tadla irrigated perimeter with coefficient of determination R2 varying from 0.55 to 0.77 and a Root Mean Square Error (RMSE) ranging between 1.02 dS/m and 2.35 dS/m. The times-series of salinity maps produced over the Tadla plain using the proposed method show that salinity is decreasing in intensity and progressively increasing in spatial extent, over the 2000–2013 period. This trend resulted in a decrease in agricultural activities in the southwestern part of the perimeter, located in the hydraulic downstream.

Wind Speed Estimation Using Polarimetric RADARSAT-2 Images: Finding the Best Polarization and Polarization Ratio

As the number of operational wind scatterometers is getting smaller, other sources of spaceborne sensors are now included in global wind mapping. One of the prominent sensors is the Synthetic Aperture Radar (SAR). Besides serving as a generic scatterometer, SAR systems are the only type of radar systems that can provide sub-km resolution sea surface wind data and offers near shore mapping capability. This unique feature is important for assessing the offshore wind resources. As an important source of renewable energy, offshore wind farms are growing rapidly. Furthermore, recent research shows that the cross-polarization radar backscatter does not seem to saturate in high winds, and provides an excellent supplement for scatterometer wind sensing in storm conditions. The saturation issues of co-polarization radar returns have so far made it difficult to resolve wind speeds beyond roughly 20 m/s, or even less for lower incidence angles. The scope of this paper is to show the potential of RADARSAT-2s polarimetric modes for wind speed retrieval. RADARSAT-2 is the first operational fully polarimetric (HH VV HV VH) C-band satellite. Standard Quad-pol images have been collected in the St. Lawrence Gulf and compared against the Mont-Louis buoy and QuikSCAT scatterometer data. Co-polarization wind speeds were computed with CMOD-5 algorithms. A few polarization ratios were tested to determine the most suitable one for RADARSAT-2s HH polarization mode. For Cross-polarization, two different models were compared. Cross-polarization gives excellent results when wind exceeds 5 m/s. In general, SAR wind retrieval is suitable for resolution of 400 m.

Soil Salinity Characterization Using Polarimetric InSAR Coherence: Case Studies in Tunisia and Morocco

The phenomenon of soil salinization in semi-arid regions is getting amplified and accentuated by both anthropogenic practices and climate change. Land salinization mapping and monitoring using conventional strategies are insufficient and difficult. Our work aims to study the potential of synthetic aperture radar (SAR) for mapping and monitoring of the spatio-temporal dynamics of soil salinity using interferometry. Our contribution in this paper consists of a statistical relationship that we establish between field salinity measurement and InSAR coherence based on an empirical analysis. For experimental validation, two sites were selected: 1) the region of Mahdia (central Tunisia) and 2) the plain of Tadla (central Morocco). Both sites underwent three ground campaigns simultaneously with three Radarsat-2 SAR image acquisitions. The results show that it is possible to estimate the temporal change in soil electrical conductivity (EC) from SAR images through the InSAR technique. It has been shown that the radar signal is more sensitive to soil salinity in HH polarization using a small incidence angle. However, for the HV polarization, a large angle of incidence is more suitable. This is, under considering the minimal influence of roughness and moisture surfaces, for a given InSAR coherence.

Retrieval of River Ice Thickness From C-Band PolSAR Data

River ice has an important effect on natural processes and human activities in northern countries. Current models for estimating river ice thickness are mostly based on environmental data. They require several inputs and yield only a global estimate of ice thickness for a large heterogeneous area. Attempts have been made intending to retrieve river ice thickness from remote sensing using monopolarized C-band radar data. No reliable maps of ice thickness have been produced. In this paper, the potential of polarimetric synthetic aperture radar (PolSAR) data for estimating river ice thickness is demonstrated, and a river ice thickness retrieval model is proposed. The C-band SAR images used in this paper were acquired by Radarsat-2 in the winter of 2009 over the Saint-François River (Southern Quebec), the Koksoak River (Northern Quebec), and the Mackenzie River (Northwest Territories) in Canada. Field campaigns were carried out to obtain ice thickness validation data at 70 locations. Polarimetric entropy was used to obtain ice thickness estimates. This approach results in spatially distributed ice thickness maps for selected ice types.

Monitoring volumetric surface soil moisture content at the La Grande basin boreal wetland by radar multi polarization data.

Understanding the hydrological dynamics of boreal wetland ecosystems (peatlands) is essential in order to better manage hydropower inter-annual productivity at the La Grande basin (Northern Quebec, QC, Canada). Given the remoteness and the huge dimension of the La Grande basin, it is imperative to develop remote sensing monitoring techniques to retrieve hydrological parameters. The main objective of this study is to find out if multi-date and multi-polarization Radar Satellite 2 (RADARSAT-2) (C-band) image analysis could detect seasonal variations of surface soil moisture conditions of the acrotelm. A change detection approach through the use of multi temporal indexes was chosen based on the assumption that the temporal variability of surface roughness and natural vegetation biomass is generally at a much longer time scale than that of surface soil moisture (Δ-Index is based on a reference image that represents dry soil, in order to maximize the sensitivity of σ° to changes in soil moisture with respect to the same location when soil is wet). The Δ-Index approach was tested with each polarization: σ° for fully polarimetric mode (HH, HV, VV) and the cross-polarization coefficient (HV/HH). Results show that the best regression adjustment with regard to surface soil moisture content in boreal wetlands was obtained with the cross-polarization coefficient. The cross-polarization multi-temporal index enables precise volumetric surface soil moisture estimation and monitoring on boreal wetlands, regardless of the influence of vegetation cover and surface roughness conditions (bias was under 1%, standard deviation and RMSE were under 10% for almost all estimation errors). Surface soil moisture estimation was more precise over permanently flooded areas than seasonally flooded ones (standard deviation is systematically greater for the seasonally flooded areas, at all analyzed scales), although the overall quality of the estimation is still precise. Cross-polarization ratio image analysis appears to be a useful mean to exploit radar data spatially, as we were able to relate changes in wetland eco-hydrological dynamics to variations in the intensity of the ratio.

Remote sensing for mapping soil moisture and drainage potential in semi-arid regions: Applications to the Campidano plain of Sardinia, Italy

The aim of this study is to investigate the potential of radar (ENVISAT ASAR and RADARSAT-2) and LANDSAT data to generate reliable soil moisture maps to support water management and agricultural practice in Mediterranean regions, particularly during dry seasons. The study is based on extensive field surveys conducted from 2005 to 2009 in the Campidano plain of Sardinia, Italy. A total of 12 small bare soil fields were sampled for moisture, surface roughness, and texture values. From field scale analysis with ENVISAT ASAR (C-band, VV polarized, descending mode, incidence angle from 15.0° to 31.4°), an empirical model for estimating bare soil moisture was established, with a coefficient of determination (R(2)) of 0.85. LANDSAT TM5 images were also used for soil moisture estimation using the TVX slope (temperature/vegetation index), and in this case the best linear relationship had an R(2) of 0.81. A cross-validation on the two empirical models demonstrated the potential of C-band SAR data for estimation of surface moisture, with and R(2) of 0.76 (bias +0.3% and RMSE 7%) for ENVISAT ASAR and 0.54 (bias +1.3% and RMSE 5%) for LANDSAT TM5. The two models developed at plot level were then applied over the Campidano plain and assessed via multitemporal and spatial analyses, in the latter case against soil permeability data from a pedological map of Sardinia. Encouraging estimated soil moisture (ESM) maps were obtained for the SAR-based model, whereas the LANDSAT-based model would require a better field data set for validation, including ground data collected on vegetated fields. ESM maps showed sensitivity to soil drainage qualities or drainage potential, which could be useful in irrigation management and other agricultural applications.

Estimation de la température de l'air et de la quantité de la vapeur d'eau atmosphérique à l'aide des données AVHRR de NOAA

Detailed measurements of the space-time variability of environmental variables such as the air temperature and moisture are crucial for the study of biophysical and climate processes. However, on a regional and global scale, the spatial density and distribution of meteorological stations do not allow to meet these needs in spatial information. Satellite imagery represents an interesting source of meteorological data outside meteorological stations with an appreciable spatial extent. The objective of this article is to adapt published estimation methods for air temperature and precipitable water using National Oceanic and Atmospheric Administration (NOAA) advanced very high resolution radiometer (AVHRR) data and to apply them to the southwestern region of the province of Quebec (Canada) context to analyse their performance. Data from 15 AVHRR images and 16 meteorological stations were used for the calibration and the validation of the satellite estimation methods for the first decade of June and July 1997. Precipitable water was thus estimated with an R2 of 0.59 and a standard error of 3.11 mm. As for the air temperature, it was obtained with an R2 of 0.72 and a standard error of 2.1 °C. The sensitivity analysis showed that the precipitable water estimation method is more sensitive to surface temperature fluctuations. The air temperature estimation method is particularly sensitive to the maximum value of the vegetation index.

An Adaptive Model to Monitor Chlorophyll-a in Inland Waters in Southern Quebec Using Downscaled MODIS Imagery.

The purpose of this study is to assess the performance of an adaptive model (AM) in estimating chlorophyll‑a concentration (Chl‑a) in optically complex inland waters. Chl‑a modeling using remote sensing data is usually based on a single model that generally follows an exponential function. The estimates produced by such models are relatively accurate at high Chl‑a concentrations, but accuracy drops at low concentrations. Our objective was to develop an approach combining spectral response classification and three semi-empirical algorithms. The AM discriminates between three blooming classes (waters poorly, moderately, and highly loaded in Chl‑a), with discrimination thresholds set using the classification and regression tree (CART) technique. The calibration of three specific estimators for each class was achieved using a multivariate stepwise regression. Compared to published models (Floating Algae Index, Kahru model, and APProach by ELimination) using the same data set, the AM provided better Chl‑a concentration estimates (R2 of 0.96, relative RMSE of 23%, relative Bias of −2%, and a relative NASH criterion of 0.9). Moreover, the AM achieved an overall success rate of 67% in the estimation of blooming classes (corresponding to low, moderate, and high Chl‑a concentration classes). This was done using an independent data set collected from 22 inland water bodies for the period 2007–2010 and for which the only information available was the blooming class.