Kalifa Goita

Algorithm development for the estimation of snow water equivalent in the boreal forest using passive microwave data

This paper presents the results of recent algorithm development to estimate snow water equivalent (SWE) in the boreal forest landscape. Airborne microwave data and ground-based measurements of snow cover were acquired during the Boreal Ecosystem Atmosphere Study (BOREAS) winter field campaign held in February 1994. Land cover information for the flight lines was derived from BOREAS Landsat Thematic Mapper (Landsat TM) and National Oceanographic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA AVHRR) classification maps. Analysis of the experimental datasets shows that the microwave vertically polarized difference index (MPDI) for 18 and 37 GHz can be used to estimate SWE in forested environments. Two linear algorithms were developed for deciduous and coniferous forest types respectively.

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.

Snow water equivalent retrieval in a Canadian boreal environment from microwave measurements using the HUT snow emission model

Snow water equivalent (SWE) is a critical parameter for climatological and hydrological studies over northern high-latitude areas. In this paper, we study the usability of the Helsinki University of Technology (HUT) snow emission model for the estimation of SWE in a Canadian boreal forest environment. The experimental data (airborne passive microwave and ground-based data) were acquired during the Boreal Ecosystem-Atmosphere Study winter field campaign held in February 1994 in Central Canada. Using the experimental dataset, surface brightness temperatures at 18 and 37 GHz (vertical polarization) were simulated with the HUT snow emission model and compared to those acquired by the airborne sensors. The results showed an important underestimation at 37 GHz (-27 K) and an overestimation at 18 GHz (10 K). In this paper, we demonstrate that the errors in the model simulations are due mainly to the extinction coefficient modeling, which is a function of snow grain size. Therefore, we propose a new semiempirical function for the extinction coefficient, based on an empirical correction to the Rayleigh scattering expression. Results presented in this paper show that the proposed function improves the HUT model accuracy to predict brightness temperature in the experimental context considered, with a mean error of /spl plusmn/5 K and /spl plusmn/9 K, respectively, at 18 and 37 GHz, and a negligible bias (less than 4 K) in both cases. These errors are comparable in magnitude to the accuracy of the radiometers used during the airborne flights. SWE was retrieved using the modified HUT snow emission model based on an iterative inversion technique. SWE was estimated with a mean error of /spl plusmn/10 mm and a negligible bias. Only a rough knowledge of mean snow grain size /spl phi/~ was required in the inversion procedure. The effects of possible errors on mean snow grain size /spl phi/~ are presented and discussed.

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.

Rule-Based Classification of a Very High Resolution Image in an Urban Environment Using Multispectral Segmentation Guided by Cartographic Data

Classification algorithms based on single-pixel analysis often do not give the desired result when applied to high-spatial-resolution remote-sensing data. In such cases, classification algorithms based on object-oriented image segmentation are needed. There are many segmentation algorithms in the literature, but few have been applied in urban studies to classify a high-spatial-resolution remote-sensing image. Furthermore, the user must specify the spectral and spatial parameters that are data dependent. In this paper, we propose an automatic multispectral segmentation algorithm inspired by the specific idea of guiding a classification process for a high-spatial-resolution remote-sensing image of an urban area using an existing digital map of the same area. The classification results could be used, for example, for high-scale database updating or change-detection studies. The algorithm developed uses digital maps and spectral data as inputs. It generates the segmentation parameters automatically. The algorithm is able to provide a segmented image with accuracy greater than 90%. The segmentation results are then used in a rule-based classification using spectral, geometric, textural, and contextual information. The classification accuracy of the proposed rule-based classification is at least 17% greater than the maximum-likelihood classification results. Results and future improvements will be discussed.

Coupling the snow thermodynamic model SNOWPACK with the microwave emission model of layered snowpacks for subarctic and arctic snow water equivalent retrievals

[1] Satellite-passive microwave remote sensing has been extensively used to estimate snow water equivalent (SWE) in northern regions. Although passive microwave sensors operate independent of solar illumination and the lower frequencies are independent of atmospheric conditions, the coarse spatial resolution introduces uncertainties to SWE retrievals due to the surface heterogeneity within individual pixels. In this article, we investigate the coupling of a thermodynamic multilayered snow model with a passive microwave emission model. Results show that the snow model itself provides poor SWE simulations when compared to field measurements from two major field campaigns. Coupling the snow and microwave emission models with successive iterations to correct the influence of snow grain size and density significantly improves SWE simulations. This method was further validated using an additional independent data set, which also showed significant improvement using the two-step iteration method compared to standalone simulations with the snow model.

Validation of SMOS Data Over Agricultural and Boreal Forest Areas in Canada

This study was conducted as part of the Soil Moisture and Ocean Salinity (SMOS) calibration and validation activities over agricultural and boreal forest sites located in Saskatchewan, Canada. For each site covering 33 km × 71 km (i.e., about two SMOS pixels), we examined the SMOS brightness temperature (L1c) and soil moisture (L2) products from May 1 to September 30, 2010. The consistency of these data with respect to theory and to the temporal variation of surface characteristics was first discussed at both sites. Then, the SMOS L1c (prototype 346) and L2 (prototypes 305-309) products were evaluated using the Canadian Experiment for Soil Moisture in 2010 (CanEx-SM10) ground measurements and L-band passive microwave airborne measurements, in addition to AMSR-E soil moisture estimates and simulations from the zeroth order τ- ω radiative transfer model. For both study sites, the model underestimated SMOS brightness temperatures in V polarization, whereas an overestimation was observed in H polarization. The data sets showed that both the SMOS and AMSR-E soil moisture values were underestimated compared with ground measurements collected during CanEx-SM10 but less so for the AMSR-E estimates. The SMOS soil moisture product was underestimated with a RMSE varying from 0.15 to 0.18 m3/ m3. Furthermore, the overall results showed that errors in the soil moisture estimates increased with the absolute value of soil moisture.

Improved Corrections of Forest Effects on Passive Microwave Satellite Remote Sensing of Snow Over Boreal and Subarctic Regions

Microwave radiometry has been extensively used in order to estimate snow water equivalent in northern regions. However, for boreal and taiga environments, the presence of forest causes important uncertainties in the estimates. Variations in snow cover and vegetation in northeastern Canada (north of the Québec province) were characterized in a transect from 50°N to 60 °N during the International Polar Year field campaign of February 2008. Forest properties show a strong latitudinal gradient in fraction and stem volume. A large database (>; 2000 points with a stem volume ranging between 0 and 700 m3 ·ha-1) showed that brightness temperatures (Tb) decrease as forest cover fraction decreases until a cover fraction of about 25% is reached. Furthermore, Tb values saturate at high stem volume, particularly at 37 GHz. We defined new relationships for the forest transmissivity as a function of stem volume and depending on the frequency/polarization. The proposed relationships give asymptotic transmissivity saturation levels of 0.51, 0.55, 0.53, and 0.53 for 19 GHz [vertical (V) polarization], 19 GHz [horizontal (H) polarization], 37 GHz (V polarization), and 37 GHz (H polarization), respectively. These relationships were used to estimate snow Tb from the Advanced Microwave Scanning Radiometer-Earth Observing System brightness temperatures at 18.7 and 36.5 GHz, and results show an estimated snow brightness temperature well correlated to the airborne snow brightness temperatures over vegetation-free areas.

A Case Study of Using a Multilayered Thermodynamical Snow Model for Radiance Assimilation

A microwave radiance assimilation (RA) scheme for the retrieval of snow physical state variables requires a snowpack physical model (SM) coupled to a radiative transfer model. In order to assimilate microwave brightness temperatures (Tbs) at horizontal polarization (h-pol), an SM capable of resolving melt-refreeze crusts is required. To date, it has not been shown whether an RA scheme is tractable with the large number of state variables present in such an SM or whether melt-refreeze crust densities can be estimated. In this paper, an RA scheme is presented using the CROCUS SM which is capable of resolving melt-refreeze crusts. We assimilated both vertical (v) and horizontal (h) Tbs at 18.7 and 36.5 GHz. We found that assimilating Tb at both h-pol and vertical polarization (v-pol) into CROCUS dramatically improved snow depth estimates, with a bias of 1.4 cm compared to -7.3 cm reported by previous studies. Assimilation of both h-pol and v-pol led to more accurate results than assimilation of v-pol alone. The snow water equivalent (SWE) bias of the RA scheme was 0.4 cm, while the bias of the SWE estimated by an empirical retrieval algorithm was -2.9 cm. Characterization of melt-refreeze crusts via an RA scheme is demonstrated here for the first time; the RA scheme correctly identified the location of melt-refreeze crusts observed in situ.

Boreal forest transmissivity in the microwave domain using ground-based measurements

This letter proposes an estimation of microwave transmissivity within the Canadian boreal forest. The aim is to correct the forest effect in snow water equivalent estimation from Special Sensor Microwave Imager and Advanced Microwave Scanning Radiometer microwave measurements. The estimation was carried out using ground-based radiometric measurements, at 19 and 37 GHz, and for both polarizations. The results show that the transmissivity is correlated with the stem volume and is independent of the tree species. For high stem volumes (>100 m/sup 3//ha), the transmissivity is found to be 0.4 and 0.3 for 19 and 37 GHz, respectively.