Ramata Magagi

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.

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.

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.

Estimating surface soil moisture and soil roughness over semiarid areas from the use of the copolarization ratio

Abstract This paper presents a new method to retrieve soil moisture and roughness from ERS-1. Wind scatterometer (WSC) data measured over the HAPEX-Sahel area (semiarid environment). The retrieval algorithm makes full use of the multiangular acquisitions and the high temporal repetition of the measured backscattering coefficients. The vegetation contribution to the signal is taken into account through a first-order radiative transfer model. The soil moisture and roughness are subsequently retrieved, throughout the rainy season, using the copolarization ratio as expressed by Oh et al. [IEEE Transactions on Geoscience and Remote Sensing GE-30 (1992) 370–381]. The paper describes the data and the approach used, together with the results gained. A good sensitivity of the backscattering coefficient to soil moisture is obtained. The results are compared with data collected during the HAPEX-Sahel campaign.

Optimal conditions for wet snow detection using RADARSAT SAR data

This paper presents simulation results of the backscattering coefficient, in order to discriminate between wet snow and dry snow covers sensed at 5.3 GHz by the RADARSAT Synthetic Aperture Radar (SAR) sensor. Snow-field measurements coinciding with the RADARSAT SAR overpasses are used to explore and set out optimal conditions for wet snow detection, as a function of the sensor incidence angles. The conditions concern wet snow surface characteristics, mainly the roughness represented by the surface slope m and the volumetric liquid water content, snwc (vol.%). Based on the 3-dB threshold value used in several wet snow detection algorithms, the results show that in order to be discriminated from dry snow covers, wet snow surfaces must be characterized as: (a) m≤0.058 and snwc≤1.1, if the sensor operates in the S1 mode (20–27° incidence angle range), and (b) m≤0.082 and snwc≤3.0, if the observations are made in the S7 mode (45–49° incidence angle range). For the identification of a very wet snow, it is also shown that the S7 mode of RADARSAT SAR sensor is more suitable than the S1 mode. The latter, however, provides better discrimination for low values of the snow liquid water content. Furthermore, for wet snow detection based on modeling, the present paper demonstrates the importance of using the appropriate methodology to assess the dielectric constant of the background medium.

Estimation of Latent Heating of Rainfall during the Onset of the Indian Monsoon Using TRMM PR and Radiosonde Data

Abstract The objective of this study is to estimate the vertical structure of the latent heating of precipitation in the vicinity of the Himalayas. Based on a cloud physics parameterization and the thermodynamic equilibrium equation, a simple algorithm is proposed to estimate latent heating from a combination of radiosonde and Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) data, specifically, the radar reflectivity and the rain-rate estimates. An evaluation of the algorithm against 6-hourly areal averages from diagnostic budget studies during the South China Sea Monsoon Experiment (SCMEX) suggests that the algorithm captures well the vertical structure of latent heating between the top of the moist layer and the cloud-top detrainment layer. The retrieval algorithm was applied systematically over the Indian subcontinent and Tibetan plateau within a region comprising 15°–32°N and 70°–95°E during June, the month of monsoon onset, for three different years (1999, 2000, and 2001). The esti...

Quantitative analysis of RADARSAT SAR data over a sparse forest canopy

This article studies the behavior of the backscattering coefficient of a sparse forest canopy composed of relatively short black spruce trees. Qualitative analysis of the multiangular data measured by the RADARSAT synthetic aperture radar (SAR) sensor shows a good agreement with surface and vegetation volume scattering fundamental behaviors. For a quantitative analysis, allometric equations and measurements of tree components collected within the framework of the Extended Collaboration to Link Ecophysiology and Forest Productivity (ECOLEAP) project are used, in an existing multilayer radiative transfer model for forest canopies, to simulate the RADARSAT SAR data. In our approach, the fractional cover of trees estimated from aerial photographs is used as a weighting parameter to adapt the closed-canopy backscattering model to the sparse forest under study. Our objective is to analyze the sensitivity of the backscattering coefficient as a function of sensor configuration, soil wetness, forest cover, and forest structural properties in order to determine the suitable soil, vegetation, and sensor parameters for a given thematic application. For the entire incidence angle domain (20/spl deg/ to 50/spl deg/) of the sensor, simulations show that over a sparse forest composed of mature trees the monitoring of the ground surface is possible only under very wet soil conditions. Therefore, this article informs about the ability of the RADARSAT SAR sensor in monitoring wetlands.

Application of Target Decomposition Theorems Over Snow-Covered Forested Areas

This paper compares two well-known polarimetric decomposition theorems, Cloude-Pottier and Freeman-Durden, applied to L- and C-band Airborne Polarimetric Synthetic Aperture Radar (AIRSAR-POLSAR) data acquired during the Cold-Land Processes Field Experiments. Three field campaigns were carried out in February 2002, March 2002, and March 2003 over a snow-covered open terrain, a sparse coniferous forest, and a dense coniferous forest. The analysis evaluates the ability of the two target decomposition methods for the identification and understanding of the main scattering mechanisms.

Results of combining L- and C-band passive microwave airborne data over the Sahelian area

This study focuses on an area in the Sahelian zone, Niger, Western Africa, where the HAPEX-Sahel experiment took place in 1992. During the hydrologic atmospheric pilot experiment in the Sahel (HAPEX-Sahel), passive microwave data were acquired with airborne radiometer, the multifrequency (5 to 90 GHz) and dual polarization sensor, PORTOS, and the four-beam sensor push broom microwave radiometer (PBMR), operating at 1.4 GHz in H-polarization. The aim of this investigation is to monitor soil moisture and vegetation parameters by combining L-band C-band passive microwave airborne measurements. Through the relationships between soil moisture measurements from the 2 cm and 0.5 cm top layers, soil moisture is estimated for PORTOS data using the estimated soil moisture along the transects covered by the PBMR flights. The simplified radiative transfer model is then used to extract the optical thickness and the single scattering albedo of vegetation at C-band, and to evaluate the vegetation effect on the estimated soil moisture at L-band. An attempt to relate the estimated optical thickness from PORTOS data to the measured vegetation biophysical parameters [water content, biomass, leaf area index (LAI)] is presented.

Evaluation of Simplified Polarimetric Decomposition for Soil Moisture Retrieval over Vegetated Agricultural Fields

This paper investigates a simplified polarimetric decomposition for soil moisture retrieval over agricultural fields. In order to overcome the coherent superposition of the backscattering contributions from vegetation and underlying soils, a simplification of an existing polarimetric decomposition is proposed in this study. It aims to retrieve the soil moisture by using only the surface scattering component, once the volume scattering contribution is removed. Evaluation of the proposed simplified algorithm is performed using extensive ground measurements of soil and vegetation characteristics and the time series of UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar) data collected in the framework of SMAP (Soil Moisture Active Passive) Validation Experiment 2012 (SMAPVEX12). The retrieval process is tested and analyzed in detail for a variety of crops during the phenological stages considered in this study. The results show that the performance of soil moisture retrieval depends on both the crop types and the crop phenological stage. Soybean and pasture fields present the higher inversion rate during the considered phenological stage, while over canola and wheat fields, the soil moisture can be retrieved only partially during the crop developing stage. RMSE of 0.06–0.12 m3/m3 and an inversion rate of 26%–38% are obtained for the soil moisture retrieval based on the simplified polarimetric decomposition.