Yann Kerr

Estimation of heat and momentum fluxes over complex terrain using a large aperture scintillometer.

A comprehensive experimental plan has been designed to further investigate the potential and the limitations associated with the use of a large aperture scintillometer (LAS) to infer path average sensible and momentum fluxes over complex surfaces as part of the Semi-Arid Land-Surface-Atmosphere (SALSA) Program. The complexity of the terrain is associated with the type and the cover of the vegetation canopy as well as with changes in topography. Scintillometer based estimates of sensible heat flux and friction velocity are compared to those measured by eddy correlation systems over a grassland patch, a mesquite patch, and over a transect spanning both patches. The results show that considering the complexity of the surface, the overall performance of the scintillometer is relatively good.

Passive Low Frequency Microwaves: Principles, Radiative Transfer, Physics of Measurements

Abstract: This chapter focuses on passive microwave remote sensing measurements over continental surfaces. In this context, the main applications are monitoring soil moisture and, to a lesser extent, vegetation properties (water content, dynamic seasonal changes, biomass, etc.) from observations that we qualify here as low frequency, meaning

Radio frequency interferences investigation using the airborne L-band full polarimetric radiometer CAROLS

In the present paper, different methods are proposed for the detection and mitigation of the undesirable effects of radio frequency interference (RFI) in microwave radiometry. The first of these makes use of kurtosis to detect the presence of non-Gaussian signals, whereas the second imposes a threshold on the standard deviation of brightness temperatures, in order to distinguish natural emission variations from RFI. Finally, the third approach is based on the use of a threshold applied to the third and fourth Stokes parameters. All of these methods have been applied and tested, with a CAROLS radiometer operating in the L-band, on data acquired during airborne campaigns made in spring 2009 over the South West of France. The performance of each, or of two combined approaches is analyzed with our database. We thus show that the kurtosis method is well adapted to pulsed RFI, whereas the method based on the second moment is well adapted to continuous-wave RFI.

Estimation of Soil Water Conditions Using Passive Microwave Remote Sensing

Abstract: The water content of soil, or soil moisture, is a key element in several fields, such as hydrology, meteorology, agriculture, and forestry. This statement is explained by the determining role of soil moisture in the processes (infiltration, runoff, evaporation, etc.) governing the water cycle and the global energy balance. This role has resulted in the recognition of soil moisture as an essential climate variable by the Global Climate Observing System (GCOS). However, as a result of its dependence on several factors, the spatial and temporal variability of soil moisture is very complex. In addition to precipitation and evapotranspiration, it is linked to surface characteristics such as the type of soil and vegetation, the topography, the surface roughness and so on. In order to analyze the variability of soil moisture, dense network stations for soil moisture observation have been temporarily installed during short field campaigns, among them the Cold Land Processes Field Experiment (CLPX) in northern Colorado, the Soil Moisture Experiment in 2002 (SMEX02) in Iowa, the Canadian Experiment for Soil Moisture in 2010 (CanEx-SM10) and the SMAP Validation Experiment in 2012 (SMAPVEX12) in the Canadian Prairies. For continuous soil moisture data, the installation of dense networks capable of providing historical series of soil moisture is ideal, but expensive. As a result, the International Soil Moisture Network has been developed through international cooperation in order to put together and archive in a database soil moisture measurements collected during field campaigns, and to make this database available to researchers. At the watershed or regional scale, hydrological models are used as an alternative for determining soil moisture. At the global scale, information about soil moisture can be obtained from passive or active microwave satellite measurements. In an effort to help soil moisture users, a database of soil moisture estimated at the global scale from passive and active microwave satellites has been developed as part of the Climate Change Initiative (CCI) program launched in 2010.

Soil Moisture Retrieval Algorithms: The SMOS Case

After the successful acquisition by a coarse L-band radiometer on board Skylab in the early seventies, the potential of L-band radiometry was made clear in spite of a strict limitation linked to minimum antenna dimensions required for appropriate spatial resolution. More than 20 years later new antenna concepts emerged to mitigate this physical constraint. The first to emerge, in 1997, and to become a reality, was the Soil Moisture and Ocean Salinity (SMOS) mission (Kerr, 1997, Kerr, 1998). It is European Space Agency’s (ESA’s) second Earth Explorer Opportunity mission (Kerr et al., 2001), launched in November 2009. It is a joint program between ESA, CNES (Centre National d’Etudes Spatiales), and CDTI (Centro para el Desarrollo Tecnologico Industrial). SMOS carries a single payload, an L-band 2D interferometric radiometer in the 1400–1427 MHz protected band. This wavelength penetrates well through the atmosphere, and hence, the instrument probes the Earth surface emissivity from space. Surface emissivity can be related to the moisture content in the first few centimeters of soil, and after some surface roughness and temperature corrections, to the sea surface salinity over ocean.Soil moisture retrieval from SMOS observations with a required accuracy of 0.04 m3/m3 is challenging and involves many steps. The retrieval algorithms are developed and implemented in the ground segment, which processes level 1 and level 2 data. Level 1 consists mainly of directional brightness temperatures, while level 2 consists of geophysical products in swath mode, i.e., for successive imaging snapshots acquired by the sensor during a half orbit from pole to pole. Level 3 consists in composites of brightness temperatures, or geophysical products over time and space, i.e., global maps over given temporal periods from 1 day to 1 month. In this context, a group of institutes prepared the soil moisture and ocean salinity Algorithm Theoretical Basis Documents (ATBD), used to in operational soil moisture and sea salinity retrieval algorithms (Kerr et al., 2010a).The principle of the level 2 soil moisture retrieval algorithm is based on an iterative approach, which aims at minimizing a cost function. The main component of the cost function is given by the sum of the squared weighted differences between measured and modeled brightness temperature (TB) at horizontal and vertical polarizations, for a variety of incidence angles. The algorithm finds the best set of parameters, e.g., soil moisture (SM) and vegetation characteristics, which drive the TB model and minimizes the cost function. From this algorithm, a more sophisticated one was developed to take into account multiorbit retrievals (i.e., level 3). Subsequently, after several years of data acquisition and algorithm improvements, a neural network approach was developed so as to be able to infer soil moisture fields in near-real time. In parallel, several simplified algorithms were tested, the goal being to achieve a seamless transition with other sensors, along with other studies targeted on specific targets such as dense forests, organic rich soils, or frozen and snow-covered grounds. Finally, it may be noted that most of these approaches deliver not only the surface soil moisture but also other quantities of interest such as vegetation optical depth, surface roughness, and surface dielectric constant. The goal of this article is to give an overview of these different approaches and corresponding results and adequate references for those wishing to go further. Sea surface salinity is not covered in this article, while the focus is SMOS data.