Initiated in 1984, the Committee Earth Observing Satellites' Working Group on Calibration and Validation (CEOS WGCV) pursues activities to coordinate, standardize and advance calibration and validation of civilian satellites and their data. One subgroup of CEOS WGCV, Land Product Validation (LPV), was established in 2000 to define standard validation guidelines and protocols and to foster data and information exchange relevant to the validation of land products. Since then, a number of leaf area index (LAI) products have become available to the science community at both global and regional extents. Having multiple global LAI products and multiple, disparate validation activities related to these products presents the opportunity to realize efficiency through international collaboration. So the LPV subgroup established an international LAI intercomparison validation activity. This paper describes the main components of this international validation effort. The paper documents the current participants, their ground LAI measurements and scaling techniques, and the metadata and infrastructure established to share data. The paper concludes by describing plans for sharing both field data and high-resolution LAI products from each site. Many considerations of this global LAI intercomparison can apply to other products, and this paper presents a framework for such collaboration

The Satellite Application Facility on Climate Monitoring (CM-SAF) aims at the provision of satellite-derived geophysical parameter data sets suitable for climate monitoring. CM-SAF provides climatologies for Essential Climate Variables (ECV), as required by the Global Climate Observing System implementation plan in support of the UNFCCC. Several cloud parameters, surface albedo, radiation fluxes at the top of the atmosphere and at the surface as well as atmospheric temperature and humidity products form a sound basis for climate monitoring of the atmosphere. The products are categorized in monitoring data sets obtained in near real time and data sets based on carefully intercalibrated radiances. The CM-SAF products are derived from several instruments on-board operational satellites in geostationary and polar orbit as the Meteosat and NOAA satellites, respectively. The existing data sets will be continued using data from the instruments on-board the new joint NOAA/EUMETSAT Meteorological Operational Polar satellite. The products have mostly been validated against several ground-based data sets both in situ and remotely sensed. The accomplished accuracy for products derived in near real time is sufficient to monitor variability on diurnal and seasonal scales. The demands on accuracy increase the longer the considered time scale is. Thus, interannual variability or trends can only be assessed if the sensor data are corrected for jumps created by instrument changes on successive satellites and more subtle effects like instrument and orbit drift and also changes to the spectral response function of an instrument. Thus, a central goal of the recently started Continuous Development and Operations Phase of the CM-SAF (2007–2012) is to further improve all CM-SAF data products to a quality level that allows for studies of interannual variability.

The surface roughness parameters commonly used as inputs to electromagnetic surface scattering models (SPM, PO, GO, and IEM) are the root mean square (RMS) height s, and autocorrelation length l. However, soil moisture retrieval studies based on these models have yielded inconsistent results, not so much because of the failure of the models themselves, but because of the complexity of natural surfaces and the difficulty in estimating appropriate input roughness parameters. In this paper, the authors address the issue of soil roughness characterization in the case of agricultural fields having different tillage (roughness) states by making use of an extensive multisite database of surface profiles collected using a novel laser profiler capable of recording profiles up to 25 m long. Using this dataset, the range of RMS height and correlation values associated with each agricultural roughness state is estimated, and the dependence of these estimates on profile length is investigated. The results show that at spatial scales equivalent to those of the SAR resolution cell, agricultural surface roughness characteristics are well described by the superposition of a single scale process related to the tillage state with a multiscale random fractal process related to field topography.

Estimation of leaf area index (LAI) using spectral vegetation indices (SVIs) was studied based on data from 683 plots on two Scots pine and Norway spruce dominated sites in Finland. The SVIs studied included the normalised difference vegetation index (NDVI), the simple ratio (SR), and the reduced simple ratio (RSR), and were calculated from Landsat ETM images of the two sites. Regular grids of size 1 km2 with gridpoints placed at 50 m intervals were established at the sites and measurements of LAI using the LAI-2000 instrument were taken at the gridpoints. SVI-LAI relationships were examined at plot scale, where the plots were defined as circular areas of radius 70 m around each gridpoint. Plotwise mean LAI was computed as a weighted average of LAI readings taken around the gridpoints belonging to the plot. Mean LAI for the plots ranged from 0.36 to 3.72 (hemisurface area). All of the studied SVIs showed fair positive correlation with LAI but RSR responded more dynamically to LAI than did SR or NDVI. Especially NDVI showed poor sensitivity to changes in LAI. RSR explained 63% of the variation in LAI when all plots were included (n = 683) and the coefficient of determination rose to 75% when data was restricted to homogeneous plots (n = 381). Maps of estimated LAI using RSR showed good agreement with maps of measured LAI for the two sites.

We present a novel 28 yr dataset of Earth's black-sky surface albedo, derived from AVHRR instruments. The dataset is created using algorithms to separately derive the surface albedo for different land use areas globally. Snow, sea ice, open water and vegetation are all treated independently. The product features corrections for the atmospheric effect in satellite-observed surface radiances, a BRDF correction for the anisotropic reflectance properties of natural surfaces, and a novel topography correction of geolocation and radiometric accuracy of surface reflectance observations over mountainous areas. The dataset is based on a homogenized AVHRR radiance timeseries. The product is validated against quality-controlled in situ observations of clear-sky surface albedo at various BSRN sites around the world. Snow and ice albedo retrieval validation is given particular attention using BSRN sites over Antarctica, Greenland Climate Network stations on the Greenland Ice Sheet (GrIS), as well as sea ice albedo data from the SHEBA and Tara expeditions. The product quality is found to be comparable to other previous long-term surface albedo datasets from AVHRR.