Terhikki Manninen

On the characterization of agricultural soil roughness for radar remote sensing studies

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.

Leaf area index estimation of boreal forest using ENVISAT ASAR

A method for retrieval of leaf area index (LAI) using ENVISAT Advanced Synthetic Aperture Radar vertical/horizontal (VV/HH) polarization ratio was derived for boreal forests. Five alternating polarization single-look complex images of the test site were acquired in summer 2003. The swath range from IS1-IS6 was studied. The VV/HH polarization ratio correlated quite well with the ground truth LAI values. The mean error of the LAI estimates was 0.27 for the test site with mixed forest when data from all images and stands were used without separating between species (or swaths). The respective mean LAI estimation error was 0.3 for Norway spruce (Picea abies (L.) Karst.) and 0.07 for Scots pine (Pinus sylvestris L.) dominated stands.

Leaf Area Index (LAI) Estimation of Boreal Forest Using Wide Optics Airborne Winter Photos

A new simple airborne method based on wide optics camera is developed for leaf area index (LAI) estimation in coniferous forests. The measurements are carried out in winter, when the forest floor is completely snow covered and thus acts as a light background for the hemispherical analysis of the images. The photos are taken automatically and stored on a laptop during the flights. The R2 value of the linear regression of the airborne and ground based LAI measurements was 0.89.

Validation of MODIS and GEOV1 fPAR Products in a Boreal Forest Site in Finland

Remote sensing of the fraction of absorbed Photosynthetically Active Radiation (fPAR) has become a timely option to monitor forest productivity. However, only a few studies have had ground reference fPAR datasets containing both forest canopy and understory fPAR from boreal forests for the validation of satellite products. The aim of this paper was to assess the performance of two currently available satellite-based fPAR products: MODIS fPAR (MOD15A2, C5) and GEOV1 fPAR (g2_BIOPAR_FAPAR), as well as an NDVI-fPAR relationship applied to the MODIS surface reflectance product and a Landsat 8 image, in a boreal forest site in Finland. Our study area covered 16 km2 and field data were collected from 307 forest plots. For all plots, we obtained both forest canopy fPAR and understory fPAR. The ground reference total fPAR agreed better with GEOV1 fPAR than with MODIS fPAR, which showed much more temporal variation during the peak-season than GEOV1 fPAR. At the chosen intercomparison date in peak growing season, MODIS NDVI based fPAR estimates were similar to GEOV1 fPAR, and produced on average 0.01 fPAR units smaller fPAR estimates than ground reference total fPAR. MODIS fPAR and Landsat 8 NDVI based fPAR estimates were similar to forest canopy fPAR.

Land Surface Albedos Computed from BRF Measurements with a Study of Conversion Formulae

Land surface hemispherical albedos of several targets have been resolved using the bidirectional reflectance factor (BRF) library of the Finnish Geodetic Institute (FGI). The library contains BRF data measured by FGI during the years 2003–2009. Surface albedos are calculated using selected BRF datasets from the library. Polynomial interpolation and extrapolation have been used in computations. Several broadband conversion formulae generally used for satellite based surface albedo retrieval have been tested. The albedos were typically found to monotonically increase with increasing zenith angle of the Sun. The surface albedo variance was significant even within each target category / surface type. In general, the albedo estimates derived using diverse broadband conversion formulas and estimates obtained by direct integration of the measured spectra were in line.

Leaf Area Index Estimation of Boreal and Subarctic Forests Using VV/HH ENVISAT/ASAR Data of Various Swaths

This paper demonstrates the potential of dual polarization synthetic aperture radar (SAR) images in the estimation of the leaf area index (LAI) of boreal forests. The SAR data do not suffer from the low sun elevation and frequent cloud cover, which often complicate the use of optical wavelengths for LAI retrieval in the area. The analysis was based on a large number of environmental satellite (ENVISAT) advanced synthetic aperture radar (ASAR) alternating polarization vertical polarization (VV)/horizontal polarization (HH) single-look-complex images covering several test sites, boreal and subarctic, during summers 2003-2006. The swath range from IS1 to IS7 was studied. In all test sites, a linear regression with the VV/HH backscattering ratio as the independent variable could typically be used for the estimation of LAI. All swaths could be used for the estimation, but larger incidence angles generally performed better. The best results were obtained for swath IS6. The swaths could be used also together, but better results were obtained using the diverse swaths individually. The LAI estimation error decreased essentially exponentially with the number of pixels averaged to give one backscattering value. The LAI estimation accuracy for a set of average quality ASAR images of swath IS6 reached 0.1 when the averaging number of pixels was 33 150, which would correspond to an area of about 2.2 km2 for images with no overlap. The spatial LAI estimation error decreased with the number of images covering the same area.

SNORTEX (Snow Reflectance Transition Experiment): Remote sensing measurement of the dynamic properties of the boreal snow-forest in support to climate and weather forecast: Report of IOP-2008

Large discrepancies are observed between snow albedo in Numerical Weather Prediction (NWP) models and from satellite observations in the case of high vegetation. Knowledge of the Bidirectional Reflectance Distribution Function (BRDF) of snow-forest system is required to solve the problem. The 3-years SNORTEX (Snow Reflectance Transition Experiment) campaign acquires from 2008 in situ measurements of snow and forest properties in support to the development of modelling tools and to validate coarse resolution satellite products (POLDER, MODIS, MERIS, METOP). The measurement scheme and some first example results are presented from the Intensive Observing Period (IOP) of 2008, which can be decomposed into airborne and ground operations. Multi-temporal BRDF at a metric resolution were acquired from OSIRIS (airPOLDER) onboard a helicopter and from ground with FigiFiGo spectrogoniometer. The same helicopter embarked a pair of UV sensors, pyranometers and a wide-optics camera. Ground component includes exhaustive snow measurements.

Land applications of ERS-1 Wind Scatterometer in boreal forest zone

The feasibility of the ERS-1 Wind Scatterometer (WS) for land applications in the boreal forest zone was analyzed using data (1) from test areas located in Finland and (2) covering the European boreal forest zone. The results show that the ERS-1 Wind Scatterometer has potential for some applications: the monitoring of soil frost and the retrieval of soil moisture/vegetation water content. The major problem in the employment of the ERS-1 scatterometer for land applications is its poor spatial resolution (47 km). This paper will also address the problem of improving the spatial resolution of the ERS-1 scatterometer data. It is shown that a realistic figure for the improved resolution is 25 km.

Automatic Segment-Level Tree Species Recognition Using High Resolution Aerial Winter Imagery

Abstract Our objective was to automatically recognize the species composition of a boreal forest from high-resolution airborne winter imagery. The forest floor was covered by snow so that the contrast between the crowns and the background was maximized. The images were taken from a helicopter flying at low altitude so that fine details of the canopy structure could be distinguished. Segments created by an object-oriented image processing were used as a basis for a linear discriminant analysis, which aimed at separating the three dominant tree species occurring in the area: Scots pine, Norway spruce, and downy birch. In a cross validation, the classification showed an overall accuracy of 81.9%, and a kappa coefficient of 0.73.

Measuring the roughness characteristics of natural surfaces at pixel scales: moving from 1 metre to 25 metre profiles

This paper describes the motivation behind and the characteristics of a laser profiler which has been specifically tailored to the measurement of surface roughness characteristics of importance in the radar electromagnetic scattering studies-and hence inversion and retrieval algorithms. The main feature of this profiler is its capability of recording roughness profiles at the scale of the spaceborne SAR resolution, which is roughly of the order of 25 meters for current radar satellites such as ERS-2 or RADARSAT.