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Remote Sens., Volume 1, Issue 2 (June 2009) – 6 articles , Pages 36-121

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452 KiB

Open AccessArticle

Polarimetric Emission of Rain Events: Simulation and Experimental Results at X-Band

by Nuria Duffo, Mercedes Vall llossera, Adriano Camps, Ignasi Corbella and Francesc Torres

Remote Sens. 2009, 1(2), 107-121; https://doi.org/10.3390/rs1020107 - 12 Jun 2009

Cited by 10 | Viewed by 10623

Abstract

Accurate models are used today for infrared and microwave satellite radiance simulations of the first two Stokes elements in the physical retrieval, data assimilation etc. of surface and atmospheric parameters. Although in the past a number of theoretical and experimental works have studied the polarimetric emission of some natural surfaces, specially the sea surface roughened by the wind (Windsat mission), very limited studies have been conducted on the polarimetric emission of rain cells or other natural surfaces. In this work, the polarimetric emission (four Stokes elements) of a rain cell is computed using the polarimetric radiative transfer equation assuming that raindrops are described by Pruppacher-Pitter shapes and that their size distribution follows the Laws-Parsons law. The Boundary Element Method (BEM) is used to compute the exact bistatic scattering coefficients for each raindrop shape and different canting angles. Numerical results are compared to the Rayleigh or Mie scattering coefficients, and to Oguchi’s ones, showing that above 1-2 mm raindrop size the exact formulation is required to model properly the scattering. Simulation results using BEM are then compared to the experimental data gathered with a X-band polarimetric radiometer. It is found that the depolarization of the radiation caused by the scattering of non-spherical raindrops induces a non-zero third Stokes parameter, and the differential phase of the scattering coefficients induces a non-zero fourth Stokes parameter. Full article

(This article belongs to the Special Issue Microwave Remote Sensing)

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389 KiB

Open AccessArticle

RPV Model Parameters Based on Hyperspectral Bidirectional Reflectance Measurementsof Fagus sylvatica L. Leaves

by Dimitrios Biliouris, Dimitry Van der Zande, Willem W. Verstraeten, Jan Stuckens, Bart Muys, Philip Dutré and Pol Coppin

Remote Sens. 2009, 1(2), 92-106; https://doi.org/10.3390/rs1020092 - 11 Jun 2009

Cited by 9 | Viewed by 13266

Abstract

The bidirectional reflectance parametric and semi-empirical Rahman-Pinty-Verstraete (RPV) model was inverted based on Bidirectional Reflectance Factor (BRF) measurements of 60 Fagus sylvatica L. leaves in the optical domain between 400 nm and 2,500 nm. This was accomplished using data retrieved from the Compact Laboratory Spectro-Goniometer (CLabSpeG) with an azimuth and zenith angular step of 30 and 15 degrees, respectively. Wavelength depended RPV parameters describing the leaf reflectance shape (rho0), the curve convexity (k) and the dominant forward scattering (Θ) were derived using the RPVinversion-2 software (Joint Research Centre) package with Correlation Coefficient values between modelled and measured data varying between 0.71 and 0.99 for all wavelengths, azimuth and zenith positions. The RPV model parameters were compared with a set of leaves not participating in the inversion procedure and presented Correlation Coefficient values ranging between 0.64 and 0.94 suggesting that RPV could be also used for simulating single canopy elements such as leaves. Full article

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637 KiB

Open AccessArticle

Effect of Land Cover Heterogeneity on Soil Moisture Retrieval Using Active Microwave Remote Sensing Data

by Tarendra Lakhankar, Hosni Ghedira, Marouane Temimi, Amir E. Azar and Reza Khanbilvardi

Remote Sens. 2009, 1(2), 80-91; https://doi.org/10.3390/rs1020080 - 13 May 2009

Cited by 31 | Viewed by 12746

Abstract

This study addresses the issue of the variability and heterogeneity problems that are expected from a sensor with a larger footprint having homogenous and heterogeneous sub-pixels. Improved understanding of spatial variability of soil surface characteristics such as land cover and vegetation in larger footprint are critical in remote sensing based soil moisture retrieval. This study analyzes the sub-pixel variability (standard deviation of sub-grid pixels) of Normalized Difference Vegetation Index and SAR backscatter. Back-propagation neural network was used for soil moisture retrieval from active microwave remote sensing data from Southern Great Plains of Oklahoma. The effect of land cover heterogeneity (number of different vegetation species within pixels) on soil moisture retrieval using active microwave remote sensing data was investigated. The presence of heterogeneous vegetation cover reduced the accuracy of the derived soil moisture using microwave remote sensing data. The results from this study can be used to characterize the uncertainty in soil moisture retrieval in the context of Soil Moisture Active and Passive (SMAP) mission which will have larger footprint. Full article

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782 KiB

Open AccessArticle

Use of Macro Fibre Composite Transducers as Acoustic Emission Sensors

by Mark Eaton, Rhys Pullin, Karen Holford, Sam Evans, Carol Featherston and Alex Rose

Remote Sens. 2009, 1(2), 68-79; https://doi.org/10.3390/rs1020068 - 24 Apr 2009

Cited by 24 | Viewed by 13186

Abstract

The need for ever lighter and more efficient aerospace structures and components has led to continuous optimization pushing the limits of structural performance. In order to ensure continued safe operation during long term service it is desirable to develop a structural health monitoring (SHM) system. Acoustic emission (AE) offers great potential for real time global monitoring of aerospace structures, however currently available commercial sensors have limitations in size, weight and adaptability to complex structures. This work investigates the potential use of macro-fibre composite (MFC) film transducers as AE sensors. Due to the inhomogeneous make-up of MFC transducers their directional dependency was examined and found to have limited effect on signal feature data. However, signal cross-correlations revealed a strong directional dependency. The sensitivity and signal attenuation with distance of MFC sensors were compared with those of commercially available sensors. Although noticeably less sensitive than the commercial sensors, the MFC sensors still had an acceptable operating range. Furthermore, a series of compressive carbon fiber coupon tests were monitored in parallel using both an MFC sensor and a commercially available sensor for comparison. The results showed good agreement of AE trends recorded by both sensors. Full article

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933 KiB

Open AccessArticle

Irrigated Area Maps and Statistics of India Using Remote Sensing and National Statistics

by Prasad S. Thenkabail, Venkateswarlu Dheeravath, Chandrashekhar M. Biradar, Obi Reddy P. Gangalakunta, Praveen Noojipady, Chandrakantha Gurappa, Manohar Velpuri, Muralikrishna Gumma and Yuanjie Li

Remote Sens. 2009, 1(2), 50-67; https://doi.org/10.3390/rs1020050 - 17 Apr 2009

Cited by 65 | Viewed by 28917

Abstract

The goal of this research was to compare the remote-sensing derived irrigated areas with census-derived statistics reported in the national system. India, which has nearly 30% of global annualized irrigated areas (AIAs), and is the leading irrigated area country in the World, along with China, was chosen for the study. Irrigated areas were derived for nominal year 2000 using time-series remote sensing at two spatial resolutions: (a) 10-km Advanced Very High Resolution Radiometer (AVHRR) and (b) 500-m Moderate Resolution Imaging Spectroradiometer (MODIS). These areas were compared with the Indian National Statistical Data on irrigated areas reported by the: (a) Directorate of Economics and Statistics (DES) of the Ministry of Agriculture (MOA), and (b) Ministry of Water Resources (MoWR). A state-by-state comparison of remote sensing derived irrigated areas when compared with MoWR derived irrigation potential utilized (IPU), an equivalent of AIA, provided a high degree of correlation with R² values of: (a) 0.79 with 10-km, and (b) 0.85 with MODIS 500-m. However, the remote sensing derived irrigated area estimates for India were consistently higher than the irrigated areas reported by the national statistics. The remote sensing derived total area available for irrigation (TAAI), which does not consider intensity of irrigation, was 101 million hectares (Mha) using 10-km and 113 Mha using 500-m. The AIAs, which considers intensity of irrigation, was 132 Mha using 10-km and 146 Mha using 500-m. In contrast the IPU, an equivalent of AIAs, as reported by MoWR was 83 Mha. There are “large variations” in irrigated area statistics reported, even between two ministries (e.g., Directorate of Statistics of Ministry of Agriculture and Ministry of Water Resources) of the same national system. The causes include: (a) reluctance on part of the states to furnish irrigated area data in view of their vested interests in sharing of water, and (b) reporting of large volumes of data with inadequate statistical analysis. Overall, the factors that influenced uncertainty in irrigated areas in remote sensing and national statistics were: (a) inadequate accounting of irrigated areas, especially minor irrigation from groundwater, in the national statistics, (b) definition issues involved in mapping using remote sensing as well as national statistics, (c) difficulties in arriving at precise estimates of irrigated area fractions (IAFs) using remote sensing, and (d) imagery resolution in remote sensing. The study clearly established the existing uncertainties in irrigated area estimates and indicates that both remote sensing and national statistical approaches require further refinement. The need for accurate estimates of irrigated areas are crucial for water use assessments and food security studies and requires high emphasis. Full article

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1032 KiB

Open AccessArticle

Effects of Percent Tree Canopy Density and DEM Misregistration on SRTM/NED Vegetation Height Estimates

by George Miliaresis and Demitris Delikaraoglou

Remote Sens. 2009, 1(2), 36-49; https://doi.org/10.3390/rs1020036 - 2 Apr 2009

Cited by 20 | Viewed by 12023

Abstract

The U.S National Elevation Dataset and the NLCD 2001 landcover data were used to test the correlation between SRTM elevation values and the height of evergreen forest vegetation in the Klamath Mountains of California.Vegetation height estimates (SRTM-NED) are valid only for the two out of eight (N, NE, E, SE, S, SW, W, NW) geographic directions, due to NED and SRTM grid data misregistration. Penetration depths of SRTM radar were found to linearly correlate to tree percent canopy density. Full article

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