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A Comparison of algorithms for Retrieving Soil Moisture from ENVISAT/ASAR Images

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2008年第10期46卷 3274-3284页

作者： Paloscia, Simonetta Pampaloni, Paolo Pettinato, Simone Santi, Emanuele CNR Ist Fis Applicata Nello Carrara IFAC I-50019 Florence Italy

In this paper, we present an intercomparison of algorithms for retrieving soil moisture content (SMC) from ENVIronmental SATtellite (ENVISAT)/Advanced Synthetic Aperture Radar images. The algorithms taken into consideration were a feedforward artificial neural network (ANN) with two hidden layers, a statistical approach based on Bayes' theorem, and an iterative algorithm based on the Nelder-Mead direct-search method. The comparison was carried out by using both simulated and experimental data. Simulated data were obtained by means of the Integral Equation Model (IEM). Experimental data were collected in an agricultural area in Northern Italy during 2003-2005;they included backscattering coefficient at HH and HV polarizations and at an incidence angle of theta = 23 degrees, as well as detailed ground truth measurements of SMC, surface roughness, and vegetation parameters. HH-polarized data were related to SMC, whereas the information of the cross-polarized channel was used to correct the backscatter for the effects of surface roughness. A comparison of the algorithms with experimental data showed that all the tested approaches produced SMC values that are very close to the measured ones. However, the predictions of the ANN were slightly more suitable than the other methods for generating maps in reasonable time. The production of moisture maps carried out at different dates using this algorithm pointed out the feasibility of separating up to six levels of spatial/temporal variations of SMC in the range of 10%-35%.

关键词： Artificial neural network (ANN) backscattering coefficient ENVIronmental SATtellite (ENVISAT)/Advanced Synthetic Aperture Radar (ASAR) inversion algorithms soil moisture content (SMC) maps

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A comparative study of processing simulated and experimental data in elastic laser light scattering

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MATHEMATICAL BIOSCIENCES 1999年第1-2期157卷 321-344页

作者： Popovici, MA Mincu, N Popovici, A Politehn Univ Bucharest Dept Phys Bucharest 77206 Romania Natl Inst Res & Dev Optoelect Bucharest 76900 Romania Romanian Acad Ctr Math Stat Bucharest 76100 Romania

The intensity of the laser light scattered by a suspension of biological particles undergoing Brownian motion contains information about their size distribution function and optical properties. We used several methods (implemented in MathCAD programs), including a new one, to invert the Fredholm integral equation of the first kind, which represents the angular dependence of the elastic scattering of light. The algorithms were first tested on different sets of simulated data. Experimental data were obtained using biological samples and an experimental arrangement which are briefly described. We study the stability of the inversion procedures relative to the noise levels, and compute the first two moments of the retrieved size distribution function. A comparison of the results corresponding to simulated and experimental data is done, to select the best processing algorithm. (C) 1999 Elsevier Science Inc. All rights reserved.

关键词： elastic light scattering biological particles particle size distribution function inversion algorithms

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Inferring vegetation water content from C- and L-band SAR images

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2007年第10期45卷 3165-3171页

作者： Notarnicola, Claudia Posa, Francesco Policlin Bari Dipartimento Interateneo Fis I-70126 Bari Italy

This paper addresses the capability of synthetic aperture radar and optical images in combination with theoretical models to detect the vegetation water content (VWC) at field level. In this paper, a retrieval algorithm for the estimation of VWC from AirSAR acquired on vegetated fields during the SMEX'02 experiment is addressed. The aforementioned campaign has been chosen because, along with sensor observations, extensive ground truth measurements were acquired. The retrieval procedure, which is based on a Bayesian approach, has been initially developed for soil moisture extraction. It consists of two modules: one is pertinent to bare soils and the other one has been modified for vegetated fields. The last one uses the synergy with optical images to correct for the contribution of VWC. The VWC, a variable in the inversion procedure, as well as soil moisture can be estimated. The results indicate a good correlation with both ground measurements and VWC calculated from Landsat images through the use of normalized difference water index (NDWI). Furthermore, in the inversion procedure, the introduction of the dependence on roughness improves the estimates. This indicates that, even for dense vegetation, the contribution from bare soil greatly influences the radar signal. Three main levels of VWC are discriminated in the inversion procedure: values below 1 kg/m(2), values between 1 and 3 kg/m(2), and values greater than 3 kg/m(2).

关键词： inversion algorithms optical images radar images roughness vegetation

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THE CASE FOR A GLOBAL INLAND WATER QUALITY PRODUCT

THE CASE FOR A GLOBAL INLAND WATER QUALITY PRODUCT

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Malthus, Tim J. Hestir, Erin L. Dekker, Arnold G. Brando, Vittorio E. CSIRO Environm Earth Observat Program Div Land & Water Canberra ACT Australia

ISBN: (纸本)9781467311595

This paper argues for the development of a quantitative global inland water quality product based on satellite optical remote sensing. Water quality is a critical component of global fresh water security and ecosystem health, yet is often overlooked when global analyses of water security are undertaken. In the face of declining surface measurements and datasets across the globe, alternatives to conventional water quality measurement are required. The case for an optical remote sensing based inland water quality product is a strong one. Global products of ocean color and their dissemination infrastructure are operational and widely used, providing a framework for global inland products. Furthermore, coastal and inland water quality algorithms based on spectral inversion algorithms are now sufficiently mature to cope with the greater variability of inherent optical properties in these systems. While these algorithms provide the greatest promise for reliable, robust and simultaneous retrieval of several water quality variables across sensors, limited knowledge of the bio-optical properties of inland waters and limited validation currently prevent global implementation. Internationally coordinated efforts are required to accumulate representative bio-optical data to improve our understanding of the optical complexity and variability of inland waters.

关键词： Inland water quality optical remote sensing inversion algorithms global datasets

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Development and assessment of lidar modeling to retrieve IOPs 10

Development and assessment of lidar modeling to retrieve IOP...

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Conference on Ocean Sensing and Monitoring X

作者： Strait, Christopher Twardowski, Michael Dalgleish, Fraser Tonizzo, Alberto Vuorenkoski, Anni Florida Atlantic Univ Harbor Branch Oceanog Inst 5600 US 1 North Ft Pierce FL 34946 USA

ISBN: (数字)9781510617742

ISBN: (纸本)9781510617742

A synthetic bio-optical dataset of inherent optical properties (IOPs) was created based on Chlorophyll concentrations ranging between 0.01 and 30 mg m(-3). Dissolved and particulate fractions of absorption were varied to account for the natural ranges in values. The IOPs will then be used as inputs to a time-resolved Monte-Carlo radiative transfer model to generate accurate lidar backscatter time history wave forms. Test experiments were performed to validate the model, where the primary lidar geometry in the model matched an existing system developed at HBOI under NOAA-OAR funding. The system uses blue and green pulsed laser sources (473 and 532 nm, respectively) and has two telescopes arranged at a 10 degrees offset (on and off axis) from one another. The field of view of the telescopes is set at 1 degrees. Approaches are being investigated to invert simulated and measured lidar results to derive input water column IOP properties. Results are tested through application to lidar measurements collected in an experimental tank with known suspended particle type and concentration.

关键词： Ocean sensing and monitoring underwater imaging remote sensing inversion algorithms

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Inferring vegetation water content from C- and L-band SAR images

Inferring vegetation water content from C- and L-band SAR im...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Notarnicola, Claudia Posa, Francesco Policlin Bari Dipartimento Interateneo Fis I-70126 Bari Italy

关键词： inversion algorithms optical images radar images roughness vegetation

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Some results of the MIRAS-SMOS demonstrator campaigns

Some results of the MIRAS-SMOS demonstrator campaigns

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Duffo, N. Torres, F. Corbella, I. Gonzalez, V. Blanch, S. S. Camps, A. Vall-Hossera, M. Alvarez, J. L. Ribo, S. Martin-Neira, M. Univ Politecn Cataluna Dept Signal Theory & Commun C Jordi Girona 1-3 ES-08034 Barcelona Spain CSIC IEEC Inst Ciencia Espai Bellaterra Spain ESA European Space Res & Technol Ctr Estec Netherlands

ISBN: (纸本)9781424412112

In this paper, some results of the MIRAS-SMOS demonstrator campaigns are presented. The follow two campaigns were held: an image validation campaign at IRTA side and a flight demonstrator campaign at Finland. The ultimate objective of the airborne demonstrator is to demonstrate its imaging capabilities by applying improved calibration and inversion algorithms to natural low-contrast targets.

关键词： SMOS MIRAS calibration inversion algorithms

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COSMO-SkyMed and RADARSAT2 image investigation for the monitoring of agricultural areas 15

COSMO-SkyMed and RADARSAT2 image investigation for the monit...

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Conference on SAR Image Analysis, Modeling, and Techniques XV

作者： Paloscia, S. Pettinato, S. Santi, E. Notarnicola, C. Greifeneder, F. Cuozzo, G. Nicolini, I. Demir, B. Bruzzone, L. CNR IFAC I-50019 Florence Italy EURAC Bolzano Italy Univ Trento Trento Trento Italy

ISBN: (纸本)9781628418521

This research aims at investigating the backscatter sensitivity at C and X band to the characteristics of agricultural surfaces and analyzing the integration of these data collected from Radarsat2 (RS2) and COSMO-SkyMed (CSK) systems on tree agricultural test areas in Italy (San Pietro Capofiume, in Emilia Romagna, Sesto Fiorentino, in Tuscany, and Mazia Valley, in South Tyrol). A preliminary test of the sensitivity of SAR signal to the soil and vegetation characteristics was first carried out by also comparing data from previous experiments. From these results, it can be concluded that X-band data are mainly sensitive to vegetation structure and biomass, and to soil moisture of bare or slightly vegetate soils, whereas C-band images could provide valuable information for the retrieval of soil moisture, even in vegetation covered soils. Two retrieval algorithms were implemented for estimating the main geophysical parameters, namely soil moisture content (SMC) and vegetation biomass (PWC) from these sensors. Over Sesto Fiorentino area, an algorithm based on Artificial Neural Network (ANN) technique was implemented for estimating both SMC of bare or scarcely vegetated soil and vegetation biomass of wheat crops at X band. On the South-Tyrol area, a SMC retrieval approach based on the Support Vector Regression methodology, which was already tested in this area using C-band data from ENVISAT/ASAR data, was adopted. This algorithm integrated data at both X and C bands showing encouraging results, even though further investigations shall be carried out on a larger time-series and larger set of samples.

关键词： X-band backscatter C-band backscatter agricultural vegetation soil moisture vegetation biomass inversion algorithms Cosmo-SkyMed (CSK) RadarSAT2 (RS2)

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Soil Moisture mapping using Sentinel-1 images: the proposed approach and its preliminary validation carried out in view of an operational product

Soil Moisture mapping using Sentinel-1 images: the proposed ...

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Conference on SAR Image Analysis, Modeling, and Techniques XI

作者： Paloscia, S. Pettinato, S. Santi, E. Pierdicca, N. Pulvirenti, L. Notarnicola, C. Pace, G. Reppucci, A. Ist Fis Applicata Nello Carrara Pisa Italy Univ Roma La Sapienza Rome Italy EURAC Res Bolzano Italy Adv Comp Syst SpA Rome Italy Starlab Barcelona Spain

ISBN: (纸本)9780819488060

The main objective of this research is to develop, test and validate a soil moisture (SMC)) algorithm for the GMES Sentinel-1 characteristics, within the framework of an ESA project. The SMC product, to be generated from Sentinel-1 data, requires an algorithm able to process operationally in near-real-time and deliver the product to the GMES services within 3 hours from observations. Two different complementary approaches have been proposed: an Artificial Neural Network (ANN), which represented the best compromise between retrieval accuracy and processing time, thus allowing compliance with the timeliness requirements and a Bayesian Multi-temporal approach, allowing an increase of the retrieval accuracy, especially in case where little ancillary data are available, at the cost of computational efficiency, taking advantage of the frequent revisit time achieved by Sentinel-1. The algorithm was validated in several test areas in Italy, US and Australia, and finally in Spain with a 'blind' validation. The Multi-temporal Bayesian algorithm was validated in Central Italy. The validation results are in all cases very much in line with the requirements. However, the blind validation results were penalized by the availability of only VV polarization SAR images and MODIS low-resolution NDVI, although the RMS is slightly > 4%.

关键词： Sentinel-1 Soil moisture inversion algorithms Artificial Neural Network Bayes Multi-temporal approach

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Data reduction methods for space lidar observation of clouds and aerosols

Data reduction methods for space lidar observation of clouds...

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Conference on Lidar Remote Sensing for Industry and Environment Monitoring

作者： Liu, ZY Voelger, P Sugimoto, N Natl Inst Environm Studies Tsukuba Ibaraki 3050053 Japan

ISBN: (纸本)0819438065

We studied data reduction algorithms for the retrievals of optical properties of cloud and aerosols for a two-wavelength (1053 nm and 527 nm) spaceborne lidar which has been developed in the frame of the Japanese spaceborne lidar project. We simulated cloud and aerosol observations with this lidar for prelaunch characterizations and investigation of the retrieval of cloud and aerosol optical properties. In the simulations, first, lidar return signals were generated for a two-dimensional artificial model atmosphere taking into account all possible noise sources as detector, background radiation, etc. and simulated realistically both the digitized data sampling process and the saturation behavior of the photon counting system. Then, the developed data reduction algorithms were tested with these simulated two-wavelength lidar signals to retrieve cloud and aerosol optical scattering parameters. The results showed that this lidar will provide not only qualitative information of cloud and aerosol distributions but also allows for the quantitative retrieval of optical properties of some aerosol layers and optically thin clouds. Recently, we applied the data reduction methods to the analysis of the data observed by the Lidar In-space Technology Experiment (LITE) in September 1994. The applicability of these methods to retrievals of optical properties of aerosols and optically thin clouds such as cirrus have been demonstrated. Moreover, comparisons of the simulated results for ELISE with the LITE observation were conducted to verify the developed simulation method. In this paper after a brief review of the data reduction algorithms, we present a three-component solution of the lidar equation for data analysis of high level thin clouds with volcanic aerosols and apply it to the LITE data of orbit 125.

关键词： space lidar lidar data reduction inversion algorithms cloud and aerosol observations

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