@article{Renzullo2008,
        title = {Multi-sensor model-data fusion for estimation of hydrologic and energy flux parameters},
        author = {Luigi J. Renzullo and Damian J. Barrett and Alan S. Marks and Michael J. Hill and Juan P. Guerschman and Qiaozhen Mu and Steve W. Running},
        booktitle = {Remote Sensing Data Assimilation Special Issue},
        journal = {Remote Sensing of Environment},
        month = {apr},
        number = 4,
        pages = {1306--1319},
        volume = 112,
        year = 2008,
        url = {http://www.sciencedirect.com/science/article/B6V6V-4PK8MPH-3/2/f495f28883b0df1eb96683f5e4659117},
        description = {ScienceDirect - Remote Sensing of Environment : Multi-sensor model-data fusion for estimation of hydrologic and energy flux parameters},
	abstract = {Model-data fusion offers considerable promise in remote sensing for improved state and parameter estimation particularly when applied to multi-sensor image products. This paper demonstrates the application of a [`]multiple constraints' model-data fusion (MCMDF) scheme to integrating AMSR-E soil moisture content (SMC) and MODIS land surface temperature (LST) data products with a coupled biophysical model of surface moisture and energy budgets for savannas of northern Australia. The focus in this paper is on the methods, difficulties and error sources encountered in developing an MCMDF scheme and enhancements for future schemes. An important aspect of the MCMDF approach emphasized here is the identification of inconsistencies between model and data, and among data sets. The MCMDF scheme was able to identify that an inconsistency existed between AMSR-E SMC and LST data when combined with the coupled SEB-MRT model. For the example presented, an optimal fit to both remote sensing data sets together resulted in an 84% increase in predicted SMC and 0.06% increase for LST relative to the fit to each data set separately. That is the model predicted on average cooler LST's (~�1.7�K) and wetter SMC values (~�0.04�g cm-�3) than the satellite image products. In this instance we found that the AMSR-E SMC data on their own were poor constraints on the model. Incorporating LST data via the MCMDF scheme ameliorated deficiencies in the SMC data and resulted in enhanced characterization of the land surface soil moisture and energy balance based on comparison with the MODIS evapotranspiration (ET) product of Mu et al. [Mu, Q., Heinsch, F.A, Zhao, M. and Running, S.W. (in press), Development of a global evapotranspiration algorithm based on MODIS and global meteorology data, Remote Sensing of Environment.]. Canopy conductance, gC, and latent heat flux, [lambda]E, from the MODIS ET product were in good agreement with RMSEs for gC�=�0.5�mm s-�1 and for [lambda]E�=�18�W m-�2, respectively. Differences were attributable to a greater canopy-to-air vapor pressure gradient in the MCMDF approach obtained from a more realistic partitioning of soil surface and canopy temperatures.},
	biburl = {http://www.bibsonomy.org/bibtex/224ce585d1661d595d179d92a670b243b/jgomezdans},
	keywords = {satellite inference moisture Bayes soilmoisture bayes mcmc vegetation evapotranspiration statistics drought Soil assimilation models evaporation remotesensing uncertainty}
}

@article{Gabban.2008,
        title = {A comparative analysis of the use of NOAA-AVHRR NDVI and FWI data for forest fire risk assessment},
        address = {Taylor & Francis},
        author = {A. Gabban and J. San-Miguel-Ayanz and D. X. Viegas},
        journal = {International Journal of Remote Sensing},
        pages = {5677 - 5687},
        series = 19,
        volume = 29,
        year = 2008,
        url = {http://www.informaworld.com/10.1080/01431160801958397},
        issn = {0143-1161},
	biburl = {http://www.bibsonomy.org/bibtex/20118a574a0c0a005f015f8e8f2625bee/jgomezdans},
	keywords = {risk fires satellite firemafs fire}
}

@inproceedings{Viovy2001,
        title = {Assimilation of remote sensing measurements into the ORCHIDEE/STOMATE DGVM biosphere model},
        author = {VIOVY N. and FRANCOIS C. and BONDEAU A. and KRINNER G. and POLCHER J. and KERGOAT L. and DEDIEU G. and DE NOBLET N. and CIAIS P. and FRIEDLINGSTEIN P.},
        booktitle = {Physical measurements & signatures in remote sensing. International symposium},
        month = {jan},
        number = 8,
        pages = {713-718},
        year = 2001,
        url = {http://dods.ipsl.jussieu.fr/orchidee/WEBORCHIDEE/aussois5.pdf},
        abstract = {An Ensemble method is presented for assimilating AVHRR/NDVI data to correct the Leaf Area Index (LAI) simulated by the dynamic biosphere model ORCHIDEE/STOMATE. The corrected LAIs over Europe are shown, as well as the consequences on the Net Primary Production.},
	biburl = {http://www.bibsonomy.org/bibtex/2495e61c76d4b710a092e1483bb715e63/jgomezdans},
	keywords = {models assimilation satellite lai vegetation uncertainty statistics}
}

@article{bgd-5-3533-2008,
        title = {Estimates of fire emissions from an active deforestation region in the southern Amazon based on satellite data and biogeochemical modelling},
        author = {G. R. van der Werf and D. C. Morton and R. S. DeFries and L. Giglio and J. T. Randerson and G. J. Collatz and P. S. Kasibhatla},
        journal = {Biogeosciences Discussions},
        number = 4,
        pages = {3533--3573},
        volume = 5,
        year = 2008,
        url = {http://www.biogeosciences-discuss.net/5/3533/2008/},
        issn = {1810-6277},
	biburl = {http://www.bibsonomy.org/bibtex/2c4b4fbbfb1aba23cb01dafc82f676035/jgomezdans},
	keywords = {models fires wildfire satellite amazonia remotesensing vegetation firemafs uncertainty rainforest fire}
}

@article{Dorigo2006,
        title = {A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling},
        author = {W.A. Dorigo and R. Zurita-Milla and A.J.W. de Wit and J. Brazile and R. Singh and M.E. Schaepman},
        booktitle = {Advances in airborne electromagnetics and remote sensing of agro-ecosystems},
        journal = {International Journal of Applied Earth Observation and Geoinformation},
        month = {may},
        number = 2,
        pages = {165--193},
        volume = 9,
        year = 2007,
        url = {http://www.sciencedirect.com/science/article/B6X2F-4KCXJND-1/2/6b5e172d43f455de979fa8ee7a7ff295},
        description = {ScienceDirect - International Journal of Applied Earth Observation and Geoinformation : A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling},
	abstract = {During the last 50 years, the management of agroecosystems has been undergoing major changes to meet the growing demand for food, timber, fibre and fuel. As a result of this intensified use, the ecological status of many agroecosystems has been severely deteriorated. Modeling the behavior of agroecosystems is, therefore, of great help since it allows the definition of management strategies that maximize (crop) production while minimizing the environmental impacts. Remote sensing can support such modeling by offering information on the spatial and temporal variation of important canopy state variables which would be very difficult to obtain otherwise. In this paper, we present an overview of different methods that can be used to derive biophysical and biochemical canopy state variables from optical remote sensing data in the VNIR-SWIR regions. The overview is based on an extensive literature review where both statistical-empirical and physically based methods are discussed. Subsequently, the prevailing techniques of assimilating remote sensing data into agroecosystem models are outlined. The increasing complexity of data assimilation methods and of models describing agroecosystem functioning has significantly increased computational demands. For this reason, we include a short section on the potential of parallel processing to deal with the complex and computationally intensive algorithms described in the preceding sections. The studied literature reveals that many valuable techniques have been developed both for the retrieval of canopy state variables from reflective remote sensing data as for assimilating the retrieved variables in agroecosystem models. However, for agroecosystem modeling and remote sensing data assimilation to be commonly employed on a global operational basis, emphasis will have to be put on bridging the mismatch between data availability and accuracy on one hand, and model and user requirements on the other. This could be achieved by integrating imagery with different spatial, temporal, spectral, and angular resolutions, and the fusion of optical data with data of different origin, such as LIDAR and radar/microwave.},
	biburl = {http://www.bibsonomy.org/bibtex/24f1cc351db72d1528ff8d32fbb34baa7/jgomezdans},
	keywords = {models assimilation satellite kalmanfilter Crop agronomic yield agriculture remotesensing crops vegetation uncertainty}
}

@article{sudmed2008,
        title = {An integrated modelling and remote sensing approach for hydrological study in arid and semi-arid regions: the SUDMED Programme},
        address = {Taylor & Francis},
        author = {A. Chehbouni and R. Escadafal and B. Duchemin and G. Boulet and V. Simonneaux and G. Dedieu and B. Mougenot and S. Khabba and H. Kharrou and P. Maisongrande and O. Merlin and A. Chaponnière and J. Ezzahar and S. Er-Raki and J. Hoedjes and R. Hadria and A. Abourida and A. Cheggour and F. Raibi and A. Boudhar and I. Benhadj and L. Hanich and A. Benkaddour and N. Guemouria and A. H. Chehbouni and A. Lahrouni and A. Olioso and F. Jacob and D. G. Williams and J. A. Sobrino},
        journal = {International Journal of Remote Sensing},
        pages = {5161 - 5181},
        series = 17,
        volume = 29,
        year = 2008,
        url = {http://www.informaworld.com/10.1080/01431160802036417},
        issn = {0143-1161},
	biburl = {http://www.bibsonomy.org/bibtex/20571808f5994a30b380de8e01ffe73e7/jgomezdans},
	keywords = {models satellite evaporation moisture remotesensing vegetation evapotranspiration drought}
}

@article{DeAlwis2007,
        title = {Unsupervised classification of saturated areas using a time series of remotely sensed images},
        journal = {Hydrology and Earth System Sciences},
        pages = {1663-1696},
        volume = 4,
        year = 2007,
        url = {http://www.hydrol-earth-syst-sci-discuss.net/4/1663/2007/},
        abstract = {The spatial distribution of saturated areas is an important consideration in numerous applications, such as water resource planning or sighting of management practices. However, in humid well vegetated climates where runoff is produced by saturation excess processes on hydrologically active areas (HAA) the delineation of these areas can be difficult and time consuming. Much of the non-point source pollution in these watersheds originates from these HAAs. Thus, a technique that can simply and reliably predict these areas would be a powerful tool for scientists and watershed managers tasked with implementing practices to improve water quality. Remotely sensed data is a source of spatial information and could be used to identify HAAs, should a proper technique be developed. The objective of this study is to develop a methodology to determine the spatial variability of saturated areas using a temporal sequence of remotely sensed images. The Normalized Difference Water Index (NDWI) was derived from medium resolution LANDSAT 7 ETM+ imagery collected over seven months in the Town Brook watershed in the Catskill Mountains of New York State and used to characterize the areas that were susceptible to saturation. We found that within a single landcover type, saturated areas were characterized by the soil surface water content when the vegetation was dormant and leaf water content of vegetation during the growing season. The resulting HAA map agreed well with both observed and spatially distributed computer simulated saturated areas. This methodology appears promising for delineating saturated areas in the landscape.
},
	biburl = {http://www.bibsonomy.org/bibtex/25e698d7a754cf6e475ad088214c251ee/jgomezdans},
	keywords = {satellite moisture classification remotesensing vegetation reflectance drought}
}

@article{Jackson2004,
        title = {Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans},
        author = {Thomas J. Jackson and Daoyi Chen and Michael Cosh and Fuqin Li and Martha Anderson and Charles Walthall and Paul Doriaswamy and E. Ray Hunt},
        booktitle = {2002 Soil Moisture Experiment (SMEX02)},
        journal = {Remote Sensing of Environment},
        month = {Sep},
        number = 4,
        pages = {475--482},
        volume = 92,
        year = 2004,
        url = {http://www.sciencedirect.com/science/article/B6V6V-4B8BWHY-1/1/bcff206f3a4c9678088761e8bc7d7e98},
        description = {ScienceDirect - Remote Sensing of Environment : Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans},
	abstract = {Information about vegetation water content (VWC) has widespread utility in agriculture, forestry, and hydrology. It is also useful in retrieving soil moisture from microwave remote sensing observations. Providing a VWC estimate allows us to control a degree of freedom in the soil moisture retrieval process. However, these must be available in a timely fashion in order to be of value to routine applications, especially soil moisture retrieval. As part of the Soil Moisture Experiments 2002 (SMEX02), the potential of using satellite spectral reflectance measurements to map and monitor VWC for corn and soybean canopies was evaluated. Landsat Thematic Mapper and Enhanced Thematic Mapper Plus data and ground-based VWC measurements were used to establish relationships based on remotely sensed indices. The two indices studied were the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI). The NDVI saturated during the study period while the NDWI continued to reflect changes in VWC. NDWI was found to be superior based upon a quantitative analysis of bias and standard error. The method developed was used to map daily VWC for the watershed over the 1-month experiment period. It was also extended to a larger regional domain. In order to develop more robust and operational methods, we need to look at how we can utilize the MODIS instruments on the Terra and Aqua platforms, which can provide daily temporal coverage.},
	biburl = {http://www.bibsonomy.org/bibtex/29ff1efcaa6d2e7d83c5fcb36f7cf5f39/jgomezdans},
	keywords = {Soil satellite evaporation moisture stress ndwi remotesensing soilmoisture vegetation reflectance drought}
}

@article{Dente2008,
        title = {Assimilation of leaf area index derived from ASAR and MERIS data into CERES-Wheat model to map wheat yield},
        author = {Laura Dente and Giuseppe Satalino and Francesco Mattia and Michele Rinaldi},
        booktitle = {Remote Sensing Data Assimilation Special Issue},
        journal = {Remote Sensing of Environment},
        month = {apr},
        number = 4,
        pages = {1395--1407},
        volume = 112,
        year = 2008,
        url = {http://www.sciencedirect.com/science/article/B6V6V-4PPFTF3-2/1/d3b3163d37d96e353f028e2605370379},
        description = {ScienceDirect - Remote Sensing of Environment : Assimilation of leaf area index derived from ASAR and MERIS data into CERES-Wheat model to map wheat yield},
	abstract = {This study presents a method to assimilate leaf area index retrieved from ENVISAT ASAR and MERIS data into CERES-Wheat crop growth model with the objective to improve the accuracy of the wheat yield predictions at catchment scale. The assimilation method consists in re-initialising the model with optimal input parameters allowing a better temporal agreement between the LAI simulated by the model and the LAI estimated by remote sensing data. A variational assimilation algorithm has been applied to minimise the difference between simulated and remotely-sensed LAI and to determine the optimal set of input parameters. After the re-initialisation, the wheat yield maps have been obtained and their accuracy evaluated. The method has been applied over Matera site located in Southern Italy and validated by using the dataset of an experimental campaign carried out during the 2004 wheat growing season. Results indicate that, LAI maps retrieved from MERIS and ASAR data can be effectively assimilated into CERES-Wheat model thus leading to accuracies of the yield maps ranging from 360�kg/ha to 420�kg/ha.},
	biburl = {http://www.bibsonomy.org/bibtex/294d5116363dc948666ef0f63f36cf2db/jgomezdans},
	keywords = {satellite Crop microwave retrieval vegetation models Data SAR maps MERIS crops ASAR LAI Wheat growth}
}

@article{LobellOrtizMonasterio,
        title = {Yield uncertainty at the field scale evaluated with multi-year satellite data},
        author = {David B. Lobell and J. Ivan Ortiz-Monasterio and Walter P. Falcon},
        journal = {Agricultural Systems},
        month = {jan},
        number = {1-3},
        pages = {76--90},
        volume = 92,
        year = 2007,
        url = {http://www.sciencedirect.com/science/article/B6T3W-4JRKD5T-1/1/220ea15932d7d8f536bb13dab8507379},
        description = {ScienceDirect - Agricultural Systems : Yield uncertainty at the field scale evaluated with multi-year satellite data},
	abstract = {The level of yield risk faced by a farmer is an important factor in the design of appropriate management and insurance strategies. The difference between field scale and regional scale yield risk, which can be significant, also represents an important measure of the factors that cause the yield gap - the difference between average and maximum yields. While field scale yield risk is difficult to assess with traditional data sources, yield maps derived from remote sensing offer promise for obtaining the necessary data in any region. We analyzed remotely sensed yield datasets for two regions in Northwest Mexico, the Yaqui and San Luis Rio Colorado Valleys, in conjunction with time series of aggregated regional yields for 1976-2002. Regional scale yield risk was roughly 8% of average yields in both regions. Field scale yield risk was determined to be 58% higher than regional scale risk in both regions. The difference between field and regional scale risk accounted for 50% of the spatial variance in yields in the Yaqui Valley, and 70% in the San Luis Rio Colorado Valley, indicating that climatic uncertainty represents an important source of the spatial yield variability. This implies that accurate seasonal climate forecasts could substantially reduce yield losses in farmers' fields. The results were shown to be fairly sensitive to assumptions about the magnitude and nature of errors in yield estimation, suggesting that improved understanding of estimation errors are needed to realize the full potential of remote sensing for yield risk analysis.},
	biburl = {http://www.bibsonomy.org/bibtex/22c053f8b367e816fa79f54802c14e77b/jgomezdans},
	keywords = {optical models satellite yield modeling remotesensing crops vegetation uncertainty}
}

@article{Davenport2003,
        title = {The use of earth observation techniques to improve catchment-scale pollution predictions},
        author = {I. J. Davenport and M. Silgram and J. S. Robinson and A. Lamb and J. J. Settle and A. Willig},
        booktitle = {Recent Development in River Basin Research and Management},
        journal = {Physics and Chemistry of the Earth, Parts A/B/C},
        number = {33-36},
        pages = {1365--1376},
        volume = 28,
        year = 2003,
        url = {http://www.sciencedirect.com/science/article/B6X1W-49XPFYX-1/1/2ff9292b097359bfd238a7ed8a943a96},
        description = {ScienceDirect - Physics and Chemistry of the Earth, Parts A/B/C : The use of earth observation techniques to improve catchment-scale pollution predictions},
	abstract = {Remote sensing can potentially provide information useful in improving pollution transport modelling in agricultural catchments. Realisation of this potential will depend on the availability of the raw data, development of information extraction techniques, and the impact of the assimilation of the derived information into models. High spatial resolution hyperspectral imagery of a farm near Hereford, UK is analysed. A technique is described to automatically identify the soil and vegetation endmembers within a field, enabling vegetation fractional cover estimation. The aerially-acquired laser altimetry is used to produce digital elevation models of the site. At the subfield scale the hypothesis that higher resolution topography will make a substantial difference to contaminant transport is tested using the AGricultural Non-Point Source (AGNPS) model. Slope aspect and direction information are extracted from the topography at different resolutions to study the effects on soil erosion, deposition, runoff and nutrient losses. Field-scale models are often used to model drainage water, nitrate and runoff/sediment loss, but the demanding input data requirements make scaling up to catchment level difficult. By determining the input range of spatial variables gathered from EO data, and comparing the response of models to the range of variation measured, the critical model inputs can be identified. Response surfaces to variation in these inputs constrain uncertainty in model predictions and are presented. Although optical earth observation analysis can provide fractional vegetation cover, cloud cover and semi-random weather patterns can hinder data acquisition in Northern Europe. A Spring and Autumn cloud cover analysis is carried out over seven UK sites close to agricultural districts, using historic satellite image metadata, climate modelling and historic ground weather observations. Results are assessed in terms of probability of acquisition probability and implications for future earth observation missions.},
	biburl = {http://www.bibsonomy.org/bibtex/289a4c57c980c4aac90a3c99208a5f041/jgomezdans},
	keywords = {optical models assimilation satellite erostion remotesensing pollution crops vegetation reflectance nitrogen}
}

@article{Bach:2003,
        title = {Methods and examples for remote sensing data assimilation in land surface process modeling},
        author = {H. Bach and W. Mauser},
        journal = {IEEE Transactions on Geoscience and Remote Sensing},
        pages = {1629- 1637},
        volume = 41,
        year = 2003,
        url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1221824},
        issn = {0196-2892},
	doi = {10.1109/TGRS.2003.813270},
	description = {Welcome to IEEE Xplore 2.0: Methods and examples for remote sensing data assimilation in land surface process modeling},
	abstract = {Land surface process models describe the energy, water, carbon, and nutrient fluxes on a local to regional scale using a set of environmental land surface parameters and variables. They need time series of spatially distributed inputs to account for the large spatial and temporal variability of land surface processes. In principle many of these inputs can be derived through remote sensing using both optical and microwave sensors. New approaches in four-dimensional data-assimilation (4DDA) form the basis to combine remote sensing data and spatially explicit land surface process models more effectively. This paper describes basic techniques for 4DDA in land surface process modeling. Two case studies were carried out to demonstrate different successful approaches of remote sensing data assimilation into land surface process models. The assimilation of surface soil moisture estimates from European Remote Sensing (ERS) synthetic aperture radar data in a flood forecasting scheme is presented, as well as the combination of a land surface process model and a radiative transfer model to improve the accuracy of land surface parameter retrieval from optical data [Landsat Thematic Mapper (TM)].},
	biburl = {http://www.bibsonomy.org/bibtex/251b9d391f143522e70101f62e7c61f0f/jgomezdans},
	keywords = {optical satellite model BRDF vegetation sensing assimilation models canopy crops remotesensing reflectance uncertainty}
}

@article{pinter_review,
        title = {Remote Sensing for Crop Management},
        author = {Paul J. Pinter Jr. and Jerry L. Hatfield and James S. Schepers and Edward M. Barnes and M. Susan Moran and Craig S.T. Daughtry and Dan R. Upchurch},
        journal = {Photogrammetric Engineering and Remote Sensing},
        month = {June},
        number = 6,
        pages = {647-664},
        volume = 69,
        year = 2003,
        url = {http://www.asprs.org/publications/pers/2003journal/june/abstracts.html#647},
        abstract = {Scientists with the Agricultural Research Service (ARS) and various government agencies and private institutions have provided a great deal of fundamental information relating spectral reflectance and thermal emittance properties of soils and crops to their agronomic and biophysical characteristics. This knowledge has facilitated the development and use of various remote sensing methods for non-destructive monitoring of plant growth and development and for the detection of many environmental stresses which limit plant productivity. Coupled with rapid advances in computing and positionlocating technologies, remote sensing from ground, air, and space-based platforms is now capable of providing detailed spatial and temporal information on plant response to their local environment that is needed for site specific agricultural management approaches. This manuscript, which emphasizes contributions by ARS researchers, reviews the biophysical basis of remote sensing; examines approaches that have been developed, refined, and tested for management of water, nutrients, and pests in agricultural crops; and assesses the role of remote sensing in yield prediction. It concludes with a discussion of challenges facing remote sensing in the future.},
	biburl = {http://www.bibsonomy.org/bibtex/244578cbf68f7162d659c5c1d6be15a45/jgomezdans},
	keywords = {optical satellite yield remotesensing crops vegetation}
}

@article{Ferrazzoli:1997,
        title = {The potential of multifrequency polarimetric SAR in assessingagricultural and arboreous biomass},
        author = {P. Ferrazzoli and S. Paloscia and P. Pampaloni and G. Schiavon and S. Sigismondi and D. Solimini},
        journal = {IEEE Transactions on Geoscience and Remote Sensing},
        pages = {5-17},
        volume = 35,
        year = 1997,
        url = {http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=551929},
        issn = {0196-2892},
	doi = {10.1109/36.551929},
	description = {IEEEXplore# The potential of multifrequency polarimetric SAR in assessing agricultural and arboreous biomass},
	abstract = {Polarimetric radar data collected by AIRSAR and SIR-C over
agricultural fields, forests, and olive groves of the Italian
Montespertoli site are analyzed. The objective is to investigate the
radar capability in discriminating among various vegetation species and
its sensitivity to agricultural and arboreous biomass. Results indicate
that a combined use of P(0.45 GHz) and L- (1.2 GHz) bands allows one to
discriminate between agricultural fields and other targets, while a
combined use of L- and C- (5.3 GHz) bands allows the authors to
discriminate within agricultural areas. To monitor biomass, P-band gives
the best results for forests and olive groves, L-band appears to be good
for crops with low plant density (m-2), while for crops with
high plant density, both L- and C-bands are useful. The availability of
crosspolarized data is important for both classification and biomass
retrieval},
	biburl = {http://www.bibsonomy.org/bibtex/27ec9969c1e92be1d2b9f830c12420b72/jgomezdans},
	keywords = {satellite biomass SAR remotesensing microwave crops vegetation}
}

@article{Ferrazzoli:1999,
        title = {Experimental and model investigation on radar classificationcapability},
        author = {P. Ferrazzoli and L. Guerriero and G. Schiavon},
        journal = {IEEE Transactions on Geoscience and Remote Sensing,},
        pages = {960-968},
        volume = 37,
        year = 1999,
        url = {http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=752214},
        issn = {0196-2892},
	doi = {10.1109/36.752214},
	description = {IEEEXplore# Experimental and model investigation on radar classification capability},
	abstract = {The capability of multifrequency polarimetric synthetic aperture
radar (SAR) to discriminate among nine vegetation classes is shown using
both experimental data and model simulations. The experimental data were
collected by the multifrequency polarimetric AIRSAR at the Dutch
Flevoland site and the Italian Montespertoli site. Simulations are
carried out using an electromagnetic model, developed at Tor Vergata
University, Rome, Italy, which computes microwave vegetation scattering.
The classes have been defined on the basis of geometrical differences
among vegetation species, leading to different polarimetric signatures.
It is demonstrated that, for each class, there are some combinations of
frequencies and polarizations producing a significant separability. On
the basis of this background, a simple, hierarchical parallelepiped
algorithm is proposed},
	biburl = {http://www.bibsonomy.org/bibtex/29de04e14a059b9256b07ca62b96fe15b/jgomezdans},
	keywords = {satellite SAR classification remotesensing microwave crops}
}

@article{1361777,
        title = {Multispectral remotely sensed data in modelling the annual variability of nitrate concentrations in the leachate},
        address = {Amsterdam, The Netherlands, The Netherlands},
        author = {Carsten Montzka and Morton Canty and Peter Kreins and Ralf Kunkel and Gunter Menz and Harry Vereecken and Frank Wendland},
        journal = {Environ. Model. Softw.},
        number = 8,
        pages = {1070--1081},
        publisher = {Elsevier Science Publishers B. V.},
        volume = 23,
        year = 2008,
        url = {http://portal.acm.org/citation.cfm?id=1361777&jmp=cit&coll=GUIDE&dl=GUIDE},
        issn = {1364-8152},
	doi = {http://dx.doi.org/10.1016/j.envsoft.2007.11.010},
	description = {Multispectral remotely sensed data in modelling the annual variability of nitrate concentrations in the leachate},
	abstract = {The advantages of using multispectral remotely sensed data instead of CORINE Land Cover for the modelling of nitrate concentrations in the leachate of the Rur catchment are presented and discussed in this paper. In this context it has been shown that the identification of main crops and annual crop rotation in the Rur catchment by SPOT, LANDSAT and ASTER imagery provides the key for a spatial and thematic enhancement of the model results. The spatial resolution of the nitrogen surplus data set which denotes the linkage between RAUMIS and GROWA is enhanced from district level to field/pixel level. In parallel, the empirical water balance model GROWA is enhanced to differentiate between agricultural crops in the real evapotranspiration calculation. It is calibrated by runoff data measured at gauging stations. Results indicate, e.g., an average nitrate concentration in the leachate of 42mg NO"3/L in the relatively wet year of 2002 and almost 62mg NO"3/L in the dry year of 2003. There is a 20mg NO"3/L weather-induced difference which can be modelled in a more detailed way using self-processed remotely sensed data. The model results were compared to nitrate concentrations observed in the top parts of multi-level wells. In this way the related coefficient of determination has been improved from a value (R) of -0.50 using CORINE to 0.59 by using self-processed remotely sensed data, thus demonstrating the potential of the enhanced model system.},
	biburl = {http://www.bibsonomy.org/bibtex/2de346d1ffe5825c9aec4d9362d78ebe7/jgomezdans},
	keywords = {satellite remotesensing crops uncertainty imported nitrogen}
}

@article{DoraiswamyMODIS2004,
        title = {Crop condition and yield simulations using Landsat and MODIS},
        author = {P. C. Doraiswamy and J. L. Hatfield and T. J. Jackson and B. Akhmedov and J. Prueger and A. Stern},
        booktitle = {2002 Soil Moisture Experiment (SMEX02)},
        journal = {Remote Sensing of Environment},
        month = {#sep#},
        number = 4,
        pages = {548--559},
        volume = 92,
        year = 2004,
        url = {http://www.sciencedirect.com/science/article/B6V6V-4D3B3J8-1/1/787fd89cbcf6981aac93c352693d3145},
        description = {ScienceDirect - Remote Sensing of Environment : Crop condition and yield simulations using Landsat and MODIS},
	abstract = {Monitoring crop condition and yields at regional scales using imagery from operational satellites remains a challenge because of the problem in scaling local yield simulations to the regional scales. NOAA AVHRR satellite imagery has been traditionally used to monitor vegetation changes that are used indirectly to assess crop condition and yields. Additionally, the 1-km spatial resolution of NOAA AVHRR is not adequate for monitoring crops at the field level. Imagery from the new MODIS sensor onboard the NASA Terra satellite offers an excellent opportunity for daily coverage at 250-m resolution, which is adequate to monitor field sizes are larger than 25 ha. A field study was conducted in the predominantly corn and soybean area of Iowa to evaluate the applicability of the 8-day MODIS composite imagery in operational assessment of crop condition and yields. Ground-based canopy reflectance and leaf area index (LAI) measurements were used to calibrate the models. The MODIS data was used in a radiative transfer model to estimate LAI through the season. LAI was integrated into a climate-based crop simulation model to scale from local simulation of crop development and responses to a regional scale. Simulations of corn and soybean yields at a 1.6�1.6-km2 grid scale were comparable to county yields reported by the USDA-National Agricultural Statistics Service (NASS). Weekly changes in soil moisture for the top 1-m profile were also simulated as part of the crop model as one of the critical parameters influencing crop condition and yields.},
	biburl = {http://www.bibsonomy.org/bibtex/2f77b75e3906f4a4113f3b01b94ddc479/jgomezdans},
	keywords = {optical satellite Crop application moisture mapping vegetation modis Soil assimilation models crops remotesensing reflectance uncertainty MODIS}
}

@article{BachVerhoef_assim,
        title = {Remote sensing data assimilation using coupled radiative transfer models},
        author = {Wout Verhoef and Heike Bach},
        booktitle = {Applications of Quantitative Remote Sensing to Hydrology},
        journal = {Physics and Chemistry of the Earth, Parts A/B/C},
        number = {1-3},
        pages = {3--13},
        volume = 28,
        year = 2003,
        url = {http://www.sciencedirect.com/science/article/B6X1W-483SMNY-2/1/5f4b4b961c42f18766c513d42b64aca9},
        description = {ScienceDirect - Physics and Chemistry of the Earth, Parts A/B/C : Remote sensing data assimilation using coupled radiative transfer models},
	abstract = {This paper discusses data assimilation of biophysical parameters retrieved from optical remote sensing images in land surface process models by means of image simulation and model inversion. Two different approaches are presented. The first is based on model inversion of atmospherically corrected Landsat TM surface reflectance images and assimilation of the retrieved parameters in a crop growth model. In the second approach top-of-atmosphere (TOA) hyperspectral radiance images have been simulated for the future ESA mission SPECTRA. In this case only the simulation of the images has been executed in order to demonstrate the feasibility of this task with existing software running on a PC. The radiative transfer models that have been used are PROSPECT (leaf level), GeoSAIL (canopy level) and MODTRAN4 (atmosphere). Coupling of this chain of models to land use information of the area can be used to generate TOA radiance images. Comparison of simulated images with actual remote sensing data can be applied to retrieve biophysical parameters and in turn these can be employed to update process models of crop growth.},
	biburl = {http://www.bibsonomy.org/bibtex/22d2a5c414376052290c3f31a7e292dcf/jgomezdans},
	keywords = {optical satellite yield BRDF vegetation assimilation canopy modeling crops remotesensing brdf reflectance uncertainty}
}

@article{Moulian1998,
        title = {Combining agricultural crop models and satellite observations: from field to regional scales},
        author = {S. Moulin and A. Bondeau and R. Delecolle},
        journal = {International Journal of Remote Sensing},
        month = {April},
        number = 6,
        pages = {1021-1036},
        volume = 19,
        year = 1998,
        url = {http://www.informaworld.com/smpp/content~content=a713859627~db=all~order=page},
        abstract = {This review article gives an overview of how satellite observations are used to feed or tune crop models and improve their capability to predict crop yields in a region. Relations between crop characteristics which correspond to models state variables and satellite observations are briefly analysed, together with the various types of crop models commonly used. Various strategies for introducing short wavelength radiometric information into specific crop models are described, from direct update of model state variables to optimization of model parameter values, and some of them are exemplified. Methods to unmix crop-specific information from mixed pixels in coarse resolution-high frequency imagery are analysed. The conditions of use of the various methods and types of information are discussed.},
	biburl = {http://www.bibsonomy.org/bibtex/262e4115275e89ecae6d11593bc2e9b02/jgomezdans},
	keywords = {wheat assimilation satellite remotesensing crops uncertainty}
}

@article{BamberGomezDans2005,
        title = {The accuracy of digital elevation models of the Antarctic continent},
        author = {Jonathan Bamber and Jose Luis Gomez-Dans},
        journal = {Earth and Planetary Science Letters},
        month = {#sep#},
        number = {3-4},
        pages = {516--523},
        volume = 237,
        year = 2005,
        url = {http://www.sciencedirect.com/science/article/B6V61-4GV2NPG-1/1/b8d7d62c14b47fa12465b3a56e3fe829},
        abstract = {The accuracy of two widely used digital elevation models of Antarctica was assessed using data from the Geoscience Laser Altimeter System onboard ICESat. The digital elevation models were derived from satellite radar altimeter and terrestrial data sets. The first, termed JLB97, was produced predominantly from ERS-1 data while the second, termed, RAMPv2 included other sources of data in areas of high relief and poor coverage by ERS-1. The accuracy of the models was examined as a function of surface slope and original data source. Large errors, in excess of 100 m, were ubiquitous in both models in areas where terrestrially-derived elevation data had been used but were more extensive in RAMPv2. Elsewhere, the systematic error (bias) was found to be a monotonic function of slope for JLB97, with a more complex, less predictable bias for RAMPv2. The magnitude of the global, slope-dependent, bias ranged from less than a metre to slightly over 10 m but with much larger regional deviations. The random error ranged from about 1 m to over 100 m depending on the DEM and slope. The random error was consistently over a factor two larger for RAMPv2 compared to JLB97.},
	biburl = {http://www.bibsonomy.org/bibtex/27cc31f335e15f2e4429dde6751ee031e/jgomezdans},
	keywords = {satellite model glaciology Antarctica ICESat elevation ERS altimetry GLAS digital dem}
}