%0 
%0 Journal Article
%A Chehbouni, A.; Escadafal, R.; Duchemin, B.; Boulet, G.; Simonneaux, V.; Dedieu, G.; Mougenot, B.; Khabba, S.; Kharrou, H.; Maisongrande, P.; Merlin, O.; Chaponnière, A.; Ezzahar, J.; Er-Raki, S.; Hoedjes, J.; Hadria, R.; Abourida, A.; Cheggour, A.; Raibi, F.; Boudhar, A.; Benhadj, I.; Hanich, L.; Benkaddour, A.; Guemouria, N.; Chehbouni, A. H.; Lahrouni, A.; Olioso, A.; Jacob, F.; Williams, D. G. & Sobrino, J. A.
%D 2008
%T An integrated modelling and remote sensing approach for hydrological study in arid and semi-arid regions: the SUDMED Programme
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%B International Journal of Remote Sensing
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%I 
%V 29
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%P 5161 - 5181
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%F sudmed2008
%K drought evaporation evapotranspiration models moisture remotesensing satellite vegetation 
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%U http://www.informaworld.com/10.1080/01431160802036417
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%0 Journal Article
%A Dorigo, W.A.; Zurita-Milla, R.; de Wit, A.J.W.; Brazile, J.; Singh, R. & Schaepman, M.E.
%D 2007
%T A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling
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%B International Journal of Applied Earth Observation and Geoinformation
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%I 
%V 9
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%N 2
%P 165--193
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%F Dorigo2006
%K Crop agriculture agronomic assimilation crops kalmanfilter models remotesensing satellite uncertainty vegetation yield 
%X 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.
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%U http://www.sciencedirect.com/science/article/B6X2F-4KCXJND-1/2/6b5e172d43f455de979fa8ee7a7ff295
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%0 
%0 Journal Article
%A Gabban, A.; San-Miguel-Ayanz, J. & Viegas, D. X.
%D 2008
%T A comparative analysis of the use of NOAA-AVHRR NDVI and FWI data for forest fire risk assessment
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%B International Journal of Remote Sensing
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%I 
%V 29
%6
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%P 5677 - 5687
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%F Gabban.2008
%K fire firemafs fires risk satellite 
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%U http://www.informaworld.com/10.1080/01431160801958397
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%0 Journal Article
%A Jackson, Thomas J.; Chen, Daoyi; Cosh, Michael; Li, Fuqin; Anderson, Martha; Walthall, Charles; Doriaswamy, Paul & Hunt, E. Ray
%D 2004
%T Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans
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%B Remote Sensing of Environment
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%I 
%V 92
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%N 4
%P 475--482
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%F Jackson2004
%K Soil drought evaporation moisture ndwi reflectance remotesensing satellite soilmoisture stress vegetation 
%X 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.
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%U http://www.sciencedirect.com/science/article/B6V6V-4B8BWHY-1/1/bcff206f3a4c9678088761e8bc7d7e98
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%0 Conference Proceedings
%A N., VIOVY; C., FRANCOIS; A., BONDEAU; G., KRINNER; J., POLCHER; L., KERGOAT; G., DEDIEU; N., DE NOBLET; P., CIAIS & P., FRIEDLINGSTEIN
%D 2001
%T Assimilation of remote sensing measurements into the ORCHIDEE/STOMATE DGVM biosphere model
%E 
%B Physical measurements & signatures in remote sensing. International symposium
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%P 713-718
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%F Viovy2001
%K assimilation lai models satellite statistics uncertainty vegetation 
%X 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.
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%U http://dods.ipsl.jussieu.fr/orchidee/WEBORCHIDEE/aussois5.pdf
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%0 Journal Article
%A P., Ceccato & S., Flasse
%D October 2002
%T Designing a spectral index to estimate vegetation water content from remote sensing data - Part 2. Validation and applications
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%B Remote Sensing of Environment
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%V 82
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%P 198-207(10)
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%F Ceccato:October2002:0034-4257:198
%K fire firemafs fires inversions modeling moisture optical radiative radiativetransfer reflectance remotesensing satellite vegetation wildfire 
%X The Global Vegetation Moisture Index (GVMI) was developed to retrieve vegetation water content from local to global scale rapidly and reliably using SPOT-VEGETATION data. This paper validates the GVMI with field measurements of vegetation water content measured over four different ecosystems in Senegal. Two of the sites show exact concordance between GVMI-derived and field-measured water content. The remaining two sites show differences in value but provide identical evolution over time. Comparison between ecosystems illustrates that GVMI-derived water content is consistent with field measurements of water content expressed as a quantity of water per unit area. Additional study shows that GVMI is not related to the vegetation moisture content expressed as a percentage of water per quantity of biomass. Comparison between the GVMI and NDVI methods also illustrates that the NDVI provides different information (vegetation greenness), which is not directly related to the quantity of water in the vegetation. Potential applications of the new GVMI are also discussed.
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%U http://www.ingentaconnect.com/content/els/00344257/2002/00000082/00000002/art00036
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%0 Journal Article
%A Renzullo, Luigi J.; Barrett, Damian J.; Marks, Alan S.; Hill, Michael J.; Guerschman, Juan P.; Mu, Qiaozhen & Running, Steve W.
%D 2008
%T Multi-sensor model-data fusion for estimation of hydrologic and energy flux parameters
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%B Remote Sensing of Environment
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%I 
%V 112
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%N 4
%P 1306--1319
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%F Renzullo2008
%K Bayes Soil assimilation bayes drought evaporation evapotranspiration inference mcmc models moisture remotesensing satellite soilmoisture statistics uncertainty vegetation 
%X 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.
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%U http://www.sciencedirect.com/science/article/B6V6V-4PK8MPH-3/2/f495f28883b0df1eb96683f5e4659117
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%0 Book Section
%A Wang, L.; Zhou, Y.; Wang, S. & Chen, S.
%D 2004
%T Monitoring for grassland and forest fire danger using remote sensing data
%E 
%B Geoscience and Remote Sensing Symposium, 2004. IGARSS'04. Proceedings. 2004 IEEE International
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%V 3
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%P 2095-2098
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%F wang2004mga
%K fire firemafs fires models moisture remotesensing satellite wildfire 
%X Grassland and forest fires do a lot of economic and environmental damages. Based on the ecological and environment features in China, our team constructed the Grassland Fire Danger Index (GFDI) using remote sensing images and weather station data in 2002, which provided early warning information for grassland fires. In the following year of 2003, the Fire Danger Index (FDI) algorithm completely using remote sensing data was developed and tested. In the year of 2004, the FDI was regularly distributed to predict the fire danger status. In this paper, the construction, accuracy testing and applications of the FDI based on remote sensing data are discussed in detail
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%U http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1370770
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%0 Journal Article
%A van der Werf, G. R.; Morton, D. C.; DeFries, R. S.; Giglio, L.; Randerson, J. T.; Collatz, G. J. & Kasibhatla, P. S.
%D 2008
%T Estimates of fire emissions from an active deforestation region in the southern Amazon based on satellite data and biogeochemical modelling
%E 
%B Biogeosciences Discussions
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%I 
%V 5
%6
%N 4
%P 3533--3573
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%F bgd-5-3533-2008
%K amazonia fire firemafs fires models rainforest remotesensing satellite uncertainty vegetation wildfire 
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%U http://www.biogeosciences-discuss.net/5/3533/2008/
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%0 Journal Article
%A 
%D 2007
%T Unsupervised classification of saturated areas using a time series of remotely sensed images
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%B Hydrology and Earth System Sciences
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%V 4
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%P 1663-1696
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%F DeAlwis2007
%K classification drought moisture reflectance remotesensing satellite vegetation 
%X 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.

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%U http://www.hydrol-earth-syst-sci-discuss.net/4/1663/2007/
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