Multitemporal C-band radar measurements on wheat fields

Inst. di Radioastronomia, Consiglio Nazionale delle Ricerche, Matera, Italy
IEEE Transactions on Geoscience and Remote Sensing (Impact Factor: 3.51). 08/2003; 41(7):1551 - 1560. DOI: 10.1109/TGRS.2003.813531
Source: IEEE Xplore


This paper investigates the relationship between C-band backscatter measurements and wheat biomass and the underlying soil moisture content. It aims to define strategies for retrieval algorithms with a view to using satellite C-band synthetic aperture radar (SAR) data to monitor wheat growth. The study is based on a ground-based scatterometer experiment conducted on a wheat field at the Matera site in Italy during the 2001 growing season. From March to June 2001, eight C-band scatterometer acquisitions at horizontal-horizontal and vertical-vertical polarization, with incidence angles ranging from 23° to 60°, were taken. At the same time, soil moisture, wheat biomass, and canopy structure were collected. The paper describes the experiment and investigates the radar sensitivity to biophysical parameters at different polarizations and incidence angles, and at different wheat phenological stages. Based on the experimental results, the retrieval of wheat biomass and soil moisture content using Advanced Synthetic Aperture Radar data is discussed.

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Available from: F. Mattia, Oct 28, 2014
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    • "Several theoretical and experimental studies, reviewed by McNairn and Brisco (2004), demonstrated the sensitivity of the microwave backscattering coefficient to vegetation and soil parameters, such as plant water content, LAI, soil moisture and soil roughness. For specific crops, such as wheat, simplified retrieval algorithms have also been developed based on the strong relationship observed between crop parameters, such as biomass and LAI, and the ratio of the HH and VV polarised backscattering coefficient, acquired at C-band and at high incidence angles, and based on its relatively low sensitivity to the soil moisture (Brown et al., 2003; Mattia et al., 2003). However, due to the long revisit time of ASAR sensor (i.e. "
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    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.
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    • "The confidence interval widths were plotted versus the number of pixels used in the calculation which translates directly to ground area. Confidence intervals have been used to establish the suitability of backscatter estimates for soil moisture retrieval (Griffiths & Wooding, 1996; Mattia et al., 2003) but have not been used in the inverse sense to determine the spatial scale over variable surfaces required for an estimate of known quality. 3.3. "
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