Article

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: 3.47). 08/2003; DOI: 10.1109/TGRS.2003.813531
Source: IEEE Xplore

ABSTRACT 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.

0 Bookmarks
 · 
87 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The provincial wetland (“status quo”) maps of the Prairie Pothole Region, Central Canada, do not adequately depict wetland resources and properties. Using satellite remote sensing data from both LANDSAT Enhanced Thematic Mapper Plus (ETM+) and RADARSAT-1 Synthetic Aperture Radar (SAR) results in a more complete picture, although using both sources together is better than when either source is used alone. This study integrates LANDSAT ETM+, RADARSAT-1 SAR, and LIght Detection And Ranging (LIDAR) data, taking advantage of the synergy in their integration. A simple density slicing of the ETM-5 band was used to map inundated areas from LANDSAT ETM+. A fuzzy thresholding technique was used to map wet areas using RADARSAT-1 SAR data after information from LIDAR-DEMs had been used to correct confusing radar backscatter overlaps between open water and dry, flat, smooth surfaces. Compared to the “status quo”, the integrated approach mapped 113% to 600% and 217% to 467% increases in the size of wet areas and pond densities, respectively. Maps based on the ETM-5 band alone detected 133% to 333% and 50% to 350% increases in the size of wet areas and pond densities, respectively over the “status quo” map, while maps based on the RADARSAT-1 SAR data detected 63% to 450% and 100% to 333% increases. The improved mapping capability is attributed to a combinatory power of the integrated approach in detecting small, transient and saturated wet areas.Highlights► This study integrates LANDSAT ETM+, RADARSAT-1 SAR, and LIDAR data for prairie mapping. ► A fuzzy thresholding technique was used to map wet areas using RADARSAT−1 SAR data. ► LIDAR was used to correct radar backscatter overlaps between open water and dry, flat surfaces. ► The integrated approach increases in the size of wet areas and pond densities.
    Catena 12/2013; 95:12-23. · 1.88 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: In this letter, a C-band SAR classification algorithm mapping agricultural crops dominated by surface or volume scattering is derived and assessed. The algorithm is an adaptive thresholding method based on the iterative solution of the Kittler-Illingworth method applied to exploit temporal series of cross-polarized SAR data. The performances of the classification algorithm have been assessed on ENVISAT ASAR data acquired over Görmin (Germany) during the AgriSAR'06 campaign and on RADARSAT-2 data acquired over Flevoland (The Netherlands) and Indian Head (Canada) during the ESA AgriSAR'09 campaign. The results indicate that the classification method improves the accuracy with respect to the one obtained by the threshold method based on a constant value, unless the data distributions are mono-modal. The algorithm is fast and robust versus changes of site location and it is expected to achieve an average overall accuracy better than 80%.
    IEEE Geoscience and Remote Sensing Letters 01/2014; 11(2):384-388. · 1.82 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper illustrates the results obtained in the frame of experimental campaigns carried out on winter wheat fields in the North China Plain from March 2006 to June 2007. Investigations focused on the methodology of estimating biomass on a regional scale with hyperspectral (EO-1 Hyperion) and microwave data (Envisat ASAR). Special importance is drawn to the combined analysis of microwave and optical satellite data for crop monitoring. Since hyperspectral and synthetic aperture radar (SAR) sensors respond to crop characteristics differently, their complementary information content can support the estimation of crop conditions. During the regular field measurements, satellite data from jointing to ripening stages were acquired. Linear regression models between measured surface reflection as well as surface backscatter and wheat’s standing biomass were established. For hyperspectral data, the normalized ratio index (NRI) based on 825 nm and 1225 nm wavebands was calculated from 2006 data as input for the regression model. In addition, Envisat ASAR VV polarization data were related to winter wheat crop parameters. Bivariate correlation results from this study indicate that both multi-temporal EO-1 Hyperion as well as Envisat ASAR data provide notable relationships with crop conditions. As expected, linear correlation of hyperspectral data performed slightly better for biomass estimation (R2 = 0.83) than microwave data (R2 = 0.75) for the 2006 field survey. Based on the results, hyperspectral Hyperion data seem to be more sensitive to crop conditions. Improvements for crop parameter estimation were achieved by combining hyperspectral indices and microwave backscatter into a multiple regression analysis as a function of crop parameters. Combined analysis was performed for biomass estimation (R2 = 0.90) with notable improvements in prediction power.
    Photogrammetrie - Fernerkundung - Geoinformation 06/2012; 2012 (3)(3):0281-0298. · 0.74 Impact Factor