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| Timely monitoring of crop growth status at different scales is crucial for improving regional crop management decisions. The main objective of the recent study is a model development to predict and estimate crop parameters, here biomass, plant N concentration and plant height, based on different earth observation systems that provide complementary information. Among these are the hyperspectral sensor Hyperion and the multi-spectral sensor ALI based on EO-1 satellite, ASAR on Envisat and TerraSAR-X. Based on the characteristics of the different systems, methods are analysed regarding crop parameter estimation and crop growth status monitoring on a regional level. Factors which are taken into account for optical data are bandwidth and centre of the wavebands, spectral reflectance response from visible and near infrared and the performance of vegetation indices based on the two sensors for semi-empirical parameter estimation. Concerning the SAR sensors Envisat ASAR and TerraSAR-X, parameters such as wavelength, incidence angle and polarization were analysed in terms of crop parameter retrieval and crop status determination. For this, two test areas, one for winter wheat in Northern China Plain (Huimin County) and one for rice in the Northeast of China (Jiansanjiang) were selected. At both sites, intensive ground data collection during the vegetation periods in 2006, 2007 for Huimin and 2009 and 2011 for Jiansanjiang were performed.
Concerning the winter wheat crop monitoring based on hyperspectral, multi-spectral and C-band SAR data, the study was conducted in Huimin County, Shandong Province of China in the growing season of 2005/2006 involving three large winter wheat fields each year managed by different farmers. Winter wheat growth parameters including aboveground biomass, plant N concentration, LAI, and plant height were collected at different growth stages. Three different prediction models were investigated: traditional vegetation indices calculated from broad and narrow bands, and Normalized Ratio Indices (NRI) calculated from all possible two-band combinations of Hyperion between 400 and 2500 nm. The results indicated that TVI performed best among the tested vegetation indices using either broad bands (R2 = 0.69, 0.32 and 0.64 for biomass, N concentration and plant height, respectively) or narrow bands (R2 = 0.71, 0.33 and 0.65 for biomass, N concentration and plant height, respectively). The best performing Normalized Ratio Index (NRI) selected through band combination analysis were significantly better than TVI, achieving R2 of 0.83, 0.81 and 0.79 for biomass, plant N concentration and plant height, respectively. The different NRI models use wavebands from the NIR (centred at 874, 732, and 763 nm) and the SWIR (centred at 1225 and 1305 nm) spectrum with varying bandwidth between 10 and 190 nm. The result of this part proved that vegetation indices derived from NIR- and SWIR-Hyperion spectrum are better predictors of plant aboveground biomass, nitrogen concentration and plant height than indices derived from only visible spectrum. 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 provides 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 parameter. Combined analysis was performed for biomass estimation (R2 = 0.90) with notable improvements in prediction power.
For the rice monitoring in the Sanjiang Plain, Northeast China, the main objective was the understanding of the coherent co-polarised X-band backscattering signature of rice at different phenological stages in order to retrieve growth status. For this, multi-temporal dual polarimetric TerraSAR-X High Resolution SpotLight data (HH/VV) as well as single polarised StripMap (VV) data were acquired from the test site. In conjunction with the satellite data acquisition, a ground truth field campaign was carried out.
The backscattering coefficients at HH and VV of the observed fields were extracted on the different dates and analysed as a function of rice phenology to provide a physical interpretation for the co-polarised backscatter response in a temporal and spatial manner. Then, a correlation analysis was carried out between TerraSAR-X backscattering signal and rice biomass of stem, leaf and head to evaluate the relationship with different vertical layers within the rice vegetation.
HH and VV signatures show two phases of backscatter increase, one at the beginning up to 46 days after transplanting and a second one from 80 days after transplanting onwards. The first increase is related to increasing double bounce reflection from the surface-stem interaction. Then, a decreasing trend of both polarizations can be observed due to signal attenuation by increasing leaf density. A second slight increase is observed during senescence. Correlation analysis showed a significant relationship with different vertical layers at different phenological stages which prove the physical interpretation of X-band backscatter of rice. The seasonal backscatter coefficient showed that X-band is highly sensitive to changes in size, orientation and density of the dominant elements in the upper canopy.
Overall, the study demonstrated successfully the estimation of crop status by multi-sensoral remote sensing data. The use of different sensor systems to acquire timely information is especially important for agricultural decision support systems. Thus, as many different systems are available in the future, the combination of different satellite sources is gaining more importance. | English |
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