Diagnosis Mechanism And Methods Of Crop Chlorophyll Information Based On Hyperspectral Imaging Technology

How to fast, nondestructively and precisely detect crop nutrition status using spectral technology is always a key problem for agricultural quantitative remote sensing. Currently, owing to restriction of ability and price for remote sensors, a larger number of researches were most focused on monitoring crop growth and probing remote sensing mechanism using non-imaging hyperspectra from ASD FieldSpec(?)3 field spectrometers. In recent years, various kinds of hyperspectral imaging spectrometers with low price and high ability were developed and applied in the field by domestic and overseas researchers, which has been an important development trend for quantitative remote sensing.In this work, field hyperspectral imaging spectrometer was used to collect imagery data of crops at both single leaf and multi-angle canopy scales. Those crops specifically included wheat, corn and soybean at different growth stages, and they were then systematically analyzed in the aspects of spectral reflectance data from imageries and correlations between the data and crop chlorophyll information. Three aspects were mainly researched:1) the reliability of spectral data from Pushbroom Imaging Spectrometer (PIS) was validated; 2) remote sensing detection mechanism and retrieval of crop chlorophyll information were explored using the advantage of hyperspectral data combing image with spectrum; 3) through analyzing the influences of multi-angle observation and image classification, the aim was to explain bidirectional reflectance distribution function (BRDF) varied features of crop canopy and estimate its chlorophyll density. Some important conclusions can be drawn as follows:A new method of field calibration was proposed. The processing was to build a transform relationship between two types of digital number (DN) values simultaneously acquired by visible and near-infrared imaging hyperspectral spectrometer (VNIS) and ASD, and then collected DN values of standard white panel by ASD was transformed to results of VNIS, and consequently the reflectance of whole image was calculated by corresponding calibration formulae. The result presented that reflectance curve calculated by the new method was similar with that from ASD, which proved that the proposed method in this study could satisfy the requirement of field calibration for hyperspectral imaging sensors.Through analyzing spectral characteristic curves of red edge and variation of red position extracted from imaging and non-imaging hyperspectral data, the result showed that they had high consistency between two types of data from PIS and ASD, so it could be proved that the performance of self-developed PIS was reliable. On that basis, new characteristic parameters were further extracted from peak-valley features of narrow-band imaging spectral curves, such as spectral change ratio, spectral angles and derivative variables of three edges, and then they were used to assess chlorophyll content of maize leaf. The results showed that new peak-valley characteristic parameters could more effectively improve the accuracy of prediction model of chlorophyll content in comparison with traditional feature parameters from three edges.VNIS was used to collect data combining image with spectrum in the field. After analyzing the spectral differences among vegetation, bare soil, illuminated leaves and shadowed leaves from crop images, normalized spectral classification index was built and used to classify different targets in the image. The results indicated that background soil affected the inversion accuracy of chlorophyll density using spectral remote sensing. In addition, shadowed leaves also influenced the assessment accuracy of chlorophyll density. When the new vegetation index was presented, the shadow percentage as influence factor should be considered and be reflected in the calculation formulae, so the aim of this study was to improve the retrieval accuracy of chlorophyll density.VNIS was also used to collect multi-angle data of soybean canopy in different densities. It could be found that BRDF features of soybean canopy existed in vegetation and soil and pure vegetation (masked bare soil) were specifically researched. Some results can be concluded that 1) For forward observation of principal plane, canopy reflectance of pure soybean vegetation gradually increased when zenith angle changed from 60 degree to 0 degree, which was different from the result of existed vegetation and soil; while for backward observation, canopy reflectance of pure soybean vegetation gradually increased when zenith angle changed from 0 degree to 60 degree, which was same as the result of existed vegetation and soil.2) For forward observation angle of perpendicular principle plane, there were consistent symmetry of BRDF features of soybean canopy in different densities between mixed targets (vegetation and soil) and pure vegetation (masked bare soil), and the symmetry of latter BRDF features was higher than the former.The paper analyzed the correlation between multi-angle spectral data of soybean canopy and chlorophyll density, some results could be concluded that the changes in zenith angles were the most key factor to affect assessment accuracy of chlorophyll density of soybean canopy, and the reason was that the percentage of background soil and shadowed leaves gradually changed in the field of view.