Airborne measurements of the partial column-averaged dry-air mixing ratio of CO2 (XCO2) and moderate airplane height detection were performed using an amplitude-modulated 1.57-μm differential laser absorption spectrometer (LAS). The LAS system proved to be compact, reliable, and rigid in the field measurements. Airborne demonstration flights resulted in a high correlation coefficient of 0.987 between XCO2 observed from LAS and XCO2 calculated from in situ measurements. The average XCO2 obtained from LAS and in situ data agreed within 1.5 ppm, and the method achieved a measurement precision of 2.5 ppm for spiral measurements.
A method is proposed herein PALSAR (Phased Array-type L-band Synthetic Aperture Radar) data to improve the detection of sea ice. This method utilizes scattering entropy calculated from SAR data to discriminate sea ice from open water. In this study, we used sea ice reference derived from MODIS (Moderate Resolution Imaging Spectro-radiometer) visible and near-infrared data. The proposed method is applied to fully polarimetric PALSAR data acquired in 2009 and 2010 that covers the Sea of Okhotsk. We applied linear discriminant analysis to samples of sea ice and open water in order to compare our method with the conventional method based on backscattering coefficients. We found that sea ice and open water were best differentiated using scattering entropy compared to any combination of backscattering coefficients.
The consistency of spectral data is essential in constellation-use of various hig-resolution satellite sensors for timely monitoring of crops and farmlands. The objective of this study was to evaluate the consistency of spectral measurements for rice paddy taken by multiple hig-resolution optical satellite sensors. We compared the reflectance spectra and four vegetation indices (NDVI, GRVI, EVI and SAVI) derived from satellite images with ground-based spectral data. The effects of atmospheric correction methods (6S, QUAC and FLAASH) were also examined. The Spectral Angle Mapper (SAM) values, as an index of the similarity, were 0.110, 0.111 and 0.312 in order of the QUAC, 6S and FLAASH, respectively. The reflectance spectra estimated by QUAC and 6S had a high degree of similarity with the ground-based spectral data. The Mean Relative Error (MRE) of NDVI, EVI and SAVI derived from satellite reflectance data against the ground-based indices was 20-40 %, whereas it was 80-90 % for the VIs derived from DN and radiance data. It therefore appears that hig-resolution optical satellite images from multiple sensors can be used for the consistent monitoring of crops and farmlands, as long as appropriate atmospheric-correction methods such as QUAC and 6S are applied.