Bo-Cai Gao

Derivation of scaled surface reflectances from AVIRIS data

An operational software program is now available for deriving scaled surface reflectances from spectral data collected by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). The program simulates both the atmospheric scattering and absorption effects. Brief descriptions of the algorithm, inputs, outputs, the limitations of the software, and procedures for obtaining the software are given.

Retrieval of equivalent water thickness and information related to biochemical components of vegetation canopies from AVIRIS data

Abstract Remote sensing of water status and biochemical components of vegetation can have important applications in the fields of agriculture and forestry. Reflectance of fresh, green vegetation in the 1.0–2.5 μm region is dominated by liquid water absorption and also weakly affected by absorption due to biochemical components, such as protein, lignin, and cellulose. We have developed both the nonlinear and linear least squares spectrum-matching techniques for deriving equivalent water thickness (EWT) of vegetation from AVIRIS data in the 1.0 μm and 1.6 μm regions. EWT values are compared with in situ canopy measurements in Harvard Forest, Massachusetts. Seasonal variations of EWTs over an agricultural area in Greeley, Colorado are determined. EWTs from the 1.0 μm region are generally greater than those from the 1.6 μm region. This is because the absorptivity of water near 1.0 μm is much less than that at 1.6 μm—resulting in a greater mean absorption path length in the 1.0 μ, region. After fitting the AVIRIS data with a reflectance spectrum of water, a weak lignin-cellulose absorption feature centered at 1.72 μm is seen in the difference spectra. We map the depth of the 1.72-μ feature, which can be considered as an index of abundance of those compounds in the canopy. AVIRIS data for the Harvard Forest, Massachusetts were analyzed and compared with laboratory chemical analysis of the foliage. The results obtained with the linear least squares spectrum-matching technique are comparable to the stepwise linear regression results obtained from first difference spectra.

Cirrus cloud detection from Airborne Imaging Spectrometer data using the 1.38 µm water vapor band

Thin cirrus clouds are difficult to detect, particularly over land, in images taken from current satellite platforms. Using spectral images acquired by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) at 20 km altitude, we show that wavelengths close to the center of the strong 1.38 µm water vapor band are useful for detecting thin cirrus clouds. The detection makes use of the fact that cirrus clouds are located above almost all the atmospheric water vapor. Because of the strong water vapor absorption in the lower atmosphere, AVIRIS channels near 1.38 µm receive little scattered solar radiance from the surface or low level clouds. When cirrus clouds are present, however, these channels receive large amounts of scattered solar radiance from the cirrus clouds. Our ability to determine cirrus cloud cover using space-based remote sensing will be improved if channels near the center of the 1.38 µm water vapor band are added to future satellites.

Column Atmospheric Water Vapor Retrievals From Awborne Imaging Spectrometer Data

High spatial resolution column atmospheric water vapor amounts were derived from spectral data collected by the Airborne Visible Infrared Imaging Spectrometer (A VIruS). The quantitatiVe derivation is made by curve fitting observed spel:tra with calculated spectra in the 1.14 J.Lm and 0.94 J.LID water vapor band absorption regions with a non-linear least squares technique. The precision of the retrieved column water vapor amounts is approximately 5%. The derived column water vapor amounts are independent of the absolute surface reflectance. Curve fitting of spectra near 1 J.Lm from areas covered with vegetation indicates that both the amount of atmospheric water vapor and the moisture content of vegetation can be retrieved simultaneously. It now appears feasible to derive high spatial resolution column water vapor amounts over land areas from satellite altitude with the proposed High Resolution Imaging Spectrometer (HIRIS) and pos.sibly with the proposed Moderate Resolution Imaging Spectrometer (MODIS).

Determination of total column water vapor in the atmosphere at high spatial resolution from AVIRIS data using spectral curve fitting and band ratioing techniques

Column atmospheric water vapor amounts at high spatial resolution were derived from spectral data collected by the airborne visible-infrared imaging spectrometer (AVIRIS). The quantitative derivation is made by curve fitting observed spectra with calculated spectra in the 0. 94 jim and 1. 14 jim water vapor band absorption regions using an atmospheric model a narrow band spectral model and a nonlinear least squares fitting technique. The derivation is also made using a band ratioing technique. These techniques are applicable for retrieving water vapor values from AVIRIS data measured on clear days with visibilities 20 km or greater. The precision of the retrieved column water vapor amounts is 5 or better. It now appears feasible to derive high spatial resolution column water vapor amounts over land areas from satellite altitude with the proposed high resolution imaging spectrometer (HIRIS). Curve fitting of spectra near 1 jim from areas covered with vegetation using an atmospheric model and a simplified vegetation reflectance model indicates that both the amount of atmospheric water vapor and the moisture content of vegetation can be retrieved simultaneously because the band centers of liquid water in vegetation and the atmospheric water vapor are offset by approxinuitely 0. 05 jim. 1.

Retrievals of Surface Reflectance from Aviris Data

Analysis of high resolution imaging spectrometer data requires a thorough compensation for atmospheric absorption and scattering. A method for retrieving surface reflectances from spectral data collected by the Airborne Visibleflnfrared Imaging Spectrometer (AVIRIS) is being developed. In this method, the integrated water vapor amount on a pixel by pixel basis is derived from the 0.94- and 1.14-pm water vapor features. The water vapor, carbon dioxide (C02), oxygen (02) and methane (CH4) transmission spectrum in the 0.4-2.5 pm region is calculated. The derived water vapor value and the solar and observational geometry are used in the calculation. The AVIRIS spectrum is ratioed against the transmission spectrum to obtain the surface reflectance spectrum. Major mineral ibsorption features near 2.2 pm in retrieved reflectance spectra can be identified. Different vegetation absorption characteristics are observed. At present, the method is most useful for deriving surface reflectances from AVIRIS data measured on clear days with high visibilities. Atmospheric scattering effects are not yet included in our spectral calculations.

Derivation of equivalent water thickness and an index of biochemical component abundance in vegetation from AVIRIS data

Remote sensing of water status and biochemical components of vegetation can have important applications in the fields of agriculture and forestry. Reflectance of fresh, green vegetation in the 1.0 - 2.5 micrometers region is dominated by liquid water absorption and also weakly affected by absorption due to biochemical components, such as lignin and cellulose. We have developed both the nonlinear and linear least squares spectrum- matching techniques for deriving equivalent water thickness (EWT) of vegetation from AVIRIS data in the 1.0 and 1.6 micrometers regions. Seasonal variations of EWTs over an agricultural area in Greeley, Colorado are determined. EWTs from 1.0 micrometers region are generally greater than those from 1.6 micrometers region because of the deeper light penetration into the canopy. After fitting the AVIRIS data with water spectrum alone, a weak lignin-cellulose absorption feature centered at 1.72 micrometers is seen in the residual spectra. We map the depth of the 1.72-micrometers feature, which can be considered as an index of component abundance in the canopy.