Chlorophyll fluorescence (ChlF) is an important signature of photosynthesis to evaluate plant response to the environment. We explored an approach to estimate an important leaf ChlF-derived parameter, the intrinsic efficiency of photosystem II photochemistry (Fv/Fm), using spectral indices calculated from leaf reflectance measured by a hyperspectral radiometer. It is observed that leaf chlorophyll content closely related to Fv/Fm in nonstressed leaves, thus the indices developed for chlorophyll estimation were successfully used to estimate Fv/Fm. For leaves under short-term stress, Fv/Fm dropped dramatically while leaf chlorophyll content remained almost the same. Compared to leaf chlorophyll content, reflectance was more sensitive to Fv/Fm variations. As Fv/Fm decreased, the slope of reflectance in the spectrum range of 700 to 900 nm obviously increased, and the first derivative reflectance in the red edge and infrared (NIR) regions was highly correlated with Fv/Fm. The indices using longwave red edge and NIR reflectance (NDRE740 and CI740) worked well for Fv/Fm retrieval in both stressed and nonstressed leaves with the coefficients of determination (R2) above 0.72 and normalized root-mean-square errors below 0.16. Note that the relationships NDRE740 and CI740 versus Fv/Fm were significantly different between nonstressed and stressed leaves, which may give a good implication to detect short-term stress occurrence.
Marine oil pollution is one of the most serious pollutants on the damage to the contemporary marine environment, with the characteristics of a wide range of proliferation, which is difficult to control and eliminate. As a result, marine oil pollution has caused huge economic losses. The remote sensing sensors can detect and record the spectral information of sea film and background seawater. Here we chose to use 250-resolution MODIS data in the area of Dalian Xingang, China where ill spill case was happened on April.4th, 2005. Based on the image pre-processing and enhanced image processing, the spectral features of different bands were analyzed. More obvious characteristics of the spectral range of film was obtained. The oil-water contrast was calculated to evaluate the feature of oil at different spectral band. The result indicates that IR band has the maximum value of reflective. So band ratio was used between 400nm and 800nm and the original radiance images were used between 800nm and 2130nm. In order to get the most obvious images of entropy windows of different sizes were tested in order to decide the optimum window. At last, a FCM fuzzy clustering method and image texture analysis was combined for the MODIS images of the oil spill area segmentation. At last, the oil spill zone was estimated, the results were satisfied.
Snow cover area is a very critical parameter for hydrologic cycle of the Earth. Furthermore, it will be a key factor for the effect of the climate change. An unbelievable situation in mapping snow cover is the existence of clouds. Clouds can easily be found in any image from satellite, because clouds are bright and white in the visible wavelengths. But it is not the case when there is snow or ice in the background. It is similar spectral appearance of snow and clouds. Many cloud decision methods are built on decision trees. The decision trees were designed based on empirical studies and simulations. In this paper a classification trees were used to build the decision tree. And then with a great deal repeating scenes coming from the same area the cloud pixel can be replaced by “its” real surface types, such as snow pixel or vegetation or water. The effect of the cloud can be distinguished in the short wave infrared. The results show that most cloud coverage being removed. A validation was carried out for all subsequent steps. It led to the removal of all remaining cloud cover. The results show that the decision tree method performed satisfied.
Soil moisture content is one of the most important factors in soil business. The basic of detecting soil moisture content
using remote sensing technology is to analyze the relationship between soil moisture content and emissivity. In this paper,
based on the analysis of spectrum collection and processing by a portable spectrometer, a set of measure schemes were
first established which can accurately measure the reflectivity and emissivity of soil spectrum with different moisture
content in near-infrared and thermal infrared bands. Then we selected different bare soil areas as the areas for survey, and
studied the relationship of different moisture content and the spectrum curve in the soil both of the same kind and of
different kind (like the soil whose structure has been modified caused by the change of organic matter contents or soil
particle size). Finally, we emphasized on the quantitative relationship between soil reflectivity & emissivity and soil
moisture content using the test data, and establish a model depicting the quantitative relationship above in near-infrared
and thermal infrared bands.