Abstract
Cloud contamination is one of the severest problems for the time-series analysis of optical remote sensing data such as vegetation phenology detection. Sub-pixel clouds are especially difficult to identify and remove. It is important for accuracy improvement in various terrestrial remote sensing applications to clarify the influence of these residual clouds on spectral vegetation indices. This study investigated the noises caused by residual sub-pixel clouds on several frequently-used spectral indices (NDVI, EVI, EVI2, NDWI, and NDII) by using in situ spectral data and sky photographs at the satellite overpass time. We conducted in situ continuous observation at a Japanese deciduous forest for over a year and compared the MODIS spectral indices with the cloud-free in situ spectral indices. Our results revealed that residual sub-pixel clouds potentially contaminated about 40% of the MODIS data after cloud screening by the state flag of MOD09 product. These residual clouds significantly decreased NDVI values during the leaf growing season. However, such noises did not appear in the other indices. This result was thought to be caused by the different combination of wavelengths among spectral indices. Our results suggested that the noises by residual sub-pixel clouds can be reduced by using EVI, NDWI, or NDII in place of NDVI.
Highlights
Sensed spectral indices calculated from a combination of band reflectances are widely used to monitor biophysical quantities related to the terrestrial ecosystem, such as leaf area index [1,2,3,4]and leaf onset/offset phenology [5,6]
We investigated the effects of cloud contamination on several widely used spectral indices measured by the moderate resolution imaging spectroradiometer (MODIS) sensor: Normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), EVI2, normalized difference water index (NDWI), and normalized difference infrared index (NDII)
When the ground was covered with snow, NDVI, EVI, and EVI2 showed their minimum values for the year; on the other hand, NDWI and NDII showed maximum values for the year
Summary
Sensed spectral indices calculated from a combination of band reflectances are widely used to monitor biophysical quantities related to the terrestrial ecosystem, such as leaf area index [1,2,3,4]and leaf onset/offset phenology [5,6]. Sensed spectral indices calculated from a combination of band reflectances are widely used to monitor biophysical quantities related to the terrestrial ecosystem, such as leaf area index [1,2,3,4]. Clouds contaminate these data, and obscure the monitoring by optical remote sensing satellites. Clouds smaller than a satellite image pixel, i.e., sub-pixel clouds, often hinder the retrieval of biophysical parameters of the ecosystem from spectral indices. Cloud masks have been produced and demonstrated to be effective [7], they are not always able to completely offset the contamination caused by clouds [8]. To completely remove cloud noise from time-series spectral indices data, we should first understand the behavior of cloud noise in several frequently used spectral indices. Normalized difference vegetation index (NDVI) [9] is the most widely used spectral index for monitoring vegetation changes
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