Evaluation of sensor calibration uncertainties on vegetation indices for MODIS

Miura, Tomoaki; Huete, Alfredo; Yoshioka, Hiroki

doi:10.1109/36.843034

Cited by 78 publications

(46 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The EVI dynamics, as compared with other VIs, showed a higher scatter, in particular for high EVI values. This result confirmed a previous study by Miura et al (2000) that demonstrated that EVI uncertainties tended to increase with increasing VI values and attributed this uncertainty to the inclusion of the blue band in VI formulation for EVI values above 0.4 (between DOY 180 and 225 in our study). PRI 645 and PRI 667 exhibited a pattern similar to other VIs, while PRI 555 and PRI 551 showed an opposite trend characterised by a progressive decrease at the beginning of the growing season up to maximum canopy development and a slower increase in the senescent phase.…”

Section: Seasonal Variability Of Spectral Datasupporting

confidence: 93%

Remote sensing-based estimation of gross primary production in a subalpine grassland

et al. 2012

View full text Add to dashboard Cite

Abstract. This study investigates the performances in a terrestrial ecosystem of gross primary production (GPP) estimation of a suite of spectral vegetation indexes (VIs) that can be computed from currently orbiting platforms. Vegetation indexes were computed from near-surface field spectroscopy measurements collected using an automatic system designed for high temporal frequency acquisition of spectral measurements in the visible near-infrared region. Spectral observations were collected for two consecutive years in Italy in a subalpine grassland equipped with an eddy covariance (EC) flux tower that provides continuous measurements of net ecosystem carbon dioxide (CO 2 ) exchange (NEE) and the derived GPP. Different VIs were calculated based on ESA-MERIS and NASA-MODIS spectral bands and correlated with biophysical (Leaf area index, LAI; fraction of photosynthetically active radiation intercepted by green vegetation, f IPAR g ), biochemical (chlorophyll concentration) and ecophysiological (green light-use efficiency, LUE g ) canopy variables. In this study, the normalized difference vegetation index (NDVI) was the index best correlated with LAI and f IPAR g (r = 0.90 and 0.95, respectively), the MERIS terrestrial chlorophyll index (MTCI) with leaf chlorophyll content (r = 0.91) and the photochemical reflectance index (PRI 551 ), computed as (R 531 − R 551 )/(R 531 + R 551 ) with LUE g (r = 0.64).Subsequently, these VIs were used to estimate GPP using different modelling solutions based on Monteith's lightuse efficiency model describing the GPP as driven by the photosynthetically active radiation absorbed by green vegetation (APAR g ) and by the efficiency (ε) with which plants use the absorbed radiation to fix carbon via photosynthesis. Results show that GPP can be successfully modelled with a combination of VIs and meteorological data or VIs only. Vegetation indexes designed to be more sensitive to chlorophyll content explained most of the variability in GPP in the ecosystem investigated, characterised by a strong seasonal dynamic of GPP. Accuracy in GPP estimation slightly improves when taking into account high frequency modulations of GPP driven by incident PAR or modelling LUE g with the PRI in model formulation. Similar results were obtained for both measured daily VIs and VIs obtained as 16-day composite time series and then downscaled from the compositing period to daily scale (resampled data). However, the use of resampled data rather than measured daily input data decreases the accuracy of the total GPP estimation on an annual basis.

show abstract

Section: Seasonal Variability Of Spectral Datasupporting

confidence: 93%

Remote sensing-based estimation of gross primary production in a subalpine grassland

et al. 2012

View full text Add to dashboard Cite

show abstract

“…Vegetation indices (VIs) derived from satellite data have been widely used to assess variations in the physiological state and biophysical properties of vegetation [7][8][9]. However, due to the various sensor characteristics, there are differences among VIs derived from multiple sensors for the same target [10][11][12][13][14]. Therefore, multi-sensor VI continuity and compatibility are critical but complicated issues in the application of multi-sensor vegetation observations [11,[15][16][17][18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Comparison of the Continuity of Vegetation Indices Derived from Landsat 8 OLI and Landsat 7 ETM+ Data among Different Vegetation Types

She

Zhang

et al. 2015

Remote Sensing

View full text Add to dashboard Cite

Landsat 8, the most recently launched satellite of the series, promises to maintain the continuity of Landsat 7. However, in addition to subtle differences in sensor characteristics and vegetation index (VI) generation algorithms, VIs respond differently to the seasonality of the various types of vegetation cover. The purpose of this study was to elucidate the effects of these variations on VIs between Operational Land Imager (OLI) and Enhanced Thematic Mapper Plus (ETM+). Ground spectral data for vegetation were used to simulate the Landsat at-senor broadband reflectance, with consideration of sensor band-pass differences. Three band-geometric VIs (Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI)) and two band-transformation VIs (Vegetation Index based on the Universal Pattern Decomposition method (VIUPD), Tasseled Cap Transformation Greenness (TCG)) were tested to evaluate the performance of various VI generation algorithms in relation to multi-sensor continuity. Six vegetation types were included to evaluate the continuity in different vegetation types. Four pairs of data during four seasons were selected to evaluate continuity with respect to seasonal variation. The simulated data showed that OLI largely inherits the band-pass characteristics of ETM+. Overall, the continuity of band-transformation derived VIs was higher than OPEN ACCESSRemote Sens. 2015, 7 13486 band-geometry derived VIs. VI continuity was higher in the three forest types and the shrubs in the relatively rapid growth periods of summer and autumn, but lower for the other two non-forest types (grassland and crops) during the same periods.

show abstract

“…Using an analytical approach, we derived a factor that deterministically compares the relative robustness of two algorithms. Note that sources of error can arise from various intervening factors, such as poor radiometric calibration and atmospheric contamination [30,31]. As a result, errors in the reflectance spectra eventually propagate into FVC estimations [32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Comparison of the Noise Robustness of FVC Retrieval Algorithms Based on Linear Mixture Models

Obata

Yoshioka

2011

Remote Sensing

View full text Add to dashboard Cite

The fraction of vegetation cover (FVC) is often estimated by unmixing a linear mixture model (LMM) to assess the horizontal spread of vegetation within a pixel based on a remotely sensed reflectance spectrum. The LMM-based algorithm produces results that can vary to a certain degree, depending on the model assumptions. For example, the robustness of the results depends on the presence of errors in the measured reflectance spectra. The objective of this study was to derive a factor that could be used to assess the robustness of LMM-based algorithms under a two-endmember assumption. The factor was derived from the analytical relationship between FVC values determined according to several previously described algorithms. The factor depended on the target spectra, endmember spectra, and choice of the spectral vegetation index. Numerical simulations were conducted to demonstrate the dependence and usefulness of the technique in terms of robustness against the measurement noise.

show abstract

Evaluation of sensor calibration uncertainties on vegetation indices for MODIS

Cited by 78 publications

References 24 publications

Remote sensing-based estimation of gross primary production in a subalpine grassland

Remote sensing-based estimation of gross primary production in a subalpine grassland

Comparison of the Continuity of Vegetation Indices Derived from Landsat 8 OLI and Landsat 7 ETM+ Data among Different Vegetation Types

Comparison of the Noise Robustness of FVC Retrieval Algorithms Based on Linear Mixture Models

Contact Info

Product

Resources

About