Evaluation of Sub-Pixel Cloud Noises on MODIS Daily Spectral Indices Based on in situ Measurements

Motohka, Takeshi; Nasahara, Kenlo Nishida; Murakami, Kazutaka; Nagai, Shin

doi:10.3390/rs3081644

Cited by 40 publications

(24 citation statements)

References 43 publications

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“…The limitations of the satellite method as compared to the earth-bound method include lower temporal resolution of data, and effect of cloudiness on the obtained vegetation indices (Motohka et al, 2011;Park, 2013). A definite advantage is free on-line access to MODIS data bases, largely increasing the number of persons and institutions able to monitor the state of plant vegetation in extensive areas on their own account.…”

Section: Introductionmentioning

confidence: 99%

Usefulness of MODIS data for assessment of the growth and development of winter oilseed rape

Bartoszek

2014

Zemdirbyste-Agriculture

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Satellite remote sensing permits the determination of the state of vegetation of plants simultaneously in distant areas. The paper focuses on the assessment of the usefulness of satellite data retrieved from a moderate resolution imaging spectroradiometer (MODIS) for the determination of the growth and development of winter oilseed rape (Brassica napus L.) in the climatic conditions of Central Europe. The study is based on values of five vegetation indicesnormalised difference vegetation index, enhanced vegetation index, leaf area index, fraction of photosynthetically active radiation and gross primary production -from the period 2000-2012, as well as results of measurements of rape plant height before winter and after flowering, and the onset of flowering and ripening. Low values of the standard error of the estimate (SEE) were obtained in models in relation to plant height before winter (SEE < 6 cm) and the onset of flowering (SEE < 3 days). This suggests the possibility of application of vegetation indices from the period preceding measurements and field observations in forecasting of those two characteristics of winter oilseed rape. No statistically significant covariance was found in the case of rape plant height after flowering and the onset of ripening.

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Section: Introductionmentioning

confidence: 99%

Usefulness of MODIS data for assessment of the growth and development of winter oilseed rape

Bartoszek

2014

Zemdirbyste-Agriculture

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“…Analyzing NDVI time-series data has been a useful tool for studying climate, vegetation, and animal distribution, and performance at large spatial and temporal scales [13]. Although NDVI data sets are pre-processed to reduce noise from sensor resolution and calibration, digital quantization errors, ground and atmospheric conditions, and orbital and sensor degradation, some noise is still present in the downloadable data sets, including noise that results from cloud cover, poor atmospheric conditions, and unfavorable sun-sensor-surface viewing geometries [13][14][15][16][17][18][19]. Though the standard maximum value compositing technique (MVC) [14] was used for the main data sets, (e.g., the vegetation index products from Global Inventory Modeling and Map Studies (GIMMS), Advanced Very High Resolution Radiometer (AVHRR) data, Pathfinder AVHRR Land (PAL) data, Satellite Pour l' Observation de la Terre Vegetation (SPOT) VEGETATION (VGT) data, and TERRA or AQUA Moderate Resolution Imaging Spectroradiometer (MODIS) data), these data sets still include some residual noise.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Eight Techniques for Reconstructing Multi-Satellite Sensor Time-Series NDVI Data Sets in the Heihe River Basin, China

et al. 2014

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Abstracts: More than 20 techniques have been developed to de-noise time-series vegetation index data from different satellite sensors to reconstruct long time-series data sets. Although many studies have compared Normalized Difference Vegetation Index (NDVI) noise-reduction techniques, few studies have compared these techniques systematically and comprehensively. This study tested eight techniques for smoothing different vegetation types using different types of multi-temporal NDVI data (Advanced Very High Resolution Radiometer (AVHRR) (Global Inventory Modeling and Map Studies (GIMMS) and Pathfinder AVHRR Land (PAL), Satellite Pour l' Observation de la Terre (SPOT) VEGETATION (VGT), and Moderate Resolution Imaging Spectroradiometer (MODIS) (Terra)) with the ultimate purpose of determining the best reconstruction technique for each type of vegetation captured with four satellite sensors. These techniques include the modified best index slope extraction (M-BISE) technique, the Savitzky-Golay (S-G) technique, the mean value iteration filter (MVI) technique, the asymmetric Gaussian (A-G) technique, the double logistic (D-L) technique, the changing-weight filter (CW) technique, the interpolation for data reconstruction (IDR) technique, and the Whittaker smoother (WS) technique. These techniques were evaluated by calculating the root mean square error (RMSE), the Akaike Information Criterion (AIC), and the Bayesian Information Criterion (BIC). The results indicate that the S-G, CW, and WS techniques perform better than the other tested techniques, while the IDR, M-BISE, and MVI techniques performed worse than the other techniques. The best de-noise technique varies with different vegetation types and NDVI data sources. The S-G performs best in most situations. In addition, the CW and WS are effective techniques that were exceeded only by the S-G technique. The assessment results are consistent in terms of the three evaluation indexes for GIMMS, PAL, and SPOT data in the study area, but not for the MODIS data. The study will be very helpful for choosing reconstruction techniques for long time-series data sets.

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“…The HSSR measures the hyperspectrum of radiation from the sky, canopy reflection, and canopy transmission in visible and near-infrared light (e.g., Motohka et al 2011). In general, paired observations of incoming radiation from the sky and outgoing radiation from the canopy are required to detect the spectral reflectance of the canopy.…”

Section: The Pen Sensor Systemmentioning

confidence: 99%

Review: Development of an in situ observation network for terrestrial ecological remote sensing: the Phenological Eyes Network (PEN)

Nasahara

Nagai

2015

Ecological Research

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104

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The Phenological Eyes Network (PEN), which was established in 2003, is a network of long‐term ground observation sites. The aim of the PEN is to validate terrestrial ecological remote sensing, with a particular focus on seasonal changes (phenology) in vegetation. There are three types of core sensors at PEN sites: an Automatic Digital Fish‐eye Camera, a HemiSpherical SpectroRadiometer, and a Sun Photometer. As of 2014, there are approximately 30 PEN sites, among which many are also FluxNet and/or International Long Term Ecological Research sites. The PEN is now part of a biodiversity observation framework. Collaborations between remote sensing scientists and ecologists working on PEN data have produced various outcomes about remote sensing and long‐term in situ monitoring of ecosystem features, such as phenology, gross primary production, and leaf area index. This article reviews the design concept and the outcomes of the PEN, and discusses its future strategy.

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Evaluation of Sub-Pixel Cloud Noises on MODIS Daily Spectral Indices Based on in situ Measurements

Cited by 40 publications

References 43 publications

Usefulness of MODIS data for assessment of the growth and development of winter oilseed rape

Usefulness of MODIS data for assessment of the growth and development of winter oilseed rape

Comparison of Eight Techniques for Reconstructing Multi-Satellite Sensor Time-Series NDVI Data Sets in the Heihe River Basin, China

Review: Development of an in situ observation network for terrestrial ecological remote sensing: the Phenological Eyes Network (PEN)

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