Supplemental Table 1. Description of atmospheric correction methods used in this

study.

Method Reference Description

COST Chavez 1996

Enhance a dark object subtraction model to eliminate atmospheric effects within a single satellite image, assuming that the signal of the dark object is entirely due to atmospheric scattering without consideration of pixel-to-pixel variation.

LEDAPS Masek et al. 2006

Utilize the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) radiative transfer model, which computes transmission, intrinsic reflectance and spherical albedo of aerosol and gases using actual satellite data, as a pixel-to-pixel input.

Supplemental Table 2. Band width of Landsat TM/ETM+, Landsat OLI, and MODIS

data for each band. (Obtained from USGS website.)

DescriptionLandsat TM/ETM+ Landsat OLI MODISBand No.

Band width (μm)

Band No.

Band width (μm)

Band No.

Band width (μm)

Blue 1 0.45 - 0.52 2 0.45 - 0.51 3 0.459 - 0.479Green 2 0.52 - 0.60 3 0.53 - 0.59 4 0.545 - 0.565Red 3 0.63 - 0.69 4 0.64 - 0.67 1 0.620 - 0.670NIR 4 0.76 - 0.90 5 0.85 - 0.88 2 0.841 - 0.876SWIR 1 5 1.55 - 1.75 6 1.57 - 1.65 6 1.628 - 1.652SWIR 2 7 2.09 - 2.35 7 2.11 - 2.29 7 2.105 - 2.155

Supplemental Table 3. Description of topographic correction methods used in this

study.

Method Reference Description

C Teillet et al. 1982

A non-Lambertian model using an empirical constant obtained from regression of illumination condition and uncorrected surface reflectance.

Minnaert Minnaert 1941

A non-Lambertian model introducing an empirical constant, which depends on surface properties.

Modified Minnaert Richter et al. 2009 A model only different from the Cosine correction in areas of low

illumination condition, applying a set of empirical rules.

Minnaert with stratified sampling based on the topographic slope

Murakami 2007

The Minnaert correction determining the Minnaert constant with samples selected by stratified random sampling of the topographic slope and aspect angle.

Gamma Shepherd and Dymond 2003A non-Lambertian model including sensor view angle and inclined terrain angle.

Cosine Teillet et al. 1982A simple Lambertian model with negligible radiative scattering from the surrounding terrain.

Supplemental Table 4. Description of gap-filling methods used in this study.

Method Reference Description

WLR

(Weighted Linear Regression)Zeng et al. 2013

Recover missing pixels by building a linear regression model from the corresponding locally similar pixels with multi-temporal auxiliary images.

Regression Tree Helmer and Ruefenacht 2005Helmer and Ruefenacht 2007

Recover missing pixels using a tree-based model from the pixels with multi-temporal auxiliary images.Random Forest

Multiple Linear Regression Helmer et al. 2010Rulloni et al. 2012

Use a linear regression model to predict missing pixels from auxiliary images.Linear Regression

SVM

(Support Vector Machine)Lorenzi et al. 2013

Recover missing pixels using a support vector machine regressor from the pixels with multi-temporal auxiliary images.

GNSPI(Geostatistical Neighborhood Similar Pixel Interpolator)

Zhu et al. 2012

Use a weighted average interpolator to predict missing pixel values from auxiliary images enhancing a geostatistical theory.

SSG (Spectral Similarity Group)

Jin et al. 2013

Find similar alternative pixels in the auxiliary image and serve as replacement values for missing pixels.

Supplemental Figure 1. Simulated SLC-off and clouds for 500 × 500 pixel subsection.

(a) Cloud-free and SLC-on images, (b) simulated SLC-off images, and (c) simulated

cloud images for subsection A (2000/11/14) and B (2003/1/23).