SATELITE IMAGERY

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MOD17A2H is an 8-day composite at 1-km spatial resolution delivered as a gridded Level-4 product in Sinusoidal projection. The MODIS Adaptive Processing System (MODAPS) at the NASA Goddard Space Flight Center produces this Collection-5 GPP product. This version contains some cloud-contaminated LAI/FPAR inputs that users should exclude by flagging them as part of the quality assessment filters. Once the cloud-contaminated pixels are excluded, this dataset may serve the near-real-time needs for the current calendar year.

Creative Commons Attribution 4.0 International

OFFICIAL SITE

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The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra MOD09A1 Version 6 product provides an estimate of the surface spectral reflectance of Terra MODIS Bands 1 through 7 corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the seven 500 meter (m) reflectance bands are two quality layers and four observation bands. For each pixel, a value is selected from all the acquisitions within the 8-day composite period. The criteria for the pixel choice include cloud and solar zenith. When several acquisitions meet the criteria the pixel with the minimum channel 3 (blue) value is used.

Creative Commons Attribution 4.0 International

OFFICIAL SITE

DESCRIPTION

MOD09Q1 is a product from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard NASA's Terra and Aqua satellites. It provides information on land surface reflectance at a 250-meter spatial resolution. The data are composited over an 8-day period, meaning that for each pixel, the reflectance value represents the best observation obtained within that timeframe.

This composite approach helps to reduce the impact of clouds and atmospheric disturbances on the data, providing more reliable and consistent information about land surface properties over time.

Creative Commons Attribution 4.0 International

DOWNLOADS: Raster Image

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It provides information on land surface reflectance at a 250-meter spatial resolution. The data are composited over an 8-day period, meaning that for each pixel, the reflectance value represents the best observation obtained within that timeframe.
A time series has been generated using the NDVI index at 250 meters every 8 days across the entire Iberian Peninsula.

MOD
09Q1
NDVI 250
TS 2000 - 2012

Creative Commons Attribution 4.0 International

DOWNLOADS Raster Image


DESCRIPTION:

Vegetation seasonality assessment through remote sensing data is crucial to understand ecosystem responses to climatic variations and human activities at large-scales. Whereas the study of the timing of phenological events showed significant advances, their recurrence patterns at different periodicities has not been widely study, especially at global scale. In this work, we describe vegetation oscillations by a novel quantitative approach based on the spectral analysis of Normalized Difference Vegetation Index (NDVI) time series. A new set of global periodicity indicators permitted to identify different seasonal patterns regarding the intra-annual cycles (the number, amplitude, and stability) and to evaluate the existence of pluri-annual cycles, even in those regions with noisy or low NDVI. The indicator maps compiled in this work provide highly relevant and practical information to advance in assessing global vegetation dynamics in the context of global change.

MOD
09A1
MOD09A1
TS

Creative Commons Attribution 4.0 International

DOWNLOADS Raster Image


DESCRIPTION:

Vegetation seasonality assessment through remote sensing data is crucial to understand ecosystem responses to climatic variations and human activities at large-scales. Whereas the study of the timing of phenological events showed significant advances, their recurrence patterns at different periodicities has not been widely study, especially at global scale. In this work, we describe vegetation oscillations by a novel quantitative approach based on the spectral analysis of Normalized Difference Vegetation Index (NDVI) time series. A new set of global periodicity indicators permitted to identify different seasonal patterns regarding the intra-annual cycles (the number, amplitude, and stability) and to evaluate the existence of pluri-annual cycles, even in those regions with noisy or low NDVI. The indicator maps compiled in this work provide highly relevant and practical information to advance in assessing global vegetation dynamics in the context of global change.

AVHRR
AVHRR
TS

Creative Commons Attribution 4.0 International

DOWNLOADS Raster Image


DESCRIPTION:

It provides information on land surface reflectance at a 250-meter spatial resolution. The data are composited over an 8-day period, meaning that for each pixel, the reflectance value represents the best observation obtained within that timeframe. A time series has been generated using the NDVI index at 250 meters every 8 days across the entire Iberian Peninsula.
Savizky Golay filter has been applied, in a process known as convolution, by fitting successive sub-sets of adjacent data points with a low-degree polynomial by the method of linear least squares.

MOD
09Q1
NDVI 250
TS 2000 - 2012
FILTERED
TS 2000 - 2012

Creative Commons Attribution 4.0 International

DOWNLOADS Raster Image


DESCRIPTION:

It provides information on land surface reflectance at a 500-meter spatial resolution. The data are composited over an 8-day period, meaning that for each pixel, the reflectance value represents the best observation obtained within that timeframe. A time series has been generated using the NDVI index at 500 meters every 8 days across the entire Iberian Peninsula.
Savizky Golay filter has been applied, in a process known as convolution, by fitting successive sub-sets of adjacent data points with a low-degree polynomial by the method of linear least squares.

MOD
09A1
FILTERED
TS 2000 - 2022

Creative Commons Attribution 4.0 International

DOWNLOADS Raster Image


DESCRIPTION:

Vegetation seasonality assessment through remote sensing data is crucial to understand ecosystem responses to climatic variations and human activities at large-scales. Whereas the study of the timing of phenological events showed significant advances, their recurrence patterns at different periodicities has not been widely study, especially at global scale. In this work, we describe vegetation oscillations by a novel quantitative approach based on the spectral analysis of Normalized Difference Vegetation Index (NDVI) time series. A new set of global periodicity indicators permitted to identify different seasonal patterns regarding the intra-annual cycles (the number, amplitude, and stability) and to evaluate the existence of pluri-annual cycles, even in those regions with noisy or low NDVI. The indicator maps compiled in this work provide highly relevant and practical information to advance in assessing global vegetation dynamics in the context of global change.

MOD
09A1
NDVI 500
TS 2000 - 2023
FILTERED
TS

Creative Commons Attribution 4.0 International