Ag Data Commons

File(s) stored somewhere else

Please note: Linked content is NOT stored on Ag Data Commons and we can't guarantee its availability, quality, security or accept any liability.

Data From: Assessing variability of corn and soybean yields in central Iowa using high spatiotemporal resolution multi-satellite imagery

posted on 2024-02-15, 19:26 authored by Feng GaoFeng Gao, Martha C. Anderson, Craig S. T. Daughtry, David Mark S. Johnson

This dataset includes daily two-band Enhanced Vegetation Index (EVI2) at 30-m resolution over a Landsat scene (path 26 and row 31) in central Iowa. Fourteen years of daily EVI2 from 2001 to 2015 (except 2012) were generated through fusing and interpolating Landsat-MODIS data.

Landsat surface reflectances were order and used in this study. Mostly clear Landsat images from each year were chosen to pair with MODIS images acquired from the same day to generate daily Landsat-MODIS surface reflectance using the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). Partially clear Landsat images were also used in generating the smoothed and gap-filled daily VI time-series. All available Landsat data including Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI) were used in this study.

The MODIS data products were downloaded and processed. These include the daily surface reflectance at both 250m (MOD09GQ) and 500m (MOD09GA) resolution, the MODIS Bidirectional Reflectance Distribution Function (BRDF) parameters at 500m resolution, and the MODIS land cover types at 500m resolution (MCD12Q1). They were used to generated daily nadir BRDF-adjusted reflectance (NBAR) at 250m resolution for fusing with Landsat.

The Landsat-MODIS data fusion results for 2001-2014 were generated from a previous study (Gao et al, 2017; doi: 10.1016/j.rse.2016.11.004). Data fusion results for 2015 were generated using Landsat 8 OLI images from day 194, 226, 258 and 338 in this study. Cloud masks were extracted from Landsat and MODIS QA layers and were used to exclude cloud, cloud shadow and snow pixels. Since Landsat 5 TM operational imaging ended in November 2011 and Landsat 8 OLI has not been launched until February 2013, Landsat 7 ETM+ Scan Line Corrector (SLC)-off images are the only available Landsat data. For this reason, 2012 was not included.

Due to the cloud contamination in the Landsat and MODIS images, the fused Landsat-MODIS results still have invalid values or gaps. To fill these gaps, a modified Savitzky-Golay (SG) filter approach was built and applied to smooth and gap-fill EVI2. The SG filter is a moving fitting approach. Each point is smoothed using the value computed from the polynomial function fit to the observations within the moving window. The program removes spike points if the fitting errors are larger than the predefined threshold (default 3 standard deviation). The modified SG filter allows us to retain small variations but also fill large gaps in an unevenly distributed time-series EVI2.

Daily EVI2 files are saved in one tar file per year. Each tar file contains a binary image file and a text header file that can be displayed in the ENVI software. The binary image file has the dimension of 7201 lines by 8061 samples by 365 days and is saved in BIP (band interleaved by pixel) format. EVI2 data are saved in 4-byte float number. The text header file contains necessary information including projection and geolocation. Daily EVI2 file is named as "flexfit_evi2.026031.yyyy.bin", where "026031" refers to the Landsat path and row, and yyyy represents year and ranges from 2001-2015.

Resources in this dataset:

  • Resource Title: Daily EVI2 Data Packages .

    File Name: Web Page, url:

    These Daily EVI2 data packages are grouped by year. Each package includes a plain binary file that saves daily EVI2, and a ENVI header file (in text) that contains metadata and geolocation information. Contents are as follows: dailyVI.026031.2000.tar.gz dailyVI.026031.2001.tar.gz dailyVI.026031.2002.tar.gz dailyVI.026031.2003.tar.gz dailyVI.026031.2004.tar.gz dailyVI.026031.2005.tar.gz dailyVI.026031.2006.tar.gz dailyVI.026031.2007.tar.gz dailyVI.026031.2008.tar.gz dailyVI.026031.2009.tar.gz dailyVI.026031.2010.tar.gz dailyVI.026031.2011.tar.gz dailyVI.026031.2013.tar.gz dailyVI.026031.2014.tar.gz dailyVI.026031.2015.tar.gz SCINet users: The .tar.gz files can be accessed/retrieved with valid SCINet account at this location: /KEEP/ADCdatastorage/NAL/published/node22870/ See the SCINet File Transfer guide for more information on moving large files: Globus users: The files can also be accessed through Globus by following this data link.The user will need to log in to Globus in order to retrieve this data. User accounts are free of charge with several options for signing on. Instructions for creating an account are on the login page.



National Aeronautics and Space Administration

U.S. Geological Survey


Data contact name

Gao, Feng

Data contact email


Ag Data Commons

Intended use

used for mapping crop phenology and yield

Use limitations

Dataset only covers limited area and years. Data quality varies on year depending on the availability of the cloud-free satellite observations

Temporal Extent Start Date


Temporal Extent End Date



  • Not specified

Geographic Coverage


Geographic location - description

Central Iowa

ISO Topic Category

  • imageryBaseMapsEarthCover

National Agricultural Library Thesaurus terms

corn; crop yield; Iowa; remote sensing; data collection; vegetation index; Landsat; moderate resolution imaging spectroradiometer; reflectance; models; time series analysis; thematic maps; vegetation types; snow; image analysis; statistical analysis; soybeans

OMB Bureau Code

  • 005:18 - Agricultural Research Service

OMB Program Code

  • 005:040 - National Research

Pending citation

  • No

Public Access Level

  • Public

Preferred dataset citation

Gao, Feng; Anderson, Martha C.; Daughtry, Craig S. T.; Johnson, David Mark S. (2019). Data From: Assessing variability of corn and soybean yields in central Iowa using high spatiotemporal resolution multi-satellite imagery. Ag Data Commons.