A dataset of spatiotemporally sampled MODIS Leaf Area Index with corresponding Landsat surface reflectance over the contiguous US

Project Title:  View help for Project Title A dataset of spatiotemporally sampled MODIS Leaf Area Index with corresponding Landsat surface reflectance over the contiguous US

Summary:  View help for Summary Leaf Area Index (LAI) is a fundamental vegetation structural variable that drives energy and mass exchanges between the plant and the atmosphere. Moderate-resolution (300m – 7km) global LAI data products have been widely applied to track global vegetation changes, drive Earth system models, monitor crop growth and productivity, etc. Yet, cutting-edge applications in climate adaptation, hydrology, and sustainable agriculture require LAI information at higher spatial resolution (< 100m) to model and understand heterogeneous landscapes.

This dataset was built to assist a machine-learning-based approach for mapping LAI from 30m-resolution Landsat images across the contiguous US (CONUS). The data was derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) Version 6 LAI/FPAR, Landsat Collection 1 surface reflectance, and NLCD Land Cover datasets over 2006 – 2018 using Google Earth Engine. Each record/sample/row includes a MODIS LAI value, corresponding Landsat surface reflectance in green, red, NIR, SWIR1 bands, a land cover (biome) type, geographic location, and other auxiliary information. Each sample represents a MODIS LAI pixel (500m) within which a single biome type dominates 90% of the area. The spatial homogeneity of the samples was further controlled by a screening process based on the coefficient of variation of the Landsat surface reflectance. In total, there are approximately 1.6 million samples, stratified by biome, Landsat sensor, and saturation status from the MODIS LAI algorithm. This dataset can be used to train machine learning models and generate LAI maps for Landsat 5, 7, 8 surface reflectance images within CONUS. Detailed information on the sample generation and quality control can be found in the related journal article.


Resources in this dataset:

Resource Title: README. File Name: LAI_train_samples_CONUS_README.txtResource Description: Description and metadata of the main datasetResource Software Recommended: Notepad,url: https://www.microsoft.com/en-us/p/windows-notepad/9msmlrh6lzf3?activetab=pivot:overviewtab

Resource Title: README.

File Name: LAI_train_samples_CONUS_README.txt

Resource Description: Description and metadata of the main dataset

Resource Software Recommended: Notepad,url: https://www.microsoft.com/en-us/p/windows-notepad/9msmlrh6lzf3?activetab=pivot:overviewtab

Resource Title: LAI_training_samples_CONUS. File Name: LAI_train_samples_CONUS_v0.1.1.csvResource Description: This CSV file consists of the training samples for estimating Leaf Area Index based on Landsat surface reflectance images (Collection 1 Tire 1). Each sample has a MODIS LAI value and corresponding surface reflectance derived from Landsat pixels within the MODIS pixel. Contact: Yanghui Kang ([email protected])

Column description

• UID: Unique identifier. Format: LATITUDE_LONGITUDE_SENSOR_PATHROW_DATE
• Landsat_ID: Landsat image ID
• Date: Landsat image date in "YYYYMMDD"
• Latitude: Latitude (WGS84) of the MODIS LAI pixel center
• Longitude: Longitude (WGS84) of the MODIS LAI pixel center
• MODIS_LAI: MODIS LAI value in "m2/m2"
• MODIS_LAI_std: MODIS LAI standard deviation in "m2/m2"
• MODIS_LAI_sat: 0 - MODIS Main (RT) method used no saturation; 1 - MODIS Main (RT) method with saturation
• NLCD_class: Majority class code from the National Land Cover Dataset (NLCD)
• NLCD_frequency: Percentage of the area cover by the majority class from NLCD
• Biome: Biome type code mapped from NLCD (see below for more information)
• Blue: Landsat surface reflectance in the blue band
• Green: Landsat surface reflectance in the green band
• Red: Landsat surface reflectance in the red band
• Nir: Landsat surface reflectance in the near infrared band
• Swir1: Landsat surface reflectance in the shortwave infrared 1 band
• Swir2: Landsat surface reflectance in the shortwave infrared 2 band
• Sun_zenith: Solar zenith angle from the Landsat image metadata. This is a scene-level value.
• Sun_azimuth: Solar azimuth angle from the Landsat image metadata. This is a scene-level value.
• NDVI: Normalized Difference Vegetation Index computed from Landsat surface reflectance
• EVI: Enhanced Vegetation Index computed from Landsat surface reflectance
• NDWI: Normalized Difference Water Index computed from Landsat surface reflectance
• GCI: Green Chlorophyll Index = Nir/Green - 1

Biome code

• 1 - Deciduous Forest
• 2 - Evergreen Forest
• 3 - Mixed Forest
• 4 - Shrubland
• 5 - Grassland/Pasture
• 6 - Cropland
• 7 - Woody Wetland
• 8 - Herbaceous Wetland

Reference Dataset:

All data was accessed through Google Earth Engine
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment. MODIS Version 6 Leaf Area Index/FPAR 4-day L5 Global 500m Myneni, R., Y. Knyazikhin, T. Park. MOD15A2H MODIS/Terra Leaf Area Index/FPAR 8-Day L4 Global 500m SIN Grid V006. 2015, distributed by NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/MODIS/MOD15A2H.006 Landsat 5/7/8 Collection 1 Surface Reflectance
Landsat Level-2 Surface Reflectance Science Product courtesy of the U.S. Geological Survey.
Masek, J.G., Vermote, E.F., Saleous N.E., Wolfe, R., Hall, F.G., Huemmrich, K.F., Gao, F., Kutler, J., and Lim, T-K. (2006). A Landsat surface reflectance dataset for North America, 1990–2000. IEEE Geoscience and Remote Sensing Letters 3(1):68-72. http://dx.doi.org/10.1109/LGRS.2005.857030.
Vermote, E., Justice, C., Claverie, M., & Franch, B. (2016). Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sensing of Environment. http://dx.doi.org/10.1016/j.rse.2016.04.008. National Land Cover Dataset (NLCD)
Yang, Limin, Jin, Suming, Danielson, Patrick, Homer, Collin G., Gass, L., Bender, S.M., Case, Adam, Costello, C., Dewitz, Jon A., Fry, Joyce A., Funk, M., Granneman, Brian J., Liknes, G.C., Rigge, Matthew B., Xian, George, A new generation of the United States National Land Cover Database—Requirements, research priorities, design, and implementation strategies: ISPRS Journal of Photogrammetry and Remote Sensing, v. 146, p. 108–123, at https://doi.org/10.1016/j.isprsjprs.2018.09.006 Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel

Resource Title: LAI_training_samples_CONUS.

File Name: LAI_train_samples_CONUS_v0.1.1.csv

Resource Description: This CSV file consists of the training samples for estimating Leaf Area Index based on Landsat surface reflectance images (Collection 1 Tire 1). Each sample has a MODIS LAI value and corresponding surface reflectance derived from Landsat pixels within the MODIS pixel. Contact: Yanghui Kang ([email protected])

Column description

UID: Unique identifier. Format: LATITUDE_LONGITUDE_SENSOR_PATHROW_DATE

Landsat_ID: Landsat image ID

Date: Landsat image date in "YYYYMMDD"

Latitude: Latitude (WGS84) of the MODIS LAI pixel center

Longitude: Longitude (WGS84) of the MODIS LAI pixel center

MODIS_LAI: MODIS LAI value in "m2/m2"

MODIS_LAI_std: MODIS LAI standard deviation in "m2/m2"

MODIS_LAI_sat: 0 - MODIS Main (RT) method used no saturation; 1 - MODIS Main (RT) method with saturation

NLCD_class: Majority class code from the National Land Cover Dataset (NLCD)

NLCD_frequency: Percentage of the area cover by the majority class from NLCD

Biome: Biome type code mapped from NLCD (see below for more information)

Blue: Landsat surface reflectance in the blue band

Green: Landsat surface reflectance in the green band

Red: Landsat surface reflectance in the red band

Nir: Landsat surface reflectance in the near infrared band

Swir1: Landsat surface reflectance in the shortwave infrared 1 band

Swir2: Landsat surface reflectance in the shortwave infrared 2 band

Sun_zenith: Solar zenith angle from the Landsat image metadata. This is a scene-level value.

Sun_azimuth: Solar azimuth angle from the Landsat image metadata. This is a scene-level value.

NDVI: Normalized Difference Vegetation Index computed from Landsat surface reflectance

EVI: Enhanced Vegetation Index computed from Landsat surface reflectance

NDWI: Normalized Difference Water Index computed from Landsat surface reflectance

GCI: Green Chlorophyll Index = Nir/Green - 1

1 - Deciduous Forest

2 - Evergreen Forest

3 - Mixed Forest

4 - Shrubland

5 - Grassland/Pasture

6 - Cropland

7 - Woody Wetland

8 - Herbaceous Wetland

Reference Dataset:

All data was accessed through Google Earth Engine
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment.

MODIS Version 6 Leaf Area Index/FPAR 4-day L5 Global 500m Myneni, R., Y. Knyazikhin, T. Park. MOD15A2H MODIS/Terra Leaf Area Index/FPAR 8-Day L4 Global 500m SIN Grid V006. 2015, distributed by NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/MODIS/MOD15A2H.006

Landsat 5/7/8 Collection 1 Surface Reflectance
Landsat Level-2 Surface Reflectance Science Product courtesy of the U.S. Geological Survey.
Masek, J.G., Vermote, E.F., Saleous N.E., Wolfe, R., Hall, F.G., Huemmrich, K.F., Gao, F., Kutler, J., and Lim, T-K. (2006). A Landsat surface reflectance dataset for North America, 1990–2000. IEEE Geoscience and Remote Sensing Letters 3(1):68-72. http://dx.doi.org/10.1109/LGRS.2005.857030.
Vermote, E., Justice, C., Claverie, M., & Franch, B. (2016). Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sensing of Environment. http://dx.doi.org/10.1016/j.rse.2016.04.008.

National Land Cover Dataset (NLCD)
Yang, Limin, Jin, Suming, Danielson, Patrick, Homer, Collin G., Gass, L., Bender, S.M., Case, Adam, Costello, C., Dewitz, Jon A., Fry, Joyce A., Funk, M., Granneman, Brian J., Liknes, G.C., Rigge, Matthew B., Xian, George, A new generation of the United States National Land Cover Database—Requirements, research priorities, design, and implementation strategies: ISPRS Journal of Photogrammetry and Remote Sensing, v. 146, p. 108–123, at https://doi.org/10.1016/j.isprsjprs.2018.09.006

Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel

Original Distribution URL:  View help for Original Distribution URL https://agdatacommons.nal.usda.gov/articles/dataset/A_dataset_of_spatiotemporally_sampled_MODIS_Leaf_Area_Index_with_corresponding_Landsat_surface_reflectance_over_the_contiguous_US/24666042

Subject Terms:  View help for Subject Terms leaf area index; landsat; MODIS; machine learning; Conus

Geographic Coverage:  View help for Geographic Coverage United States

Time Period(s):  View help for Time Period(s) 1/1/2006 – 12/31/2018

Data Type(s):  View help for Data Type(s) observational data; program source code

Collection Notes:  View help for Collection Notes DOI: 10.15482/USDA.ADC/1521097 Citation: Kang, Yanghui; Ozdogan, Mutlu; Gao, Feng; Anderson, Martha C.; White, William A.; Yang, Yun; Yang, Yang; Erickson, Tyler A. (2021). A dataset of spatiotemporally sampled MODIS Leaf Area Index with corresponding Landsat surface reflectance over the contiguous US. Ag Data Commons. https://doi.org/10.15482/USDA.ADC/1521097 (Downloaded 2026-06-26)