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

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.

• 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.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 (kangyanghui@gmail.com)

  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