@article{2cd462d16b624b0d8a6904af0b52b08c,
title = "Deriving high-spatiotemporal-resolution leaf area index for agroecosystems in the U.S. Corn Belt using Planet Labs CubeSat and STAIR fusion data",
abstract = "Leaf area index (LAI) is a key variable for characterizing crop growth conditions and estimating crop productivity. Despite continuing efforts to develop LAI estimation algorithms, LAI datasets still need improvement at spatial and temporal resolutions to meet the requirements of agricultural applications. Advancements in data fusion technique and the emergence of new satellite data provide opportunities for LAI data at higher resolutions in both space and time. In this study, we derived new LAI estimations by leveraging novel satellite remote sensing datasets, STAIR fusion (MODIS-Landsat fusion) and Planet Labs' CubeSat data (through a reprocessed pipeline) for a typical agricultural landscape in the U.S. Corn Belt. The STAIR fused data and our reprocessed CubeSat data have both fine spatial resolutions (30 m and 3.125 m, respectively) and high frequencies (daily for both). To reliably estimate LAI from these advanced satellite datasets, we used two methods: inversion of a radiative transfer model (RTM), and empirical relationship with vegetation index (VI) calibrated from field measured LAI. Compared to the ground-truth LAI collected at 36 sites across the study region, reliable approximations were achieved by both LAI estimations based on PROSAIL RTM (STAIR: R2 = 0.69 and root mean squared error (RMSE) = 1.12 (m2 m− 2), CubeSat: R2 = 0.76 and RMSE = 1.09 (m2 m− 2)), and LAI estimations based on Green Wide Dynamic Range Vegetation Index (GrWDRVI) (STAIR: R2 = 0.75, RMSE = 1.10 (m2 m− 2), CubeSat: R2 = 0.76, RMSE = 1.08 (m2 m− 2), where validation ground-truth is independent from calibration data). Newly estimated high-resolution LAI data were aggregated at 500 m resolution and compared with MODIS and VIIRS LAI products, revealing substantial uncertainties and biases in these two products. We also demonstrated phenology stage estimation at fine spatial resolutions based on our high-frequency LAI data. The proposed LAI estimation methods at both high spatial resolution and temporal frequency can be applied to the entire U.S. Corn Belt and provide significant advancement to crop monitoring and precision agriculture.",
keywords = "Camera network, CubeSat, Leaf area index, PROSAIL, Planet Labs, STAIR fusion, Vegetation index",
author = "Hyungsuk Kimm and Kaiyu Guan and Chongya Jiang and Bin Peng and Gentry, {Laura F.} and Wilkin, {Scott C.} and Sibo Wang and Yaping Cai and Bernacchi, {Carl J.} and Jian Peng and Yunan Luo",
note = "Funding Information: Guan, Kimm, and Peng acknowledge the support from NASA New Investigator Award (NNX1 6AI56G) and NASA Carbon Monitoring System (80NSSC18K0170), managed by the NASA Terrestrial Ecology Program and Blue Waters Professorship from National Center for Supercomputing Applications of University of Illinois at Urbana-Champaign (UIUC). Guan and Kimm also acknowledges a fellowship from USGS Illinois Water Resource Center. Guan and Gentry acknowledge the support from NASA Harvest Program managed by University of Maryland. Jiang, Peng, and Bernacchi acknowledge the support from DOE Center for Advanced Bioenergy and Bioproducts Innovation. The authors thank the following farmer friends who generously provided their cropland for the camera data collection: Chris Hausman, Dirk Rice, Jeremy Wolf, Paul Compton, Steve Moser, and Steve Stierwalt. The authors also acknowledge the destructive LAI data from Dr. Andrew Suyker and his team. Finally, the authors acknowledge Joseph Mascaro from Planet Labs for his guidance on using the Planet Labs' CubeSat data. Funding Information: Guan, Kimm, and Peng acknowledge the support from NASA New Investigator Award ( NNX1 6AI56G ) and NASA Carbon Monitoring System ( 80NSSC18K0170 ), managed by the NASA Terrestrial Ecology Program and Blue Waters Professorship from National Center for Supercomputing Applications of University of Illinois at Urbana-Champaign (UIUC) . Guan and Kimm also acknowledges a fellowship from USGS Illinois Water Resource Center. Guan and Gentry acknowledge the support from NASA Harvest Program managed by University of Maryland . Jiang, Peng, and Bernacchi acknowledge the support from DOE Center for Advanced Bioenergy and Bioproducts Innovation. The authors thank the following farmer friends who generously provided their cropland for the camera data collection: Chris Hausman, Dirk Rice, Jeremy Wolf, Paul Compton, Steve Moser, and Steve Stierwalt. The authors also acknowledge the destructive LAI data from Dr. Andrew Suyker and his team. Finally, the authors acknowledge Joseph Mascaro from Planet Labs for his guidance on using the Planet Labs' CubeSat data. Appendix A Publisher Copyright: {\textcopyright} 2019 Elsevier Inc.",
year = "2020",
month = mar,
day = "15",
doi = "10.1016/j.rse.2019.111615",
language = "English (US)",
volume = "239",
journal = "Remote Sensing of Environment",
issn = "0034-4257",
publisher = "Elsevier Inc.",
}