|KIMM, HYUNGSUK - University Of Illinois|
|GUAN, KAIYU - University Of Illinois|
|JIANG, CHONGYA - University Of Illinois|
|PENG, BIN - University Of Illinois|
|GENTRY, LAURA - University Of Illinois|
|WILKIN, SCOTT - University Of Illinois|
|WANG, SIBO - University Of Illinois|
|CAI, YAPING - University Of Illinois|
|PENG, JIAN - University Of Illinois|
|LUO, YUNAN - University Of Illinois|
Submitted to: Remote Sensing of Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/19/2019
Publication Date: 1/2/2020
Citation: Kimm, H., Guan, K., Jiang, C., Peng, B., Gentry, L.F., Wilkin, S.C., Wang, S., Cai, Y., Bernacchi, C.J., Peng, J., Luo, Y. 2020. Deriving high-spatiotemporal-resolution leaf area index for agroecosystems in the U.S. Corn Belt using Planet Labs CubeSat and STAIR fusion data. Remote Sensing of Environment. 239:111615. https://doi.org/10.1016/j.rse.2019.111615.
Interpretive Summary: Leaf Area Index is a measure of the total area of leaves per ground area. This is a very important measurements as it is linked with productivity with a higher LAI usually meaning better crop growth. Despite it being very important it is very hard to measured, especially at the scale of agriculture representing whole regions of the United States, for example the Midwestern Corn-Belt. In this experiment, two different satellite data products were combined and tested against a very thorough ground-based experiment in Central Illinois. The new technique outlined in this study is much improved from traditional techniques and can be applied to entire agricultural regions to improve the ability to measure remotely LAI.
Technical 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 500m 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.