Deriving high-spatiotemporal-resolution leaf area index for agroecosystems in the U.S. Corn Belt using Planet Labs CubeSat and STAIR fusion data

• Published in: Remote sensing of environment 2020-03, Vol.239 (C), p.111615, Article 111615
• Main Authors: Kimm, Hyungsuk, Guan, Kaiyu, Jiang, Chongya, Peng, Bin, Gentry, Laura F., Wilkin, Scott C., Wang, Sibo, Cai, Yaping, Bernacchi, Carl J., Peng, Jian, Luo, Yunan
• Format: Article
• Language: English
• Subjects: Agricultural ecosystems; Agricultural land; Agriculture; Algorithms; Calibration; Camera network; Corn; Corn belt; Crop growth; Crop production; Crops; CubeSat; Data integration; Datasets; Frequency dependence; Growth conditions; High resolution; Land use; Landsat; Landsat satellites; Leaf area; Leaf area index; MODIS; Planet Labs; Planets; Precision farming; PROSAIL; Radiative transfer; Remote sensing; Root-mean-square errors; Satellite data; Satellites; Spatial discrimination; Spatial resolution; STAIR fusion; Vegetation; Vegetation index
• ISSN: 0034-4257; 1879-0704
• DOI: 10.1016/j.rse.2019.111615

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. •High-spatiotemporal-resolution LAI is achieved from MODIS-Landsat Fusion and CubeSat.•Both empirical and process-based LAI estimations achieve high performance.•The MODIS and VIIRS LAI products show large bias for cropland.