Abstract
We proposed a direct approach to validate hectometric and kilometric resolution leaf area index (LAI) products that involved the scaling up of field-measured LAI via the validation and recalibration of the decametric Sentinel-2 LAI product. We applied it over a test study area of maize crops in northern China using continuous field measurements of LAINet along the year 2019. Sentinel-2 LAI showed an overall accuracy of 0.67 in terms of Root Mean Square Error (RMSE) and it was used, after recalibration, as a benchmark to validate six coarse resolution LAI products: MODIS, Copernicus Global Land Service 1 km Version 2 (called GEOV2) and 300 m (GEOV3), Satellite Application Facility EUMETSAT Polar System (SAF EPS) 1.1 km, Global LAnd Surface Satellite (GLASS) 500 m and Copernicus Climate Change Service (C3S) 1 km V2. GEOV2, GEOV3 and MODIS showed a good agreement with reference LAI in terms of magnitude (RMSE ≤ 0.29) and phenology. SAF EPS (RMSE = 0.68) and C3S V2 (RMSE = 0.41), on the opposite, systematically underestimated high LAI values and showed systematic differences for phenological metrics: a delay of 6 days (d), 20 d and 24 d for the start, peak and the end of growing season, respectively, for SAF EPS and an advance of −4 d, −6 d and −6 d for C3S.
Highlights
Leaf area index (LAI), defined as half the total leaf area per unit of ground surface area [1], is a critical variable in land surface processes such as photosynthesis, respiration, and precipitation interception [2]
The validation approach has three main steps (Figure 2): (1) Validation of the SL2Pbased Sentinel-2 LAI product and generation of reference LAI maps after recalibration with field measurements; (2) Validation of the hectometric and kilometric MODIS, GEOV2, GEOV3, EPS and Global LAnd Surface Satellite (GLASS) and C3S LAI products; and (3) Validation of the phenological metrics derived from time series of LAI
The LAI grows rapidly in the growing period from approximately 0.5 on day of year (DOY) 152 to 4–5 at the peak of the growing period occurring on DOY 211
Summary
Leaf area index (LAI), defined as half the total leaf area per unit of ground surface area [1], is a critical variable in land surface processes such as photosynthesis, respiration, and precipitation interception [2]. The Global Climate Observing System (GCOS) identified. LAI as one of the essential climate variables accessible from remote sensing observations [3]. A broad variety of LAI retrieval methods has been proposed and, as described in the literature, they can be grouped in four categories [4]: parametric regression, nonparametric regression, physically based and hybrid methods. Regression methods define statistical relationships between optical remote sensing observations and LAI. A wide variety of statistical approaches mainly based on vegetation indices have been proposed in the literature (e.g., [5]). The physical methods are based on the inversion of canopy radiative transfer models. The look up tables (LUTs) are Remote Sens.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
