Year-end Sale % OFF 
Abstract
ABSTRACT This study validated SNAP-derived LAI from Sentinel-2 and its consistency with existing global LAI products. The validation and inter-comparison experiments were performed on two processing levels, i.e., Top-of-Atmosphere and Bottom-of-Atmosphere reflectances and two spatial resolutions, i.e., 10 m, and 20 m. These were chosen to determine their effect on retrieved LAI accuracy and consistency. The results showed moderate R 2, i.e., ~0.6 to ~0.7 between SNAP-derived LAI and in-situ LAI, but with high errors, i.e., RMSE, BIAS, and MAE >2 m2 m–2 with marked differences between processing levels and insignificant differences between spatial resolutions. In contrast, inter-comparison of SNAP-derived LAI with MODIS and Proba-V LAI products revealed moderate to high consistencies, i.e., R 2 of ~0.55 and ~0.8 respectively, and RMSE of ~0.5 m2 m–2 and ~0.6 m2 m–2, respectively. The results in this study have implications for future use of SNAP-derived LAI from Sentinel-2 in agricultural landscapes, suggesting its global applicability that is essential for large-scale agricultural monitoring. However, enormous errors in characterizing field-level LAI variability indicate that SNAP-derived LAI is not suitable for precision farming. In fact, from the study, the need for further improvement of LAI retrieval arises, especially to support farm-level agricultural management decisions.
Highlights
Improving remotely sensed characterization of biophysical properties of vegetation is of paramount importance for a variety of applications (Davi et al 2009; Zhu et al 2010)
The results show that leaf area index (LAI) derived from TOA reflectances had marginally better agreement with observed LAI, i.e., R2 = 0.69 than that derived from BOA reflectances that achieved R2 ~ 0.68 across all spatial resolutions
Sentinel Application Platform (SNAP)-derived LAI correlates moderately well in agricultural landscapes when compared with in-situ LAI data; the errors are considerably high for field-level agricultural management
Summary
Improving remotely sensed characterization of biophysical properties of vegetation is of paramount importance for a variety of applications (Davi et al 2009; Zhu et al 2010). LAI is defined as the one-sided green leaf area per unit ground area (Myneni 2012), and it is regarded as an essential climate variable (ECV) (GCOS 2009). This is mainly due to its significance in characterizing basic information related to vegetation growth and productivity such as foliage density, plant health, and functioning, as. LAI is essential for, among others, monitoring the variability in crops and rangelands productivity, crop stress and health, biomass, phenology, and yield estimation (Mulla 2013; Cho, Ramoelo, and Dziba 2017; Novelli et al 2019). Remotely sensed effective LAI (hereafter, LAI) provides a promising alternative for operational agricultural monitoring to support the implementation of global and regional food security mandates such as the United Nations Sustainable Development Goals (UN-SDGs) and Agenda 2063
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.
