Validation of Sentinel-2, MODIS, CGLS, SAF, GLASS and C3S Leaf Area Index Products in Maize Crops

Yu, Huinan; Yin, Gaofei; Liu, Guoxiang; Ye, Yuanxin; Qu, Yonghua; Xu, Baodong; Verger, Aleixandre

doi:10.3390/rs13224529

Cited by 7 publications

(1 citation statement)

References 60 publications

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“…At the satellite level, the parametric method proved to be effective even when compared against Sentinel-2 Biophysical Processor LAI estimations, which are often used due to their independence from specific information regarding the field conditions and crop types (Levitan et al, 2019). The comparison between the ground-measured LAI and parametric Sentinel-2 and UAV LAI estimations showed clear underestimations in the Sentinel-2 LAI MLA -generated product, as was also pointed out by Djamai et al (2019) for a variety of crops and by Xie et al (2019) for winter wheat; however, this result contrasted with that reported by (Yu et al, 2021) for maize, where the Sentinel-2 LAI MLA product resulted in fairly good estimations. Nonetheless, the MLA method was able to retrieve relative LAI reductions between undamaged and damaged vegetation consistently with those found through the parametric estimation.…”

Section: Discussionmentioning

confidence: 73%

LAI estimation through remotely sensed NDVI following hail defoliation in maize (Zea mays L.) using Sentinel-2 and UAV imagery

et al. 2023

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Extreme events such as hailstorms are a cause for concern in agriculture, leading to both economic and food supply losses. Traditional damage estimation techniques have recently been called into question since damages have rarely been quantified at the large-field scale. Damage-estimation methods used by field inspectors are complex and sometimes subjective and hardly account for damage spatial variability. In this work, a normalized difference vegetation index (NDVI)-based parametric method was applied using both unmanned aerial vehicles (UAV) and Sentinel-2 sensors to estimate the leaf area index (LAI) of maize (Zea mays L.) resulting from simulated hail damage. These methods were then compared to the LAI values generated from the Sentinel-2 Biophysical Processor. A two-year experiment (2020–2021) was conducted during the maize cropping season, with hail events simulated during a range of maize developmental stages (the 8th-leaf, flowering, milky and dough stages) using a 0–40% defoliation gradient of damage intensities performed with the aid of specifically designed prototype machines. The results showed that both sensors were able to accurately estimate LAI in a nonstandard damaged canopy while requiring only the calibration of the extinction coefficient $$k(\vartheta )$$ k ( ϑ ) in the case of parametric estimations. In this case, the calibration was performed using 2020 data, providing $$k(\vartheta )$$ k ( ϑ ) values of 0.59 for Sentinel-2 and 0.37 for the UAV sensor. The validation was performed on 2021 data, and showed that the UAV sensor had the best accuracy (R2 of 0.86, root-mean-square error (RMSE) of 0.71). The $$k(\vartheta )$$ k ( ϑ ) value proved to be sensor-specific, accounting for the NDVI retrieval differences likely caused by the different spatial operational scales of the two sensors. NDVI proved effective in parametrically estimating maize LAI under damaged canopy conditions at different defoliation degrees. The parametric method matched the Sentinel-2 biophysical process-generated LAI well, leading to less underestimations and more accurate LAI retrieval.

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