Assessing PROSPECT performance on aquatic plant leaves

Villa, Paolo; Dalla Vecchia, Alice; Piaser, Erika; Bolpagni, Rossano

doi:10.1016/j.rse.2023.113926

Cited by 1 publication

(1 citation statement)

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“…This method is characterized by its robust universality, as it is not constrained by environmental conditions or crop types. However, it should be noted that the radiative transfer processes in natural settings are influenced by various factors, including the characteristics of the canopy architecture, and are contingent upon several assumed parameters for simulation [30]. Empirical model methods, on the other hand, are predominantly founded upon establishing regression relationships between observed vegetation parameters and corresponding vegetation indices.…”

Section: Introductionmentioning

confidence: 99%

An Angle Effect Correction Method for High-Resolution Satellite Side-View Imaging Data to Improve Crop Monitoring Accuracy

Gong,

Zhong,

Zhu

et al. 2024

Remote Sensing

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In recent years, the advancement of CubeSat technology has led to the emergence of high-resolution, flexible imaging satellites as a pivotal source of information for the efficient and precise monitoring of crops. However, the dynamic geometry inherent in flexible side-view imaging poses challenges in acquiring the high-precision reflectance data necessary to accurately retrieve crop parameters. This study aimed to develop an angular correction method designed to generate nadir reflectance from high-resolution satellite side-swing imaging data. The method utilized the Anisotropic Flat Index (AFX) in conjunction with a fixed set of Bidirectional Reflectance Distribution Function (BRDF) parameters to compute the nadir reflectance for the Jilin-1 GP01/02 multispectral imager (PMS). Crop parameter retrieval was executed using regression models based on vegetation indices, the leaf area index (LAI), fractional vegetation cover (FVC), and chlorophyll (T850 nm/T720 nm) values estimated based on angle corrected reflectance compared with field measurements taken in the Inner Mongolia Autonomous Region. The findings demonstrate that the proposed angular correction method significantly enhances the retrieval accuracy of the LAI, FVC, and chlorophyll from Jilin-1 GP01/02 PMS data. Notably, the retrieval accuracy for the LAI and FVC improved by over 25%. We expect that this approach will exhibit considerable potential to improve crop monitoring accuracy from high-resolution satellite side-view imaging data.

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Section: Introductionmentioning

confidence: 99%