Xianfeng Zhou^1,2,*, Ruiju Sun¹, Zhaojie Zhang¹, Yuanyuan Song¹, Lijiao Jin¹, Lin Yuan³

Phyton-International Journal of Experimental Botany, Vol.94, No.2, pp. 503-519, 2025, DOI:10.32604/phyton.2025.060827 - 06 March 2025

Abstract An accurate and robust estimation of leaf chlorophyll content (LCC) is very important to better know the process of material and energy exchange between plants and the environment. Compared with traditional remote sensing methods, abundant research has made progress in agronomic parameter retrieval using different CNN frameworks. Nevertheless, limited reports have paid attention to the problems, i.e., limited measured data, hyperspectral redundancy, and model convergence issues, when concerning CNN models for parameter estimation. Therefore, the present study tried to analyze the effects of synthetic data size expansion employing a Gaussian process regression (GPR) model for… More >

Nur Nabihah Ghazali¹, Noraain Mohamed Saraf^1,*, Abdul Rauf Abdul Rasam^1,*, Ainon Nisa Othman¹, Siti Aekbal Salleh¹, Nurhafiza Md Saad²

Revue Internationale de Géomatique, Vol.34, pp. 89-101, 2025, DOI:10.32604/rig.2025.057562 - 24 February 2025

Abstract Forest fires are contributing significantly to the acceleration of deforestation. Monitoring and mapping these fires are crucial, and remote sensing technology has proven effective for this purpose. This research employs remote sensing methods to evaluate the severity of a forest fire in Kampung Balai Besar, Dungun. The incident, covering a 23-hectare area, occurred on 15 June 2021. Initial data processing utilized Sentinel-2 satellite images from 14 June 2021 (pre-fire) and 19 June 2021 (post-fire). The extent and severity of the fire were assessed using the Normalized Burn Ratio (NBR) index derived from satellite images. Different… More >

Ting Cui, Xianfeng Zhou^*, Yufeng Huang, Yanting Guo, Yunrui Lin, Leyi Song, Jingcheng Zhang

Phyton-International Journal of Experimental Botany, Vol.92, No.9, pp. 2633-2648, 2023, DOI:10.32604/phyton.2023.029259 - 28 July 2023

Abstract Accurate assessment of canopy carotenoid content (CC_x+cC) in crops is central to monitor physiological conditions in plants and vegetation stress, and consequently supporting agronomic decisions. However, due to the overlap of absorption peaks of carotenoid (C_x+c) and chlorophyll (C_a), accurate estimation of carotenoid using reflectance where carotenoid absorb is challenging. The objective of present study was to assess CC_x+cC in winter wheat (Triticum aestivum L.) with ground- and aircraft-based hyperspectral measurements in the visible and near-infrared spectrum. In-situ hyperspectral reflectance were measured and airborne hyperspectral data were acquired during major growth stages of winter wheat in five consecutive… More >