Work

Enhancing Cross-Species Prediction of Leaf Mass per Area from Hyperspectral Remote Sensing Using Fractional Order Derivatives and 1D-CNNs

Year: 2026

Type: article

Abstract: Leaf mass per area (LMA) plays an important role in vegetation productivity, carbon cycling, and remote sensing-based ecosystem monitoring. However, remotely predicting LMA from hyperspectral reflectance remains challenging due to the weak and strongly overlapping spectral response of LMA and spectral variability across species. To address these limitations, this study proposed an integrated framework that combines a fractional-order spectral derivative (FOD) with a one-dimensional convolutional neural network (1D-CNN) to enhance LMA prediction accuracy and cross-species generalization. Leaf hyperspectral reflectance was processed using FOD with 0–2 orders, and the relationship between FOD-enhanced spectra and LMA was analyzed. Model performance was assessed using (i) overall prediction accuracy by an 8:2 random split between training and test sets, and (ii) cross-species generalization through leave-one-species-out validation. The results demonstrated that the 1D-CNN using a 1.5-order derivative achieved the best performance (R2 = 0.85; RMSE = 11.57 g/m2), outperforming common machine-learning models including partial least squares regression (PLSR), random forest (RF), and support vector regression (SVR). The proposed method also demonstrated great generalization in cross-species prediction. These results indicate that integrating FOD with 1D-CNN effectively enhances LMA-related spectral information and improves LMA prediction across various species. It provides a promising pathway for applying airborne and satellite hyperspectral images in vegetation biochemical parameter mapping, crop monitoring, and ecological assessment.

Source: Remote Sensing

DOI: 10.3390/rs18030444

Authors: Shijie Shan, Qiaozhen Guo, Lu Xu, Weiguo Jiang, Shuo Shi, Yiyun Chen

Institutions: Tianjin Chengjian University, Wuhan University, State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Beijing Normal University

Language: English

Topic: Remote Sensing in Agriculture

Subfield: Ecology

Field: Environmental Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,258

OpenAlex ID: W7126419015

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