Work

A Difference Wavelet Feature Index for Estimating Aerial N Uptake of Winter Wheat from In Situ Hyperspectral Remote Sensing

Year: 2025

Type: article

Abstract: The real-time and accurate assessment of crop aerial nitrogen (N) uptake is of significant importance for optimizing N fertilization. To develop a robust method for determining aerial N uptake in winter wheat, a field experiment with different N fertilizer levels was conducted over three successive years at two ecological sites in Henan, China. This research systematically compared the correlation between aerial N uptake and spectral parameters derived from various spectral transform methods: continuum removal (CR), standard normal variate transform method (SNV), first derivative reflectance (FDR), continuous wavelet transforms (CWT). The findings revealed that CWT exhibited the highest efficacy among all the spectral transform methods, followed by FDR, with R²values of 0.777 for WF(4,770) and 0.764 for FDR748. A new index, termed the difference wavelet feature index (DWF), is defined as DWF(4,560,770) = WF(4,560) − WF(4,770). This simple yet effective index significantly enhances the assessment of aerial N uptake, achieving an R²of 0.815. Validation with independent data showed that the RMSE for the DIDA, FDR748, WF(4,770), and DWF(4,560,770) under different cultivation factors were 3.578-4.361 g m-2, 3.501-4.219 g m-2, 3.472-4.309 g m-2, 3.262-4.030 g m-2, respectively. It was further verified that the newly DWF(4,560,770) index has excellent universality and stability. Therefore, the aforementioned studies indicated that the novel DWF(4,560,770) is more suitable for evaluating aerial N uptake at the heterogeneous field scale and also has significant potential for precise prediction of aerial N uptake using UAV remote sensing.

Source: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

DOI: 10.1109/jstars.2025.3559100

Authors: Bin-Bin Guo, Wen-Hui Wang, Chao Ma, Jun Zhang, Fei Yin, Wei Feng

Institutions: Henan University of Science and Technology, Langfang Normal University, Henan Agricultural University

Language: English

Topic: Remote Sensing in Agriculture

Subfield: Ecology

Field: Environmental Science

Domain: Physical Sciences

SDG: Zero hunger

Open Access status: gold

Estimated APC paid: $1,800