Coupling PROSPECT with prior estimation of leaf structure to improve the retrieval of leaf nitrogen content in Ginkgo from bidirectional reflectance factor (BRF) spectra DOI Creative Commons
Kai Zhou, Saiting Qiu, Fuliang Cao

et al.

Plant Phenomics, Journal Year: 2024, Volume and Issue: 6

Published: Jan. 1, 2024

Leaf nitrogen content (LNC) is a crucial indicator for assessing the status of forest trees. The LNC retrieval can be achieved with inversion PROSPECT-PRO model. However, from commonly used leaf bidirectional reflectance factor (BRF) spectra remains challenging arising confounding effects mesophyll structure, specular reflection, and other chemicals such as water. To address this issue, study proposed an advanced BRF spectra-based approach, by alleviating reflection enhancing absorption signals

Language: Английский

Inversion of Leaf Chlorophyll Content in Different Growth Periods of Maize Based on Multi-Source Data from “Sky–Space–Ground” DOI Creative Commons

Wu Nile,

Rina Su,

Na Mula

et al.

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(4), P. 572 - 572

Published: Feb. 8, 2025

Leaf chlorophyll content (LCC) is a key indicator of crop growth condition. Real-time, non-destructive, rapid, and accurate LCC monitoring paramount importance for precision agriculture management. This study proposes an improved method based on multi-source data, combining the Sentinel-2A spectral response function (SRF) computer algorithms, to overcome limitations traditional methods. First, equivalent remote sensing reflectance was simulated by UAV hyperspectral images with ground experimental data. Then, using grey relational analysis (GRA) maximum information coefficient (MIC) algorithm, we explored complex relationship between vegetation indices (VIs) LCC, further selected feature variables. Meanwhile, utilized three (DSI, NDSI, RSI) identify sensitive band combinations analyzed original bands LCC. On this basis, nonlinear machine learning models (XGBoost, RFR, SVR) one multiple linear regression model (PLSR) construct inversion model, chose optimal generate spatial distribution maps maize at regional scale. The results indicate that there significant correlation VIs XGBoost, SVR outperforming PLSR model. Among them, XGBoost_MIC achieved best during tasseling stage (VT) growth. In R2 = 0.962 RMSE 5.590 mg/m2 in training set, 0.582 6.019 test set. For Sentinel-2A-simulated set had 0.923 8.097 mg/m2, while showed 0.837 3.250 which indicates improvement accuracy. scale, also yielded good (train 0.76, 0.88, 18.83 mg/m2). conclusion, proposed not only significantly improves accuracy methods but also, its outstanding versatility, can achieve precise different regions various types, demonstrating broad application prospects practical value agriculture.

Language: Английский

Citations

1
Vis/NIR Spectroscopy and Chemometrics for Non-Destructive Estimation of Chlorophyll Content in Different Plant Leaves DOI Creative Commons
Qiang Huang, Meihua Yang, Liao Ouyang

et al.

Sensors, Journal Year: 2025, Volume and Issue: 25(6), P. 1673 - 1673

Published: March 8, 2025

Vegetation biochemical and biophysical variables, especially chlorophyll content, are pivotal indicators for assessing drought’s impact on plants. Chlorophyll, crucial photosynthesis, ultimately influences crop productivity. This study evaluates the mean squared Euclidean distance (MSD) method, traditionally applied in soil analysis, estimating content five diverse leaf types across various months using visible/near-infrared (vis/NIR) spectral reflectance. The MSD method serves as a tool selecting representative calibration dataset. By integrating with partial least squares regression (PLSR) Cubist model, we aim to accurately predict focusing key bands within ranges of 500–640 nm 740–1100 nm. In validation dataset, PLSR achieved high determination coefficient (R2) 0.70 low bias error (MBE) 0.04 mg g−1. model performed even better, demonstrating an R2 0.77 exceptionally MBE 0.01 These results indicate that dataset leaves, vis/NIR spectrometry combined is promising alternative traditional methods quantifying over months. technique non-destructive, rapid, consistent, making it invaluable drought impacts plant health

Language: Английский

Citations

0
Measurement of irrigation management based on canopy-air temperature modeling for corn and wheat crops DOI

Zhenfeng Yang,

Juncang Tian, Zan Ouyang

et al.

Plant and Soil, Journal Year: 2025, Volume and Issue: unknown

Published: April 29, 2025

Language: Английский

Citations

0
Comparative Analysis of Spectroradiometric and Chemical Methods for Nutrient Detection in Black Gram Leaves DOI Creative Commons

M. Balamurugan,

K. Kalaiarasi,

Jayalakshmi Shanmugam

et al.

Results in Engineering, Journal Year: 2024, Volume and Issue: 24, P. 103065 - 103065

Published: Oct. 9, 2024

Language: Английский

Citations

3
Coupling PROSPECT with prior estimation of leaf structure to improve the retrieval of leaf nitrogen content in Ginkgo from bidirectional reflectance factor (BRF) spectra DOI Creative Commons
Kai Zhou, Saiting Qiu, Fuliang Cao

et al.

Plant Phenomics, Journal Year: 2024, Volume and Issue: 6

Published: Jan. 1, 2024

Leaf nitrogen content (LNC) is a crucial indicator for assessing the status of forest trees. The LNC retrieval can be achieved with inversion PROSPECT-PRO model. However, from commonly used leaf bidirectional reflectance factor (BRF) spectra remains challenging arising confounding effects mesophyll structure, specular reflection, and other chemicals such as water. To address this issue, study proposed an advanced BRF spectra-based approach, by alleviating reflection enhancing absorption signals

Language: Английский

Citations

0