A Model Combining Sensitive Vegetation Indices and Fractional-Order Differential Characteristic Bands for SPAD Value Estimation in Cd-Contaminated Rice Leaves

Rongcai Tian, Bin Zou, Shenxin Li (), Li Dai, Bo Zhang, Yulong Wang, Hao Tu, Jie Zhang and Lunwen Zou
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Rongcai Tian: School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
Bin Zou: School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
Shenxin Li: School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
Li Dai: Hunan Rice Research Institute, Changsha 410125, China
Bo Zhang: School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
Yulong Wang: School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
Hao Tu: School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
Jie Zhang: School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
Lunwen Zou: School of Geographical Sciences, Hunan Normal University, Changsha 410081, China

Agriculture, 2025, vol. 15, issue 3, 1-20

Abstract: Rapid and nondestructive estimation of leaf SPAD values is crucial for monitoring the effects of cadmium (Cd) stress in rice. To address the issue of low estimation accuracy in leaf SPAD value models due to the loss of spectral information in existing studies, a new estimation model, which combines sensitive vegetation indices (VIss) and fractional order differential characteristic bands (FODcb), is proposed in this study. To validate the effectiveness of this new model, three scenarios, with no Cd contamination, 1.0 mg/kg Cd contamination, and 1.4 mg/kg Cd contamination, were set up. Leaf spectral reflectance and SPAD values were measured during the critical growth period of rice. Subsequently, 16 vegetation indices were constructed, and fractional order difference (FOD) transformation was applied to process the spectral data. The variable importance in projection (VIP) algorithm was employed to extract VIss and FODcb. Finally, the random forest (RF) algorithm was used to construct three models, VIss + FODcb-RF, FODcb-RF, and VIss-RF. The estimated leaf SPAD values for the three models showed that: (1) there was a significant difference between the leaf SPAD values with no Cd contamination and those treated with 1.4 mg/kg Cd contamination on the 31st and 87th days after transplanting; (2) the 400–773 nm spectral range was sensitive for estimating leaf SPAD values, with the Cd-contaminated scenario exhibiting higher reflectance in the visible wavelength range than the Cd-uncontaminated scenario; (3) compared with the individual FODcb-RF and Viss-RF models, the combined model (VIss + FODcb-RF) improved the estimation accuracy of the leaf SPAD values. Particularly, the Viss + FOD 1.2cb -RF model provided the best performance, with R 2 v, RMSEv, and RPDv values of 0.821, 2.621, and 2.296, respectively. In conclusion, this study demonstrates the effectiveness of combining VIss and FODcb for accurately estimating Cd-contaminated rice leaf SPAD values. This finding will provide a methodological reference for remote sensing monitoring of Cd contamination in rice.

Keywords: Cd-contaminated rice; leaf SPAD values; vegetation indices; fractional order differential characteristic bands; hyperspectral remote sensing (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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