Prior knowledge and active learning enable hybrid method for estimating leaf chlorophyll content from multi-scale canopy reflectance

Wan, Liang; Liu, Yufei; He, Yong; Cen, Haiyan

doi:10.1016/j.compag.2023.108308

Cited by 5 publications

(1 citation statement)

References 52 publications

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“…Verrelst et al suggested that AL offers RTM training data for building inverse models [32]. Wan et al demonstrated that the AL method can improve the accuracy of chlorophyll content inversion based on canopy and UAV data [33]. Guo et al constructed a hybrid model consisting of the PROSAIL model and GPR algorithm and at the same time, introduced the AL strategy to invert the chlorophyll content of maize leaves and canopies, and the results showed that the AL strategy could improve the accuracy of maize chlorophyll inversion [34].…”

Section: Introductionmentioning

confidence: 99%

Potato Leaf Chlorophyll Content Estimation through Radiative Transfer Modeling and Active Learning

Ma,

Qiu,

Zhang

et al. 2023

Agronomy

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Leaf chlorophyll content (LCC) significantly correlates with crop growth conditions, nitrogen content, yield, etc. It is a crucial indicator for elucidating the senescence process of plants and can reflect their growth and nutrition status. This study was carried out based on a potato nitrogen and potassium fertilizer gradient experiment in the year 2022 at Keshan Farm, Qiqihar Branch of Heilongjiang Agricultural Reclamation Bureau. Leaf hyperspectral and leaf chlorophyll content data were collected at the potato tuber formation, tuber growth, and starch accumulation periods. The PROSPECT-4 radiative transfer model was employed to construct a look-up table (LUT) as a simulated data set. This was accomplished by simulating potato leaves’ spectral reflectance and chlorophyll content. Then, the active learning (AL) technique was used to select the most enlightening training samples from the LUT based on the measured potato data. The Gaussian process regression (GPR) algorithm was finally employed to construct the inversion models for the chlorophyll content of potato leaves for both the whole and single growth periods based on the training samples selected by the AL method and the ground measured data of the potatoes. The R2 values of model validation accuracy for the potato whole plantation period and three single growth periods are 0.742, 0.683, 0.828, and 0.533, respectively with RMSE values of 4.207, 4.364, 2.301, and 3.791 µg/cm2. Compared with the LCC inversion accuracy through LUT with a cost function, the validation accuracies of the GPR_PROSPECT-AL hybrid model were improved by 0.119, 0.200, 0.328, and 0.255, and the RMSE were reduced by 3.763, 2.759, 0.118, and 5.058 µg/cm2, respectively. The study results indicate that the hybrid method combined with the radiative transfer model and active learning can effectively select informative training samples from a data pool and improve the accuracy of potato LCC estimation, which provides a valid tool for accurately monitoring crop growth and growth health.

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Section: Introductionmentioning

confidence: 99%