Dr. Weiping Kong Profile

Dr. Weiping Kong

at Aerospace Information Research Institute CAS

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Publications (2)

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Proceedings Article | 12 September 2021 Presentation + Paper

Estimation of leaf area index at the late growth stage of crops using unmanned aerial vehicle hyperspectral images

Weiping Kong, Wenjiang Huang, Lingling Ma, Binbin Chen, Chuanrong Li, Lingli Tang

Proceedings Volume 11856, 118560X (2021) https://doi.org/10.1117/12.2611121

KEYWORDS: Vegetation, Unmanned aerial vehicles, Reflectivity, Hyperspectral imaging, Remote sensing, Data modeling, Data analysis, Sensors, Spectral resolution

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Estimation of leaf area index (LAI) is of vital importance to improve the prediction accuracy of crops quality and yield. However, it is more difficult to precisely assess LAI at the late growth stages of crops due to the influences of leaf senescence and soil background. Unmanned aerial vehicles (UAVs), with hyperspectral sensors onboard, can acquire high spatial and spectral resolution images and provide detailed information of fields, and consequently, are widely used for monitoring the biophysical parameters of crops in precision agriculture. The aim of this study was to evaluate the potential of UAV-based hyperspectral data in LAI estimation for sunflower and maize at the milk-filling stage, with machine learning regression algorithms (MLRA) for data analyses. Three algorithms including linear regression (LR), partial least square regression (PLSR) and kernel ridge regression (KRR) were used with the individual vegetation index (VI), VI-combination and spectral reflectance of full wavelengths as input variables. Results indicate that from the perspective of accuracy of estimation models, the PLSR based on VI-combination derived from hyperspectral images outperformed the LR based on individual VI and KRR based on VI-combination or spectral reflectance, which was proven to be the most suitable for the LAI estimation for both maize and sunflower at late growth stage, with 68% and 64% of the variation in LAI were explained, respectively. From the perspective of VIs tested, the modified triangular vegetation index (MTVI1) and improved soil-adjusted vegetation index (MSAVI) were found to be the best LAI estimators for maize and sunflower. Meanwhile, the contributions of the two VIs were also superior over other VIs tested in developing estimation models based on the PLSR method.

Proceedings Article | 20 September 2020 Presentation + Paper

Detecting leaf phosphorus content in arbuscular mycorrhizal fungi-inoculated soybean using hyperspectral remote sensing data

Weiping Kong, Wenjiang Huang, Lingling Ma, Lingli Tang, Chuanrong Li, Yinli Bi

Proceedings Volume 11528, 115280M (2020) https://doi.org/10.1117/12.2570757

KEYWORDS: Reflectivity, Phosphorus, Remote sensing, Near infrared, Data modeling, Visible radiation, Atmospheric modeling, Statistical modeling, Nondestructive evaluation

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Phosphorus (P) is an important parameter participated in the process of metabolism, photosynthesis, and energy exchange of crops. A growing number of studies have focused on effects of arbuscular mycorrhizal fungi (AMF) inoculation on crop P uptake. In this context, efficient and nondestructive monitoring of the changes of leaf P content (LPC) in inoculated crops is of vital. In this study, hyperspectral remote sensing was explored in an attempt to diagnose P deficiency in the inoculated and non-inoculated soybean plants. Greenhouse pot experiment was conducted under drought stresses, and measurements of leaf spectral reflectance and LPC were carried out at the 30th, 45th and 64th days after inoculation. We transformed the raw spectral reflectance (R) into the first derivation (FD), second derivation (SD), reciprocal (1/R), reciprocal logarithm (log(1/R)) and first derivation of log(1/R) (log’(1/R)). Results indicated that the AMF-inoculated plant had significantly higher LPC than the counterparts under different drought stresses. Analysis of the correlation between LPC and the raw and five transformed reflectance in the 350-2500 nm spectral range indicated that the green bands center around 545 nm and 567 nm, as well as NIR band center around 832 nm were the most sensitive (r>0.73). The kernel ridge regression (KRR) of LPC with the sensitive bands selected from the raw/ transformed reflectance was performed, showing that FD, 1/R and log(1/R) produced excellent results in LPC assessment, with determination of coefficient (R²) all larger than 0.70. Validation with independent samples revealed that the log(1/R)-KRR model achieved the strongest and superior prediction accuracy, with R2 of 0.93, RMSE of 0.23 g/kg and RRMSE of 7.8%, respectively. Our results indicate that the log(1/R)-KRR derived from hypersectral remote sensing data can provide the most suitable estimation model for describing the dynamic changes of LPC in the AMF-inoculated soybean.

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