Simulating the Leaf Area Index of Rice from Multispectral Images

Abstract

Accurate estimation of the leaf area index (LAI) is essential for crop growth simulations and agricultural management. This study conducted a field experiment with rice and measured the LAI in different rice growth periods. The multispectral bands (B) including red edge (RE, 730 nm ± 16 nm), near-infrared (NIR, 840 nm ± 26 nm), green (560 nm ± 16 nm), red (650 nm ± 16 nm), blue (450 nm ± 16 nm), and visible light (RGB) were also obtained by an unmanned aerial vehicle (UAV) with multispectral sensors (DJI-P4M, SZ DJI Technology Co., Ltd.). Based on the bands, five vegetation indexes (VI) including Green Normalized Difference Vegetation Index (GNDVI), Leaf Chlorophyll Index (LCI), Normalized Difference Red Edge Index (NDRE), Normalized Difference Vegetation Index (NDVI), and Optimization Soil-Adjusted Vegetation Index (OSAVI) were calculated. The semi-empirical model (SEM), the random forest model (RF), and the Extreme Gradient Boosting model (XGBoost) were used to estimate rice LAI based on multispectral bands, VIs, and their combinations, respectively. The results indicated that the GNDVI had the highest accuracy in the SEM (R2 = 0.78, RMSE = 0.77). For the single band, NIR had the highest accuracy in both RF (R2 = 0.73, RMSE = 0.98) and XGBoost (R2 = 0.77, RMSE = 0.88). Band combination of NIR + red improved the estimation accuracy in both RF (R2 = 0.87, RMSE = 0.65) and XGBoost (R2 = 0.88, RMSE = 0.63). NDRE and LCI were the first two single VIs for LAI estimation using both RF and XGBoost. However, putting more than one VI together could only increase the LAI estimation accuracy slightly. Meanwhile, the bands + VIs combinations could improve the accuracy in both RF and XGBoost. Our study recommended estimating rice LAI by a combination of red + NIR + OSAVI + NDVI + GNDVI + LCI + NDRE (2B + 5V) with XGBoost to obtain high accuracy and overcome the potential over-fitting issue (R2 = 0.91, RMSE = 0.54).

Mapping mangrove leaf area index (LAI) by combining remote sensing images with PROSAIL‐D and XGBoost methods

“…Liu et al. (2021) demonstrated that XGBoost outperformed the other machine‐learning models in estimating crop LAI. Moreover, our study reported value of 0.84–0.86 using the hybrid method of PROSAIL‐D and XGBoost model with Worldview‐2 and Zhuhai‐1, suggesting XGBoost model has a great potential in accurate estimation of mangrove LAI.…”

“…PROSAIL is one of the most widely used canopy-based RTMs due to its computational efficiency and robustness, and it is based on the combination of leaf-based PROSPECT (Jacquemoud & Baret, 1990) and canopybased SAIL (Verhoef, 1984) model. Many machinelearning methods, such as ANN (artificial neural networks) (Omer et al, 2016), SVR (support vector regression) (Badnakhe et al, 2018), RF (random forest) (Campos-Taberner et al, 2018), and XGBoost (extreme gradient boosting) (Liu et al, 2021;Zhang, Cheng, et al, 2021) have been employed to estimate LAI. Danner et al (2021) showed XGBoost outperformed ANN and RF in the retrieval of LAI and biomass.…”

Comparative leaf area index estimation using multispectral and RGB images from a UAV platform

“…The 40m altitude has slightly better results regarding R 2 and RMSE, but there are no substantial differences between the three altitudes. Moreover, among the different indices, the NDRE index is the most promising for estimating the leaf area index [43], [44], as it shows relatively high R 2 values and low MAE across all stages and heights.…”

Analysis of the Vigor of Pinus hartwegii Lindl. along an Altitudinal Gradient Using UAV Multispectral Images: Evidence of Forest Decline Possibly Associated with Climatic Change

“…The LCI is a sensitive index for monitoring chlorophyll content in plants [38]. It has been used to assess vegetation growth and productivity [67,68]. It has also been shown to be an indicator for assessing stress, disease, and decline in conifers [35].…”