Abstract
Accurate estimation of above ground biomass (AGB) is very important for crop growth monitoring. The objective of this study was to estimate rice biomass by utilizing structural and meteorological features with widely used spectral features. Structural features were derived from the triangulated irregular network (TIN), which was directly built from structure from motion (SfM) point clouds. Growing degree days (GDD) was used as the meteorological feature. Three models were used to estimate rice AGB, including the simple linear regression (SLR) model, simple exponential regression (SER) model, and machine learning model (random forest). Compared to models that do not use structural and meteorological features (NDRE, R2 = 0.64, RMSE = 286.79 g/m2, MAE = 236.49 g/m2), models that include such features obtained better estimation accuracy (NDRE*Hcv/GDD, R2 = 0.86, RMSE = 178.37 g/m2, MAE = 127.34 g/m2). This study suggests that the estimation accuracy of rice biomass can benefit from the utilization of structural and meteorological features.
Highlights
Rice is an important cereal grain that provides a stable source of food for more than half of the world’s population [1]
Our study aims to: (1) Evaluate the performance of above ground biomass (AGB) estimations based on multispectral features and structure features; (2) establish and evaluate models based on a combination of multispectral and structure features; and (3) establish and evaluate biomass estimation models based on a combination of spectral, structural, and meteorological features
When a single spectral variable was used for AGB estimation, we found that NDRE, CIrededge, and ρ700 had better performance among all the remotely sensed features
Summary
Rice is an important cereal grain that provides a stable source of food for more than half of the world’s population [1]. Traditional methods to estimate biomass are both labor-intensive and time-consuming, and are difficult to apply over large areas [3]. Remote sensing is a non-contact and non-destructive measuring method that can acquire both spectral and structural properties of the target at different spatial and temporal scales. These characteristics make remote sensing an optimal method for large area biomass estimation [3]. Satellite-based data may not provide sufficient data resolution for applications in precision agriculture. The high spatial resolution of LiDAR can provide a precise crop surface model (CSM), which further allows the biomass to be estimated using plant height [7,8]. A high computational resource is required for processing LiDAR data, which would not be ideal for applications that cover a large area [9]
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