UAV-Based Biomass Estimation for Rice-Combining Spectral, TIN-Based Structural and Meteorological Features

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.

A Systematic Review of the Factors Influencing the Estimation of Vegetation Aboveground Biomass Using Unmanned Aerial Systems

“…Some studies used spectral information [2,11,18,24,26,43,58,[60][61][62] and some structural information [1,8,22,23,28,34,48,50,55,[63][64][65]. Others used both [3][4][5][6]9,12,16,20,21,25,30,33,49,57,[66][67][68], while a few studies used spectral and structural metrics plus another data type [13,27,69] (Table A1). Within these categories, a wide range of species, study areas and methods are examined, demonstrating the applicability of UAS data to AGB estimation in agricultural and non-agricultural environments.…”

“…Mean height [3,9,12,13,15,16,[19][20][21]23,25,28,30,34,[48][49][50]57,58,63,65,[67][68][69][74][75][76] Maximum height [1,3,4,13,28,30,34,48,57,63,65,69] Minimum height [3,28,34,48,57,63,65,69] Median height [12,21,27,48,63,65,…”

“…[68] TIN-based structure, area, slope [13] Mean, median and maximum height metrics provide information on the vertical distribution of the vegetation canopy [3,69]. Maximum or median height appear to be particularly useful for trees.…”

“…Percentiles of height and metrics such as the coefficient of variation (CV) or standard deviation (SD) of mean height can also be useful because they shed light on the horizontal and vertical complexity and heterogeneity of the canopy [13,15]. Wijensingha et al [63] found 75th percentile of height was the best among the ten height variables compared, producing moderate accuracy in grassland AGB estimation from linear regression models (R 2 = 0.58-0.62).…”

“…In agricultural systems, AGB is a key agro-ecological indicator [4,10] that can be used to monitor crop growth, light use efficiency, carbon stock and physiological condition [3,5,6,[11][12][13][14][15][16]; predict crop yield and ensure yield quality [2,3,5,[12][13][14][17][18][19][20][21][22][23]; inform precision agriculture practices [3,15,[21][22][23]; maximize efficiency of fertilization and watering [4,10,14,21]; detect growth differences among phenotypes or cultivars [10,23]; calculate nitrogen content and assess nutrient status of plants [24][25][26]; and optimize economic decision-making throughout the growing season [3,14,15,19,22,27].…”