Abstract
Timely and accurate estimates of crop parameters are crucial for agriculture management. Unmanned aerial vehicles (UAVs) carrying sophisticated cameras are very pertinent for this work because they can obtain remote-sensing images with higher temporal, spatial, and ground resolution than satellites. In this study, we evaluated (i) the performance of crop parameters estimates using a near-surface spectroscopy (350~2500 nm, 3 nm at 700 nm, 8.5 nm at 1400 nm, 6.5 nm at 2100 nm), a UAV-mounted snapshot hyperspectral sensor (450~950 nm, 8 nm at 532 nm) and a high-definition digital camera (Visible, R, G, B); (ii) the crop surface models (CSMs), RGB-based vegetation indices (VIs), hyperspectral-based VIs, and methods combined therefrom to make multi-temporal estimates of crop parameters and to map the parameters. The estimated leaf area index (LAI) and above-ground biomass (AGB) are obtained by using linear and exponential equations, random forest (RF) regression, and partial least squares regression (PLSR) to combine the UAV based spectral VIs and crop heights (from the CSMs). The results show that: (i) spectral VIs correlate strongly with LAI and AGB over single growing stages when crop height correlates positively with AGB over multiple growth stages; (ii) the correlation between the VIs multiplying crop height and AGB is greater than that between a single VI and crop height; (iii) the AGB estimate from the UAV-mounted snapshot hyperspectral sensor and high-definition digital camera is similar to the results from the ground spectrometer when using the combined methods (i.e., using VIs multiplying crop height, RF and PLSR to combine VIs and crop heights); and (iv) the spectral performance of the sensors is crucial in LAI estimates (the wheat LAI cannot be accurately estimated over multiple growing stages when using only crop height). The LAI estimates ranked from best to worst are ground spectrometer, UAV snapshot hyperspectral sensor, and UAV high-definition digital camera.
Highlights
Crop parameters, such as leaf area index (LAI) and above-ground biomass (AGB) are crucial for accurately monitoring crop growth for agriculture management [1,2], and accurate estimates of crop variables can help improve crop monitoring and yield predictions [3,4]
Note: AGB: above-ground biomass; LAI: leaf area index; MAE: mean absolute error; RMSE: root mean square error; G- indicates data measured by a steel tape ruler; UHD- indicates data measured using the snapshot hyperspectral sensor mounted on the unmanned aerial vehicles (UAVs); and DC- indicates data measured by using the digital camera mounted on the UAV
Note: AGB: above-ground biomass; LAI: leaf area index; MAE: mean absolute error; RMSE: root mean square error; G- indicates data measured by a steel tape ruler; UHD- indicates data measured using the snapshot hyperspectral RemotseeSnesnosr. m20o1u8n, 1te0d, xoFnOthRePUEAEVR; RanEdVIDECW- indicates data measured by using the digital camera mounted on the UAV1.7 of 24
Summary
Crop parameters, such as leaf area index (LAI) and above-ground biomass (AGB) are crucial for accurately monitoring crop growth for agriculture management [1,2], and accurate estimates of crop variables can help improve crop monitoring and yield predictions [3,4]. And accurate estimates of crop parameters are crucial for agriculture management To achieve this goal, remote sensing via unmanned aerial vehicles (UAVs) has recently attracted the attention of many researchers because it yields remote-sensing images with higher temporal, spatial, and ground resolutions than are available from satellites [13,14,15,16]. UAVs have simpler requirements for takeoff and landing
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