Crop Monitoring Using Satellite/UAV Data Fusion and Machine Learning

TLDR

Non‑destructive, large‑area crop monitoring is crucial for precision agriculture, plant phenotyping, and food‑security decision making. This study evaluates whether fusing satellite spectral data with UAV‑derived canopy structure features improves crop monitoring via machine learning. Using synchronized Worldview‑2/3 imagery and high‑resolution UAV RGB over soybean, the authors extracted vegetation indices, canopy height, and cover, then applied PLSR, RFR, SVR, and ELR models to predict LAI, AGB, and leaf nitrogen. Combining satellite spectral and UAV structural features enhanced AGB, LAI, and N estimates, with ELR slightly outperforming other models for AGB and LAI and RFR best for N, demonstrating the benefits and limitations of satellite/UAV fusion for crop monitoring.

Abstract

Non-destructive crop monitoring over large areas with high efficiency is of great significance in precision agriculture and plant phenotyping, as well as decision making with regards to grain policy and food security. The goal of this research was to assess the potential of combining canopy spectral information with canopy structure features for crop monitoring using satellite/unmanned aerial vehicle (UAV) data fusion and machine learning. Worldview-2/3 satellite data were tasked synchronized with high-resolution RGB image collection using an inexpensive unmanned aerial vehicle (UAV) at a heterogeneous soybean (Glycine max (L.) Merr.) field. Canopy spectral information (i.e., vegetation indices) was extracted from Worldview-2/3 data, and canopy structure information (i.e., canopy height and canopy cover) was derived from UAV RGB imagery. Canopy spectral and structure information and their combination were used to predict soybean leaf area index (LAI), aboveground biomass (AGB), and leaf nitrogen concentration (N) using partial least squares regression (PLSR), random forest regression (RFR), support vector regression (SVR), and extreme learning regression (ELR) with a newly proposed activation function. The results revealed that: (1) UAV imagery-derived high-resolution and detailed canopy structure features, canopy height, and canopy coverage were significant indicators for crop growth monitoring, (2) integration of satellite imagery-based rich canopy spectral information with UAV-derived canopy structural features using machine learning improved soybean AGB, LAI, and leaf N estimation on using satellite or UAV data alone, (3) adding canopy structure information to spectral features reduced background soil effect and asymptotic saturation issue to some extent and led to better model performance, (4) the ELR model with the newly proposed activated function slightly outperformed PLSR, RFR, and SVR in the prediction of AGB and LAI, while RFR provided the best result for N estimation. This study introduced opportunities and limitations of satellite/UAV data fusion using machine learning in the context of crop monitoring.