Combining UAV and Sentinel-2 satellite multi-spectral images to diagnose crop growth and N status in winter wheat at the county scale

Jiang, J. and Atkinson, P.M. and Chen, C. and Cao, Q. and Tian, Y. and Zhu, Y. and Liu, X. and Cao, W. (2023) Combining UAV and Sentinel-2 satellite multi-spectral images to diagnose crop growth and N status in winter wheat at the county scale. Field Crops Research, 294: 108860. ISSN 0378-4290

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Abstract

Real-time and non-destructive nitrogen (N) status diagnosis is needed to support in-season N management decision-making for modern wheat production. For this purpose, satellite sensor imaging can act as an effective tool for collecting crop growth information across large areas, but they can be challenging to calibrate with ground reference data. This research aimed to calibrate satellite remote sensing-derived models for crop growth estimation and N status diagnosis based on fine-resolution unmanned aerial vehicle (UAV) images, thus, map wheat growth and N status at the county scale. Seven wheat field experiments involving multi cultivars and different N applications were conducted at four farms of Xinghua county from 2017 to 2021. A fixed-wing UAV sensing system and the Sentinel 2 (S2) satellite were used to collect wheat canopy multispectral images; three growth variables (plant dry matter (PDM), plant N accumulation (PNA) and N nutrition index (NNI)) and weather data, synchronized with spectral imagery, were obtained at the jointing and booting stages. The farm-scale PDM (UAV-PDM) and PNA (UAV-PNA) maps can be derived from the UAV images at the four farms, which were further upscaled to grids to match the S2 image resolution using pixel aggregation method. Then, satellite-based prediction models were constructed by fitting four machine learning algorithms to the relationships between satellite spectral indices, upscaled PDM (PNA) and weather data. Amongst the four methods tested, the random forest (RF) achieved the greatest prediction accuracy for PDM (R2 = 0.69–0.93) and PNA (R2 = 0.60–0.77). Meanwhile, an indirect diagnosis method was used to calculate the NNI. The results indicated that the model derived from the S2 imagery performed well for predicting NNI (R2 = 0.46–0.54) at the jointing and booting stages. Thereby, the NNI was used to map winter wheat N nutrition status at the county scale. In summary, this research demonstrated and evaluated an approach to combine UAV and satellite sensor images to diagnose wheat growth and N status across large areas.