Comparing the predictive ability of Sentinel-2 multispectral imagery and a proximal hyperspectral sensor for the estimation of pasture nutritive characteristics in an intensive rotational grazing system

Thomson, A.; Jacobs, J.; Morse-McNabb, E.

doi:10.1016/j.compag.2023.108275

Cited by 4 publications

(2 citation statements)

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“…The R 2 , RMSE, and RPD of the model based on AGB/Hdsm are 0.88, 2291.90 kg/hm 2 , and 2.75, respectively. The improved model based on AGB/Hdsm could estimate the AGB well, and the estimated mean AGB (17,478.21 kg/hm 2 ) is very close to the measured mean AGB (17,222.59 kg/hm 2 ). The improved model based on AGB/Hdsm (Figure 9) outperforms the model described in Section 3.4 (Figure 7), with a higher R 2 , a higher RPD, and a lower RMSE.…”

Section: Agb Estimation Model Construction and Improvementsupporting

confidence: 56%

“…However, satellite remote sensing images, which are usually used as data for AGB estimation and retrieval, suffer from low spatio-temporal resolution and interference from atmospheric conditions [15,16]. Proximal hyperspectral imagery has a high spectral resolution, but the process is time-consuming and labor-intensive, which limits its application in large-spatial-scale surveys [17]. Compared to satellite remote sensing and proximal hyperspectral remote sensing, low-altitude UAVs can carry different types of sensors, such as RGB cameras, hyperspectral imagers, and multispectral imagers, according to the specific purpose of the survey being conducted [10,11,15].…”

Section: Introductionmentioning

confidence: 99%

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Improvement of Winter Wheat Aboveground Biomass Estimation Using Digital Surface Model Information Extracted from Unmanned-Aerial-Vehicle-Based Multispectral Images

Guo,

He,

Zhang

et al. 2024

Agriculture

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Aboveground biomass (AGB) is an important indicator for characterizing crop growth conditions. A rapid and accurate estimation of AGB is critical for guiding the management of farmland and achieving production potential, and it can also provide vital data for ensuring food security. In this study, by applying different water and nitrogen treatments, an unmanned aerial vehicle (UAV) equipped with a multispectral imaging spectrometer was used to acquire images of winter wheat during critical growth stages. Then, the plant height (Hdsm) extracted from the digital surface model (DSM) information was used to establish and improve the estimation model of AGB, using the backpropagation (BP) neural network, a machine learning method. The results show that (1) the R2, root-mean-square error (RMSE), and relative predictive deviation (RPD) of the AGB estimation model, constructed directly using the Hdsm, are 0.58, 4528.23 kg/hm2, and 1.25, respectively. The estimated mean AGB (16,198.27 kg/hm2) is slightly smaller than the measured mean AGB (16,960.23 kg/hm2). (2) The R2, RMSE, and RPD of the improved AGB estimation model, based on AGB/Hdsm, are 0.88, 2291.90 kg/hm2, and 2.75, respectively, and the estimated mean AGB (17,478.21 kg/hm2) is more similar to the measured mean AGB (17,222.59 kg/hm2). The improved AGB estimation model boosts the accuracy by 51.72% compared with the AGB directly estimated using the Hdsm. Moreover, the improved AGB estimation model shows strong transferability in regard to different water treatments and different year scenarios, but there are differences in the transferability for different N-level scenarios. (3) Differences in the characteristics of the data are the key factors that lead to the different transferability of the AGB estimation model. This study provides an antecedent in regard to model construction and transferability estimation of AGB for winter wheat. We confirm that, when different datasets have similar histogram characteristics, the model is applicable to new scenarios.

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Section: Agb Estimation Model Construction and Improvementsupporting

confidence: 56%