Mapping spatial variability of foliar nitrogen in coffee (Coffea arabica L.) plantations with multispectral Sentinel-2 MSI data

Chemura, Abel; Mutanga, Onisimo; Odindi, John; Kutywayo, Dumisani

doi:10.1016/j.isprsjprs.2018.02.004

Cited by 60 publications

(38 citation statements)

References 39 publications

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“…As a bridge connecting the spectral parameters and the grain protein content, agronomic parameters must not only reflect the level of grain protein content but also have a significant correlation with spectral parameters. Chemura et al [80] have assessed the feasibility of Sentinel-2 MSI spectral bands and vegetation indices in empirical estimation of coffee foliar N content at landscape level with Sentinel-2 data; results showed that coffee foliar N is related to Sentinel-2 MSI B4, B6, B7, B8 and B12 bands, and relative vegetation indices were more related to coffee foliar N, combining optimized bands and vegetation indices produced the best results in coffee foliar N modelling (R 2 = 0.78, RMSE = 0.23). Since the quality of wheat is determined by many factors, considering the correlation between the spectral parameters and the grain content of wheat protein in previous research, in this study, the spectral parameters were added as the inversion parameters in the process of inverting the grain protein content combined with agronomic parameters in order to improve the inversion accuracy of the grain protein content.…”

Section: Discussionmentioning

confidence: 99%

Monitoring of Nitrogen and Grain Protein Content in Winter Wheat Based on Sentinel-2A Data

et al. 2019

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Grain protein content (GPC) is an important indicator of wheat quality. Earlier estimation of wheat GPC based on remote sensing provided effective decision to adapt optimized strategies for grain harvest, which is of great significance for agricultural production. The objectives of this field study are: (i) To assess the ability of spectral vegetation indices (VIs) of Sentinel 2 data to detect the wheat nitrogen (N) attributes related to the grain quality of winter wheat production, and (ii) to examine the accuracy of wheat N status and GPC estimation models based on different VIs and wheat nitrogen parameters across Analytical Spectra Devices (ASD) and Unmanned Aerial Vehicle (UAV) hyper-spectral data-simulated sentinel data and the real Sentinel-2 data. In this study, four nitrogen parameters at the wheat anthesis stage, including plant nitrogen accumulation (PNA), plant nitrogen content (PNC), leaf nitrogen accumulation (LNA), and leaf nitrogen content (LNC), were evaluated for their relationship between spectral parameters and GPC. Then, a multivariate linear regression method was used to establish the wheat nitrogen and GPC estimation model through simulated Sentinel-2A VIs. The coefficients of determination (R2) of four nitrogen parameter models were all greater than 0.7. The minimum R2 of the prediction model of wheat GPC constructed by four nitrogen parameters combined with VIs was 0.428 and the highest R2 was 0.467. The normalized root mean square error (nRMSE) of the four nitrogen estimation models ranged from 26.333% to 29.530% when verified by the ground-measured data collected from the Beijing suburbs, and the corresponding nRMSE for the GPC-predicted models ranged from 17.457% to 52.518%. The accuracy of the estimated model was verified by UAV hyper-spectral data which had resized to different spatial resolution collected from the National Experimental Station for Precision Agriculture. The normalized root mean square error (nRMSE) of the four nitrogen estimation models ranged from 16.9% to 37.8%, and the corresponding nRMSE for the GPC-predicted models ranged from 12.3% to 13.2%. The relevant models were also verified by Sentinel-2A data collected in 2018 while the minimum nRMSE for GPC invert model based on PNA was 7.89% and the maximum nRMSE of the GPC model based on LNC was 12.46% in Renqiu district, Hebei province. The nRMSE for the wheat nitrogen estimation model ranged from 23.200% to 42.790% for LNC and PNC. These data demonstrate that freely available Sentinel-2 imagery can be used as an important data source for wheat nutrition and grain quality monitoring.

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Section: Discussionmentioning

confidence: 99%

Monitoring of Nitrogen and Grain Protein Content in Winter Wheat Based on Sentinel-2A Data

et al. 2019

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“…Space-borne sensors are widely available [12], but data collection is affected by clouds, poor atmospheric conditions, and has to cope with atmospheric perturbation [13]. In addition, they suffer from low spectral and spatial resolution (10-60 m) [14][15][16]. The closer the sensor to the target, the higher the spatial resolution [17].…”

Section: Introductionmentioning

confidence: 99%

Determination of Plant Nitrogen Content in Wheat Plants via Spectral Reflectance Measurements: Impact of Leaf Number and Leaf Position

2019

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The determination of plant nitrogen (N) content (%) in wheat via destructive lab analysis is expensive and inadequate for precision farming applications. Vegetation indices (VI) based on spectral reflectance can be used to predict plant N content indirectly. For these VI, reflectance from space-borne, airborne, or ground-borne sensors is captured. Measurements are often taken at the canopy level for practical reasons. Hence, translocation processes of nutrients that take place within the plant might be ignored or measurements might be less accurate if nutrient deficiency symptoms occur on the older leaves. This study investigated the impact of leaf number and measurement position on the leaf itself on the determination of plant N content (%) via reflectance measurements. Two hydroponic experiments were carried out. In the first experiment, the N fertilizer amount and growth stage for the determination of N content was varied, while the second experiment focused on a secondary induction of N deficiency due to drought stress. For each plant, reflectance measurements were taken from three leaves (L1, L2, L3) and at three positions on the leaf (P1, P2, P3). In addition, the N content (%) of the whole plant was determined by chemical lab analysis. Reflectance spectrometer measurements (400–1650 nm) were used to calculate 16 VI for each combination of leaf and position. N content (%) was predicted using each VI for each leaf and each position. Significant lower mean residual error variance (MREV) was found for leaves L1 and L3 and for measurement position on P3 in the N trial, but the difference of MREV between the leaves was very low and therefore considered as not relevant. The drought stress trial also led to no significant differences in MREV between leaves and positions. Neither the position on the leaf nor the leaf number had an impact on the accuracy of plant nitrogen determination via spectral reflectance measurements, wherefore measurements taken at the canopy level seem to be a valid approach.

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“…After pre-processing Sentinel-2 data for each season, we computed a number of vegetation indices as shown in Table 5.1. These vegetation indices were selected guided by their performance in estimating of leaf traits as reported in previous studies (Chemura et al, 2018;Main et al, 2011;Stagakis et al, 2010). To harmonize spatial scales of canopy traits values measured in the field against satellite data, we extracted the reflectance of a plot as the average of nine (3 × 3) pixels centered on the plot centre.…”

Section: Satellite Imagery Data and Preprocessingmentioning

confidence: 99%

“…Our study demonstrated for the first time the capability of Sentinel 2 data to estimate canopy LMA and carbon across vegetation phenophases. Previous efforts using Sentinel-2 data have mainly focused on foliar chlorophyll (Clevers et al, 2017;Delloye et al, 2018;Li et al, 2018a;Vincini et al, 2014) and nitrogen (Chemura et al, 2018;Mutowo et al, 2018) for single point in time typically at the peak growing season in agricultural systems. Our study, also demonstrated the development of a generalized model that captures phenological changes in leaf traits across multiple seasons.…”

Section: Does Canopy Traits Expression Affect the Correlation And Estmentioning

confidence: 99%

“…Remote sensing provides a cost-effective and practical means of estimating and monitoring leaf traits for biodiversity conservation (Kissling et al, 2017). Field spectroscopy and satellite multispectral data such as Sentinel-2 are critical primary data sources that can improve quantitative estimation and monitoring of foliar traits (Chemura et al, 2018). Essentially, two modelling approaches [i.e.…”

Section: Introductionmentioning

confidence: 99%

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Quantitative remote sensing of essential biodiversity variables

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Mapping spatial variability of foliar nitrogen in coffee (Coffea arabica L.) plantations with multispectral Sentinel-2 MSI data

Cited by 60 publications

References 39 publications

Monitoring of Nitrogen and Grain Protein Content in Winter Wheat Based on Sentinel-2A Data

Monitoring of Nitrogen and Grain Protein Content in Winter Wheat Based on Sentinel-2A Data

Determination of Plant Nitrogen Content in Wheat Plants via Spectral Reflectance Measurements: Impact of Leaf Number and Leaf Position

Quantitative remote sensing of essential biodiversity variables

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