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

Zhao, Haitao; Song, Xiaoyu; Yang, Guijun; Li, Zhenhai; Zhang, Dongyan; Feng, Haikuan

doi:10.3390/rs11141724

Cited by 42 publications

(25 citation statements)

References 65 publications

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“…The magnitude of the impedance can be calculated as (2) where R is the resistance and X is the reactance, and the gain factor is determined by the calibration using a known resistance of 7.5 kΩ [31]. After the impedances were measured, the samples with nitrogen concentrations were dried in a 60 °C incubator for 2 days.…”

Section: Methodsmentioning

confidence: 99%

“…Nitrogen is the most required mineral nutrient of a crop due to its importance in several plant cell components; its concentration in plant tissue is the highest of all mineral nutrients. Leaf nitrogen concentration (LNC) provides valuable information about the physiological status of plants which is directly linked to photosynthetic potential and primary production [1][2][3]. Precise timing and the rate of nitrogen fertilizer application play a major role in plant nutrition.…”

Section: Introductionmentioning

confidence: 99%

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Determination of Leaf Nitrogen Concentrations Using Electrical Impedance Spectroscopy in Multiple Crops

2020

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In this work, crop leaf nitrogen concentration (LNC) is predicted by leaf impedance measurements made by electrical impedance spectroscopy (EIS). This method uses portable equipment and is noninvasive, as are other available nondestructive methods, such as hyperspectral imaging, near-infrared spectroscopy, and soil-plant analyses development (SPAD). An EVAL-AD5933EBZ evaluation board is used to measure the impedances of four different crop leaves, i.e., canola, wheat, soybeans, and corn, in the frequency range of 5 to 15 kHz. Multiple linear regression using the least square method is employed to obtain a correlation between leaf nitrogen concentrations and leaf impedances. A strong correlation is found between nitrogen concentrations and measured impedances for multiple features using EIS. The results are obtained by PrimaXL Data Analysis ToolPak and validated by analysis of variance (ANOVA) tests. Optimized regression models are determined by selecting features using the backward elimination method. After a comparative analysis among the four different crops, the best multiple regression results are found for canola with an overall correlation coefficient (R) of 0.99, a coefficient of determination (R 2 ) of 0.98, and root mean square (RMSE) of 0.54% in the frequency range of 8.7-12 kHz. The performance of EIS is also compared with an available SPAD reading which is moderately correlated with LNC. A high correlation coefficient of 0.94, a coefficient of determination of 0.89, and RMSE of 1.12% are obtained using EIS, whereas a maximum correlation coefficient of 0.72, a coefficient of determination of 0.53, and RMSE of 1.52% are obtained using SPAD for the same number of combined observations. The proposed multiple linear regression models based on EIS measurements sensitive to LNC can be used on a very local scale to develop a simple, rapid, inexpensive, and effective instrument for determining the leaf nitrogen concentrations in crops.Remote Sens. 2020, 12, 566 2 of 18 and hyperspectral imaging are mostly desirable as rapid, nondestructive, and noninvasive methods for predicting nitrogen in the leaves [4][5][6]. Leaf chlorophyll content is a key indicator of plant physiological status. It is correlated with leaf nitrogen concentration, although the correlation depends on soil condition, plant species, and the stage of growth [7][8][9][10][11][12]. The nitrogen status in the leaves can also be determined by analyzing the distribution of the color components of an image of a single leaf or group of plants.In one study, leaf nitrogen status was determined by hyperspectral indices which were based on various algorithms [4]. In other studies, hyperspectral remote sensing images and spectral indices were used to assess the leaf nitrogen status and reflectance spectra of a wheat and reed canopy, as shown by the variation in wavelengths [13][14][15]. Hyperspectral data, fluorescence, and near-infrared spectroscopy, detected via digital cameras and satellite-mounted hyperspectral sensors, have been developed and studie...

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Determination of Leaf Nitrogen Concentrations Using Electrical Impedance Spectroscopy in Multiple Crops

2020

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“…The RMSE and MAPE of the retrieved LAI and LNC with hyperspectral and multispectral data is shown in Table Ⅵ. The multispectral data was acquired by convoluting hyperspectral data with the spectral response function of the Pika L sensor, and vegetation indices such as the NDVI, Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI), and RED-EDGE NDVI [64] were calculated by multispectral data. To obtain the LAI and LNC, empirical linear relationships between the LAI and LNC of winter wheat and these vegetation indices were established, which were based on local training, without considering the bidirectional reflectance distribution function (BRDF) characteristics of the canopy and background.…”

Section: Advantages Of Hyperspectral Datamentioning

confidence: 99%

Integration of Crop Growth Model and Random Forest for Winter Wheat Yield Estimation From UAV Hyperspectral Imagery

Yang

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Accurate and timely crop yield estimation is critical for food security and sustainable development. The rapid development of unmanned aerial vehicles (UAVs) offers a new approach to acquire high spatio-temporal resolution imagery of farmland at a low cost. In order to realize the full potential of UAV platform and sensor, machine learning has been introduced to estimate crop yield, but the shortages of field measurements have troubled researchers. In this study, the CW-RF model, a new wheat yield estimation model suitable for the North China Plain, was established using random forest, and the crop growth model (the CERES-Wheat model) was chosen to simulate abundant training samples for random forest at field plot scale. According to CERES-Wheat model simulation, the leaf area index (LAI) and leaf nitrogen content (LNC) at the wheat jointing and heading stages were selected as the most sensitive parameters, and were retrieved from UAV hyperspectral imagery using the directional second derivative (DSD) and angular insensitivity vegetation index (AIVI) methods respectively. Then the retrieved LAI and LNC results were input into the CW-RF model to estimate winter wheat yield. The field validation in Luohe, Henan showed that the root mean squared error (RMSE) of the retrieved LAI and LNC were 6.27% and 12.17% at jointing stages, 9.21% and 13.64% at heading stages, respectively. The RMSE of estimated yield was 1,008.08 kg/ha, and the mean absolute percent error (MAPE) of estimated yield was 9.36%, demonstrating the available of the CW-RF model in wheat yield estimation at field plot scale. Apart from Luohe, validations in some other fields (e.g. Xiaotangshan, Beijing), prove the wide applicability of the CW-RF model. In addition, the UAV hyperspectral data were found to significantly improve the retrieval accuracy, and further improve CW-RF model estimation accuracy. In conclusion, this study showed that the CERES-Wheat model simulation can be important data source for machine learning-based wheat yield estimation model at field plot scale, and the hyperspectral sensor mounted on a UAV is a feasible remote sensing data acquisition mode for winter wheat growth monitoring and yield estimation.Index Terms-wheat yield estimation, the CERES-Wheat model, random forest, unmanned aerial vehicle (UAV), hyperspectral remote sensing

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“…The spatial variability of GPC has been successfully estimated from NDVI and other vegetation indices using different remote sensing platforms [19][20][21][22][23], especially during latter stages of crop development, namely anthesis [24,25]. Wheat yield is often more strongly related to vegetation indices that are integrated across the growing-season to capture the full period of canopy development and thereby crop carbon uptake [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

The spatial dynamics of wheat yield and protein content at the field scale

Stoy¹,

Khan²,

Wipf³

et al. 2021

Preprint

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Wheat is a staple crop that is critical for feeding a hungry and growing planet, but its nutritive value has declined as global temperatures have warmed. The price offered to producers depends not only on yield but also grain protein content (GPC), which are often negatively related at the field scale but can positively covary depending in part on management strategies, emphasizing the need to predict their variability within individual fields. We measured yield and GPC in a winter wheat field in Sun River, Montana, USA and tested the ability of normalized difference vegetation index (NDVI) measurements from an unpiloted aerial vehicle (UAV) on spatial scales of ~10 cm and from Landsat on spatial scales of 30 m to predict them. Landsat observations were poorly related to wheat measurements. A multiple linear model using information from four (three) UAV flyovers was selected as the most parsimonious and predicted 26% (40%) of the variability in wheat yield (GPC). We sought to understand the optimal spatial scale for interpreting UAV observations given that the ~ 10 cm pixels yielded more than 12 million measurements at far finer resolution than the 12 m scale of the harvester. The variance in NDVI observations was ‘averaged out’ at larger pixel sizes but only ~ 20% of the total variance was averaged out at the spatial scale of the harvester on some measurement dates. Spatial averaging to the scale of the harvester also made little difference in the total information content of NDVI fit using Beta distributions as quantified using the Kullback-Leibler divergence. Radially-averaged power spectra of UAV-measured NDVI revealed relatively steep power law relationships with exponentially less variance at finer spatial scales. Results suggest that larger pixels can reasonably capture the information content of within-field NDVI, but the 30 m Landsat scale is too coarse to describe some of the key features of the field, which are consistent with topography, historic management practices, and edaphic variability. Future research should seek to determine an ‘optimum’ spatial scale for NDVI observations that minimizes effort (and therefore cost) while maintaining the ability of producers to make management decisions that positively impact yield and GPC.

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

Cited by 42 publications

References 65 publications

Determination of Leaf Nitrogen Concentrations Using Electrical Impedance Spectroscopy in Multiple Crops

Determination of Leaf Nitrogen Concentrations Using Electrical Impedance Spectroscopy in Multiple Crops

Integration of Crop Growth Model and Random Forest for Winter Wheat Yield Estimation From UAV Hyperspectral Imagery

The spatial dynamics of wheat yield and protein content at the field scale

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