Exploiting the centimeter resolution of UAV multispectral imagery to improve remote-sensing estimates of canopy structure and biochemistry in sugar beet crops

Jay, Sylvain; Baret, Frédéric; Dutartre, Dan; Malatesta, Ghislain; Héno, Stéphanie; Comar, Alexis; Weiss, Marie; Maupas, Fabienne

doi:10.1016/j.rse.2018.09.011

Cited by 155 publications

(115 citation statements)

References 76 publications

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“…Due to the influence of the dark green pixels from shaded leaves, the mND blue yielded higher R 2 in the forward viewing direction compared to the backward direction. This was similar to the finding reported by Jay et al (2019) on quantifying the biochemistry in sugar beet crops. In contrast, the other two VIs (CI red-edge , CI green) appeared to be much less sensitive to VZA.…”

Section: Difference In Estimation Accuracy Between Vissupporting

confidence: 92%

“…The visible atmospherically resistant index (VARI) derived from the visible region has proved to be sensitive to vegetation fraction (Gitelson et al, 2002) and correlate well with LAI and biomass (Gitelson et al, 2003b). The modified normalized difference index with a blue band (mND blue ) was proposed by Jay et al (2019) as a strong indicator of crop chlorophyll content with weak effect of soil background. The green band chlorophyll index (CI green ) and the red edge chlorophyll index (CI red-edge ) have proved to be accurate predictors of leaf (Gitelson et al, 2003a;Schlemmer et al, 2013) and canopy chlorophyll contents (Gitelson 2005;Schlemmer et al, 2013;Clevers et al, 2017).…”

Section: Image Pre-processing and Spectral Vegetation Index Calculationmentioning

confidence: 99%

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Estimation of Nitrogen Nutrition Status in Winter Wheat From Unmanned Aerial Vehicle Based Multi-Angular Multispectral Imagery

Wang

Zhang

et al. 2019

Front. Plant Sci.

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Rapid, non-destructive and accurate detection of crop N status is beneficial for optimized fertilizer applications and grain quality prediction in the context of precision crop management. Previous research on the remote estimation of crop N nutrition status was mostly conducted with ground-based spectral data from nadir or oblique angles. Few studies investigated the performance of unmanned aerial vehicle (UAV) based multispectral imagery in regular nadir views for such a purpose, not to mention the feasibility of oblique or multi-angular images for improved estimation. This study employed a UAV-based five-band camera to acquire multispectral images at seven view zenith angles (VZAs) (0°, ± 20°, ± 40°and ±60°) for three critical growth stages of winter wheat. Four representative vegetation indices encompassing the Visible Atmospherically Resistant Index (VARI), Red edge Chlorophyll Index (CI red-edge ), Green band Chlorophyll Index (CI green ), Modified Normalized Difference Vegetation Index with a blue band (mND blue ) were derived from the multi-angular images. They were used to estimate the N nutrition status in leaf nitrogen concentration (LNC), plant nitrogen concentration (PNC), leaf nitrogen accumulation (LNA), and plant nitrogen accumulation (PNA) of wheat canopies for a combination of treatments in N rate, variety and planting density. The results demonstrated that the highest accuracy for single-angle images was obtained with CI green for LNC from a VZA of -60°(R 2 = 0.71, RMSE = 0.34%) and PNC from a VZA of -40°(R 2 = 0.36, RMSE = 0.29%). When combining an off-nadir image (-40°) and the 0°i mage, the accuracy of PNC estimation was substantially improved (CI red-edge : R 2 = 0.52, RMSE = 0.28%). However, the use of dual-angle images did not significantly increase the estimation accuracy for LNA and PNA compared to the use of single-angle images. Our findings suggest that it is important and practical to use oblique images from a UAV-based Frontiers in Plant Science | www.frontiersin.org

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Section: Difference In Estimation Accuracy Between Vissupporting

confidence: 92%

Section: Image Pre-processing and Spectral Vegetation Index Calculationmentioning

confidence: 99%

Estimation of Nitrogen Nutrition Status in Winter Wheat From Unmanned Aerial Vehicle Based Multi-Angular Multispectral Imagery

Wang

Zhang

et al. 2019

Front. Plant Sci.

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“…Furthermore, high cost, low revisit frequency and potential cloud occurrence limit the suitability of satellite remote sensing in agriculture, while operational complexity presents a major constraint for manned airborne platforms [121][122][123]. Indeed, high spatial resolution images collected at low altitude have favorable signal-to-noise ratio, and it is possible to eliminate soil and shadow pixels with high confidence [40,[124][125][126]. Additionally, image information (radiance and reflectance) extracted from pure vegetation pixels is likely to reduce the effects of shadows and background soils.…”

Section: Model Scalability and Transferabilitymentioning

confidence: 99%

Dual Activation Function-Based Extreme Learning Machine (ELM) for Estimating Grapevine Berry Yield and Quality

Maimaitiyiming

Sagan

Kwasniewski³

2019

Remote Sensing

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Reliable assessment of grapevine productivity is a destructive and time-consuming process. In addition, the mixed effects of grapevine water status and scion-rootstock interactions on grapevine productivity are not always linear. Despite the potential opportunity of applying remote sensing and machine learning techniques to predict plant traits, there are still limitations to previously studied techniques for vine productivity due to the complexity of the system not being adequately modeled. During the 2014 and 2015 growing seasons, hyperspectral reflectance spectra were collected using a handheld spectroradiometer in a vineyard designed to investigate the effects of irrigation level (0%, 50%, and 100%) and rootstocks (1103 Paulsen, 3309 Couderc, SO4 and Chambourcin) on vine productivity. To assess vine productivity, it is necessary to measure factors related to fruit ripeness and not just yield, as an over cropped vine may produce high-yield but poor-quality fruit. Therefore, yield, Total Soluble Solids (TSS), Titratable Acidity (TA) and the ratio TSS/TA (maturation index, IMAD) were measured. A total of 20 vegetation indices were calculated from hyperspectral data and used as input for predictive model calibration. Prediction performance of linear/nonlinear multiple regression methods and Weighted Regularized Extreme Learning Machine (WRELM) were compared with our newly developed WRELM-TanhRe. The developed method is based on two activation functions: hyperbolic tangent (Tanh) and rectified linear unit (ReLU). The results revealed that WRELM and WRELM-TanhRe outperformed the widely used multiple regression methods when model performance was tested with an independent validation dataset. WRELM-TanhRe produced the highest prediction accuracy for all the berry yield and quality parameters (R2 of 0.522–0.682 and RMSE of 2–15%), except for TA, which was predicted best with WRELM (R2 of 0.545 and RMSE of 6%). The results demonstrate the value of combining hyperspectral remote sensing and machine learning methods for improving of berry yield and quality prediction.

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“…The sensitivity of the green and infrared bands for the estimation of LCC has previously been demonstrated [9,27]. Specifically, the Green Chlorophyll Vegetation Index (near infra-red/green; [31]) has successfully been applied in several studies for deriving crop chlorophyll content [24,[53][54][55]. Research by Wang et al [56] involved the analysis of winter wheat spectral reflectance under different N applications and demonstrated that bands centred around the green and near-infrared spectral regions were sensitive to the treatments, whereas the blue band was comparatively less sensitive.…”

Section: Sentinel-2 Bands and Gpr Modelling For Parameter Retrievalsmentioning

confidence: 99%

The Value of Sentinel-2 Spectral Bands for the Assessment of Winter Wheat Growth and Development

et al. 2019

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Leaf Area Index (LAI) and chlorophyll content are strongly related to plant development and productivity. Spatial and temporal estimates of these variables are essential for efficient and precise crop management. The availability of open-access data from the European Space Agency's (ESA) Sentinel-2 satellite-delivering global coverage with an average 5-day revisit frequency at a spatial resolution of up to 10 metres-could provide estimates of these variables at unprecedented (i.e., sub-field) resolution. Using synthetic data, past research has demonstrated the potential of Sentinel-2 for estimating crop variables. Nonetheless, research involving a robust analysis of the Sentinel-2 bands for supporting agricultural applications is limited. We evaluated the potential of Sentinel-2 data for retrieving winter wheat LAI, leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC). In coordination with destructive and non-destructive ground measurements, we acquired multispectral data from an Unmanned Aerial Vehicle (UAV)-mounted sensor measuring key Sentinel-2 spectral bands (443 to 865 nm). We applied Gaussian processes regression (GPR) machine learning to determine the most informative Sentinel-2 bands for retrieving each of the variables. We further evaluated the GPR model performance when propagating observation uncertainty. When applying the best-performing GPR models without propagating uncertainty, the retrievals had a high agreement with ground measurements-the mean R 2 and normalised root-mean-square error (NRMSE) were 0.89 and 8.8%, respectively. When propagating uncertainty, the mean R 2 and NRMSE were 0.82 and 11.9%, respectively. When accounting for measurement uncertainty in the estimation of LAI and CCC, the number of most informative Sentinel-2 bands was reduced from four to only two-the red-edge (705 nm) and near-infrared (865 nm) bands. This research demonstrates the value of the Sentinel-2 spectral characteristics for retrieving critical variables that can support more sustainable crop management practices. the value of LAI data for updating state variables in process-based agroecosystem models in order to improve estimates of crop yield [3][4][5][6] and land-atmosphere carbon dioxide exchanges [7,8]. On the other hand, chlorophyll is a key driver of plant light absorption and conversion to chemical energy and is, therefore, an indicator of plant health and potential gross primary productivity [9,10]. In particular, leaf chlorophyll content is linked to leaf photosynthetic capacity via the maximum rate of carboxylation (V max ). The Ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO) enzyme, which relates to V max and leaf-level carbon fixation, correlates to leaf nitrogen (N) content [11]. Since leaf N also consists of chlorophyll, plant chlorophyll is strongly correlated to leaf N [12][13][14] including that for winter wheat [15].LAI and chlorophyll content are important factors determining crop reflectance [11] and can, therefore, be estimated from optical Earth observation satellit...

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Exploiting the centimeter resolution of UAV multispectral imagery to improve remote-sensing estimates of canopy structure and biochemistry in sugar beet crops

Cited by 155 publications

References 76 publications

Estimation of Nitrogen Nutrition Status in Winter Wheat From Unmanned Aerial Vehicle Based Multi-Angular Multispectral Imagery

Estimation of Nitrogen Nutrition Status in Winter Wheat From Unmanned Aerial Vehicle Based Multi-Angular Multispectral Imagery

Dual Activation Function-Based Extreme Learning Machine (ELM) for Estimating Grapevine Berry Yield and Quality

The Value of Sentinel-2 Spectral Bands for the Assessment of Winter Wheat Growth and Development

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