Development and implementation of a multiscale biomass model using hyperspectral vegetation indices for winter wheat in the North China Plain

Gnyp, Martin L.; Bareth, Georg; Li, Fēi; Lenz-Wiedemann, Victoria I. S.; Koppe, Wolfgang; Miao, Yuxin; Hennig, Simon; Liu, Jia; Laudien, Rainer; Chen, Xinping; Zhang, Fusuo

doi:10.1016/j.jag.2014.05.006

Cited by 64 publications

(57 citation statements)

References 45 publications

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“…A later saturation of these VIs aligns well with other studies [53,56]. Similarly, the REIP did not show any saturation effects in the pre-anthesis and yielded very good results for dry and fresh biomass.…”

Section: Biomass Estimation From Vegetation Indicessupporting

confidence: 91%

“…Nevertheless, across the whole observed period, the performance of the REIP also decreased due to saturation. The importance of the NIR domain for biomass estimation aligns with other studies [15,16,53,56] and should be further investigated. Similar to PH, the NIR VIs performed better for dry than for fresh biomass while the VISNIR VIs generally performed better with fresh biomass.…”

Section: Biomass Estimation From Vegetation Indicessupporting

confidence: 80%

“…The NDVI, RDVI, and REIP use wavelengths in the visible and near-infrared domain (VISNIR VIs), the GnyLi and NRI use wavelengths in the near-infrared domain (NIR VIs), while the RGBVI uses wavelengths in the visible domain (VIS VI). The formulas of the VIs used in this study are given in Table 3, [47,49,51,53,54,56]. …”

Section: Spectral Datamentioning

confidence: 99%

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Fusion of Plant Height and Vegetation Indices for the Estimation of Barley Biomass

2015

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Plant biomass is an important parameter for crop management and yield estimation. However, since biomass cannot be determined non-destructively, other plant parameters are used for estimations. In this study, plant height and hyperspectral data were used for barley biomass estimations with bivariate and multivariate models. During three consecutive growing seasons a terrestrial laser scanner was used to establish crop surface models for a pixel-wise calculation of plant height and manual measurements of plant height confirmed the results (R 2 up to 0.98). Hyperspectral reflectance measurements were conducted with a field spectrometer and used for calculating six vegetation indices (VIs), which have been found to be related to biomass and LAI: GnyLi, NDVI, NRI, RDVI, REIP, and RGBVI. Furthermore, biomass samples were destructively taken on almost the same dates. Linear and exponential biomass regression models (BRMs) were established for evaluating plant height and VIs as estimators of fresh and dry biomass. Each BRM was established for the whole observed period and pre-anthesis, which is important for management decisions. Bivariate BRMs supported plant height as a strong estimator (R 2 up to 0.85), whereas BRMs based on individual VIs showed varying performances (R 2 : 0.07-0.87). Fused approaches, where plant height and one VI were used for establishing multivariate BRMs, yielded improvements in some cases (R 2 up to 0.89). Overall, this study reveals the potential of remotely-sensed plant parameters for estimations of barley biomass. Moreover, it is a first step towards the fusion of 3D spatial and spectral measurements for improving non-destructive biomass estimations. OPEN ACCESSRemote Sens. 2015, 7 11450

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Section: Biomass Estimation From Vegetation Indicessupporting

confidence: 91%

Section: Biomass Estimation From Vegetation Indicessupporting

confidence: 80%

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Fusion of Plant Height and Vegetation Indices for the Estimation of Barley Biomass

2015

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“…Consequently, the performance of the RGBVIuncal. should be investigated against real agronomic parameters rather than the NDVI in a multiscale approach like it is proposed by Gnyp et al (2014). Additionally, the potential of polygon grids for zonal statistics should be tested on this field level (Bareth et al 2016).…”

Section: Discussionmentioning

confidence: 99%

“…Since decades, the development and application of vegetation indices (VI) to monitor crop status is in the focus of numerous research activities (Thenkabail et al, 2000;Tucker 1979). In general, these VIs are developed from multi-or hyperspectral field or remote sensing data (Gnyp et al, 2014;Koppe et al, 2010;Li et al, 2008). In the last five years, the image acquisition with UAVs exponentially increased (Colomina and Molina 2014;Zhang and Kovacs 2012).…”

Section: Introductionmentioning

confidence: 99%

Comparison of Uncalibrated Rgbvi With Spectrometer-Based Ndvi Derived From Uav Sensing Systems on Field Scale

Bareth

Bolten

Gnyp

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The development of UAV-based sensing systems for agronomic applications serves the improvement of crop management. The latter is in the focus of precision agriculture which intends to optimize yield, fertilizer input, and crop protection. Besides, in some cropping systems vehicle-based sensing devices are less suitable because fields cannot be entered from certain growing stages onwards. This is true for rice, maize, sorghum, and many more crops. Consequently, UAV-based sensing approaches fill a niche of very high resolution data acquisition on the field scale in space and time. While mounting RGB digital compact cameras to low-weight UAVs (< 5 kg) is well established, the miniaturization of sensors in the last years also enables hyperspectral data acquisition from those platforms. From both, RGB and hyperspectral data, vegetation indices (VIs) are computed to estimate crop growth parameters. In this contribution, we compare two different sensing approaches from a low-weight UAV platform (< 5 kg) for monitoring a nitrogen field experiment of winter wheat and a corresponding farmers' field in Western Germany. (i) A standard digital compact camera was flown to acquire RGB images which are used to compute the RGBVI and (ii) NDVI is computed from a newly modified version of the Yara N-Sensor. The latter is a well-established tractor-based hyperspectral sensor for crop management and is available on the market since a decade. It was modified for this study to fit the requirements of UAV-based data acquisition. Consequently, we focus on three objectives in this contribution: (1) to evaluate the potential of the uncalibrated RGBVI for monitoring nitrogen status in winter wheat, (2) investigate the UAV-based performance of the modified Yara N-Sensor, and (3) compare the results of the two different UAV-based sensing approaches for winter wheat.

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Remotely assessing above-ground fresh biomass weight of wheat based on the combinations of pair vegetation indexes from HJ-CCD images

et al. 2018

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Development and implementation of a multiscale biomass model using hyperspectral vegetation indices for winter wheat in the North China Plain

Cited by 64 publications

References 45 publications

Fusion of Plant Height and Vegetation Indices for the Estimation of Barley Biomass

Fusion of Plant Height and Vegetation Indices for the Estimation of Barley Biomass

Comparison of Uncalibrated Rgbvi With Spectrometer-Based Ndvi Derived From Uav Sensing Systems on Field Scale

Remotely assessing above-ground fresh biomass weight of wheat based on the combinations of pair vegetation indexes from HJ-CCD images

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