Estimating leaf carotenoid content in vineyards using high resolution hyperspectral imagery acquired from an unmanned aerial vehicle (UAV)

Zarco‐Tejada, Pablo J.; Guillén-Climent, M. L.; Hernández-Clemente, Rocío; Catalina, A.; Rebollo, Miguel Ángel González; Martín, Pedro

doi:10.1016/j.agrformet.2012.12.013

Cited by 254 publications

(136 citation statements)

References 46 publications

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“…Johnson et al [76] proposed one of the first applications where different sensors were used for determining measures related to chlorophyll function and photosynthetic activity, LAI, and plant health status (among other variables) to mapping vigor differences within fields. More recently, Zarco-Tejada et al [52,[77][78][79][80] demonstrated the potential for monitoring specific variables such as crop water stress index, photosynthetic activity, and carotenoid content in vineyards using multispectral, hyperspectral, and thermal cameras.…”

Section: Vegetation Monitoring and Precision Agriculturementioning

confidence: 99%

On the Use of Unmanned Aerial Systems for Environmental Monitoring

et al. 2018

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Environmental monitoring plays a central role in diagnosing climate and management impacts on natural and agricultural systems; enhancing the understanding of hydrological processes; optimizing the allocation and distribution of water resources; and assessing, forecasting, and even preventing natural disasters. Nowadays, most monitoring and data collection systems are based upon a combination of ground-based measurements, manned airborne sensors, and satellite observations. These data are utilized in describing both small-and large-scale processes, but have spatiotemporal constraints inherent to each respective collection system. Bridging the unique spatial and temporal divides that limit current monitoring platforms is key to improving our understanding of environmental systems. In this context, Unmanned Aerial Systems (UAS) have considerable potential to radically improve environmental monitoring. UAS-mounted sensors offer an extraordinary opportunity to bridge the existing gap between field observations and traditional air-and space-borne remote sensing, by providing high spatial detail over relatively large areas in a cost-effective way and an entirely new capacity for enhanced temporal retrieval. As well as showcasing recent advances in the field, there is also a need to identify and understand the potential limitations of UAS technology. For these platforms to reach their monitoring potential, a wide spectrum of unresolved issues and application-specific challenges require focused community attention. Indeed, to leverage the full potential of UAS-based approaches, sensing technologies, measurement protocols, postprocessing techniques, retrieval algorithms, and evaluation techniques need to be harmonized. The aim of this paper is to provide an overview of the existing research and applications of UAS in natural and agricultural ecosystem monitoring in order to identify future directions, applications, developments, and challenges.

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Section: Vegetation Monitoring and Precision Agriculturementioning

confidence: 99%

On the Use of Unmanned Aerial Systems for Environmental Monitoring

et al. 2018

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“…The rising availability of spectroradiometers, with the ability to provide hyperspectral reflectance data, i.e., data collected in very narrow bandwidths (1-10 nm) and continuously over the spectral range [19], has contributed to increasing the interest in using vegetation indices (VI) based on narrowband or hyperspectral data. Most applications of such narrowband indices are focused on the study of leaf pigments concentration (e.g., [20,21]) but studies related with crop water status and plant water stress have also been published (e.g., [11,22]). …”

Section: Introductionmentioning

confidence: 99%

Predicting Grapevine Water Status Based on Hyperspectral Reflectance Vegetation Indices

Pôças

Rodrigues²,

Gonçalves

et al. 2015

Remote Sensing

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Several vegetation indices (VI) derived from handheld spectroradiometer reflectance data in the visible spectral region were tested for modelling grapevine water status estimated by the predawn leaf water potential (Ψpd). The experimental trial was carried out in a vineyard in Douro wine region, Portugal. A statistical approach was used to evaluate which VI and which combination of wavelengths per VI allows the best correlation between VIs and Ψpd. A linear regression was defined using a parameterization dataset. The correlation analysis between Ψpd and the VIs computed with the standard formulation showed relatively poor results, with values for squared Pearson correlation coefficient (r 2 ) smaller than 0.67. However, the results of r 2 highly improved for all VIs when computed with the selected best combination of wavelengths (optimal VIs). The optimal Visible Atmospherically Resistant Index (VARI) and Normalized Difference Greenness Vegetation Index (NDGI) showed the higher r 2 and stability index results. The equations obtained through the regression between measured Ψpd (Ψpd_obs) and optimal VARI and between Ψpd_obs and optimal NDGI when using the parameterization dataset were adopted for predicting Ψpd using a testing dataset. The comparison of Ψpd_obs with Ψpd predicted based on VARI led to R 2 = 0.79 and a regression coefficient b = 0.96. Similar R 2 was achieved for the prediction based on NDGI, but b was smaller (b = 0.93). Results obtained allow the future use of optimal VARI and NDGI for estimating Ψpd, supporting vineyards irrigation management.

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“…In precision agriculture, remote sensing has been used to monitor crop growth and health by computing a range of spectral vegetation indices [2][3][4][5][6][7][8][9]. However, recently more attention has been given to the use of crop height modelling for yield estimation [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Poppy Crop Height and Capsule Volume Estimation from a Single UAS Flight

et al. 2017

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Abstract:The objective of this study was to estimate poppy plant height and capsule volume with remote sensing using an Unmanned Aircraft System (UAS). Data were obtained from field measurements and UAS flights over two poppy crops at Cambridge and Cressy in Tasmania. Imagery acquired from the UAS was used to produce dense point clouds using structure from motion (SfM) and multi-view stereopsis (MVS) techniques. Dense point clouds were used to generate a digital surface model (DSM) and orthophoto mosaic. An RGB index was derived from the orthophoto to extract the bare ground spaces. This bare ground space mask was used to filter the points on the ground, and a digital terrain model (DTM) was interpolated from these points. Plant height values were estimated by subtracting the DSM and DTM to generate a Crop Height Model (CHM). UAS-derived plant height (PH) and field measured PH in Cambridge were strongly correlated with R 2 values ranging from 0.93 to 0.97 for Transect 1 and Transect 2, respectively, while at Cressy results from a single flight provided R 2 of 0.97. Therefore, the proposed method can be considered an important step towards crop surface model (CSM) generation from a single UAS flight in situations where a bare ground DTM is unavailable. High correlations were found between UAS-derived PH and poppy capsule volume (CV) at capsule formation stage (R 2 0.74), with relative error of 19.62%. Results illustrate that plant height can be reliably estimated for poppy crops based on a single UAS flight and can be used to predict opium capsule volume at capsule formation stage.

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Estimating leaf carotenoid content in vineyards using high resolution hyperspectral imagery acquired from an unmanned aerial vehicle (UAV)

Cited by 254 publications

References 46 publications

On the Use of Unmanned Aerial Systems for Environmental Monitoring

On the Use of Unmanned Aerial Systems for Environmental Monitoring

Predicting Grapevine Water Status Based on Hyperspectral Reflectance Vegetation Indices

Poppy Crop Height and Capsule Volume Estimation from a Single UAS Flight

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