Mapping crop water stress index in a ‘Pinot-noir’ vineyard: comparing ground measurements with thermal remote sensing imagery from an unmanned aerial vehicle

Bellvert, Joaquim; Zarco‐Tejada, Pablo J.; Girona, J.; Fereres, E.

doi:10.1007/s11119-013-9334-5

Cited by 307 publications

(231 citation statements)

References 20 publications

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“…The optimum time to acquire thermal images is around noon, confirming previous results obtained by Bellvert et al. [45] in vineyards. Better performances of ΔT around noon can be also partially explained considering that in the morning and afternoon images, the mutual shading of leaves increases causing different degrees of stomatal conductance within the canopy and lowering the relationship between ΔT and water stress.…”

Section: Synthesis Of Thermal and Optical Datasupporting

confidence: 88%

Discriminating Irrigated and Rainfed Maize with Diurnal Fluorescence and Canopy Temperature Airborne Maps

Rossini

Panigada

Cilia

et al. 2015

IJGI

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This study evaluates the potential of airborne remote sensing images to detect water stress in maize. Visible and near infrared CASI (Itres Research Ltd., Calgary, AL, Canada) and thermal AHS-160 (Sensytech Inc., Beverly, MA, USA) data were acquired at three different times during the day on a maize field (Zea mays L.) grown with three different irrigation treatments. An intensive field campaign was also conducted concurrently with image acquisition to measure leaf ecophysiological parameters and the leaf area index. The analysis of the field data showed that maize plants were experiencing moderate to severe water stress in rainfed plots and a weaker stress condition in the plots with a water deficit imposed between stem elongation and flowering. Vegetation indices including the normalized difference vegetation index (NDVI) and the photochemical reflectance index (PRI) computed from the CASI images, sun-induced chlorophyll fluorescence (F760) and canopy temperature (Tc) showed different performances in describing the water stress during the day. During the OPEN ACCESS ISPRS Int. J. Geo-Inf. 2015, 4 627 morning overpass, NDVI was the index with the highest discriminant power due to the sensitivity of NDVI to maize canopy structure, affected by the water irrigation treatment. As the day progressed, processes related to heat dissipation through plant transpiration became more and more important and at midday Tc showed the best performances. Furthermore, Tc retrieved from the midday image was the only index able to distinguish all the three classes of water status. Finally, during the afternoon, PRI and F760 showed the best performances. These results demonstrate the feasibility to detect water stress using thermal and optical airborne data, pointing out the importance of careful planning of the airborne surveys as a function of the specific aims of the study.

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Section: Synthesis Of Thermal and Optical Datasupporting

confidence: 88%

Discriminating Irrigated and Rainfed Maize with Diurnal Fluorescence and Canopy Temperature Airborne Maps

Rossini

Panigada

Cilia

et al. 2015

IJGI

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“…The crop water stress index (CWSI) [91,92] calculated from leaf water potential can be used to determine the required frequency, timing, and duration of watering. In this regard, the CWSI, derived with a UAS equipped with a thermal camera, is frequently adopted to quantify the physiological status of plants, and, more specifically, leaf water potential in experimental vineyards or orchards [52,80,[93][94][95][96]. The derived CWSI maps can serve as important inputs for precision irrigation.…”

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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“…Bellvert [15] evaluated the correlation between CWSI and leaf water potential in a 'Pinot Noir' vineyard, reporting that for an accurate CWSI analysis the minimum spatial resolution of images should be 0.3 m pixel −1 .…”

Section: Introductionmentioning

confidence: 99%

Estimation of Water Stress in Grapevines Using Proximal and Remote Sensing Methods

et al. 2018

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Abstract:In light of climate change and its impacts on plant physiology, optimizing water usage and improving irrigation practices play a crucial role in crop management. In recent years, new optical remote sensing techniques have become widespread since they allow a non-invasive evaluation of plant water stress dynamics in a timely manner. Unmanned aerial vehicles (UAV) currently represent one of the most advanced platforms for remote sensing applications. In this study, remote and proximal sensing measurements were compared with plant physiological variables, with the aim of testing innovative services and support systems to farmers for optimizing irrigation practices and scheduling. The experiment, conducted in two vineyards located in Sardinia, Italy, consisted of two regulated deficit irrigation (RDI) treatments and two reference treatments maintained under stress and well-watered conditions. Indicators of crop water status (Crop Water Stress Index-CWSI-and linear thermal index) were calculated from UAV images and ground infrared thermal images and then related to physiological measurements. The CWSI values for moderate water deficit (RDI-1) were 0.72, 0.28 and 0.43 for 'Vermentino', 'Cabernet' and 'Cagnulari' respectively, while for severe (RDI-2) water deficit the values were 0.90, 0.34 and 0.51. The highest differences for net photosynthetic rate (Pn) and stomatal conductance (Gs) between RDI-1 and RDI-2 were observed in 'Vermentino'. The highest significant correlations were found between CWSI with Pn (R = −0.80), with Φ PSII (R = −0.49) and with Fv'/Fm' (R = −0.48) on 'Cagnulari', while a unique significant correlation between CWSI and non-photochemical quenching (NPQ) (R = 0.47) was found on 'Vermentino'. Pn, as well as the efficiency of light use by the photosystem II (PSII), declined under stress conditions and when CWSI values increased. Under the experimental water stress conditions, grapevines were able to recover their efficiency during the night, activating a photosynthetic protection mechanism such as thermal energy dissipation (NPQ) to prevent irreversible damage to the photosystem. The results presented here demonstrate that CWSI values derived from remote and proximal sensors could be valuable indicators for the assessment of the spatial variability of crop water status in Mediterranean vineyards.

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Mapping crop water stress index in a ‘Pinot-noir’ vineyard: comparing ground measurements with thermal remote sensing imagery from an unmanned aerial vehicle

Cited by 307 publications

References 20 publications

Discriminating Irrigated and Rainfed Maize with Diurnal Fluorescence and Canopy Temperature Airborne Maps

Discriminating Irrigated and Rainfed Maize with Diurnal Fluorescence and Canopy Temperature Airborne Maps

On the Use of Unmanned Aerial Systems for Environmental Monitoring

Estimation of Water Stress in Grapevines Using Proximal and Remote Sensing Methods

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