Imaging chlorophyll fluorescence with an airborne narrow-band multispectral camera for vegetation stress detection

Zarco‐Tejada, Pablo J.; Berni, J.A.J.; Suárez, Lola; Sepulcre-Cantó, G.; Morales, Fermı́n; Miller, John R.

doi:10.1016/j.rse.2009.02.016

Cited by 254 publications

(160 citation statements)

References 61 publications

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“…Furthermore CASI at sensor radiances were used to estimate F760 using the method first proposed by Maier et al [33] and then applied in different studies [11,34]. Top of canopy fluorescence was computed exploiting non fluorescent targets (white and black panels, low and high reflecting soils and asphalts) present in the scene to estimate the atmospheric transmittance and path radiance inside and outside the absorption feature.…”

Section: Airborne Hyperspectral Imagery Acquisition and Processing Tementioning

confidence: 99%

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: Airborne Hyperspectral Imagery Acquisition and Processing Tementioning

confidence: 99%

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 ChlF signal can provide critical information about the growth status of plants, and therefore it has been used as an effective tool to monitor plant stress induced by air pollution [25,26], water deficit [28,29], herbicide treatment [2], and salt and drought [30]. Quantitative estimation of the fluorescence parameters for crop leaves would be of high importance in assessing the photosynthetic rates of green plants and monitoring the stress conditions of crops.…”

Section: Potential and Limitations Of Applying Model Inversion For Thmentioning

confidence: 99%

Quantitative Estimation of Fluorescence Parameters for Crop Leaves with Bayesian Inversion

et al. 2015

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Abstract:In this study, backward and forward fluorescence radiance within the emission spectrum of 640-850 nm were measured for leaves of soybean, cotton, peanut and wheat using a hyperspectral spectroradiometer coupled with an integration sphere. Fluorescence parameters of crop leaves were retrieved from the leaf hyperspectral measurements by inverting the FluorMODleaf model, a leaf-level fluorescence model able to simulate chlorophyll fluorescence spectra for both sides of leaves. This model is based on the widely used and validated PROSPECT (leaf optical properties) model. Firstly, a sensitivity analysis of the FluorMODleaf model was performed to identify and quantify influential parameters to assist the strategy for the inversion. Implementation of the Extended Fourier Amplitude Sensitivity Test (EFAST) method showed that the leaf chlorophyll content and the OPEN ACCESSRemote Sens. 2015, 7 14180 fluorescence lifetimes of photosystem I (PSI) and photosystem II (PSII) were the most sensitive parameters among all eight inputs of the FluorMODleaf model. Based on results of sensitivity analysis, the FluorMODleaf model was inverted using the leaf fluorescence spectra measured from both sides of crop leaves. In order to achieve stable inversion results, the Bayesian inference theory was applied. The relative absorption cross section of PSI and PSII and the fluorescence lifetimes of PSI and PSII of the FluorMODleaf model were retrieved with the Bayesian inversion approach. Results showed that the coefficient of determination (R 2 ) and root mean square error (RMSE) between the fluorescence signal reconstructed from the inverted fluorescence parameters and measured in the experiment were 0.96 and 3.14 × 10 −6 W· m −2 · sr −1 · nm −1 , respectively, for backward fluorescence, and 0.92 and 3.84 × 10 −6 W· m −2 · sr −1 · nm −1 for forward fluorescence. Based on results, the inverted values of the fluorescence parameters were analyzed, and the potential of this method was investigated.

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“…This increase has been observed not only in practical applications, but also in the peer-reviewed literature. Two recent special issues on UAS for environmental remote sensing applications in the journals Geocarto International [1] and GIScience and Remote Sensing [2] as well as other recent publications reflect the growing acceptance by the remote sensing community of UAS as suitable platforms for acquiring quality imagery and other data for various application such as wildfire mapping [3,4], arctic sea ice and atmospheric studies [5], detection of invasive species [6], rangeland mapping [7][8][9], hydrology and riparian applications [10][11][12], and precision agriculture [13][14][15][16]. Due to limited payload capacities on small unmanned aerial vehicles (<50 kg), consumer digital cameras are often used.…”

Section: Introductionmentioning

confidence: 99%

“…A higher quality multispectral sensor (MCA-6, Tetracam, Inc.) was used for retrieving various biophysical parameters and detecting water stress in orchard crops and the researchers concluded that quantitative multispectral remote sensing could be conducted with small UAS, although the area covered was limited by the endurance of the UAS [15,16]. Turner and Lucieer [26] presented promising preliminary results using imagery acquired from an unmanned helicopter equipped with visible, thermal, and multispectral sensors for precision viticulture applications.…”

Section: Introductionmentioning

confidence: 99%

Multispectral Remote Sensing from Unmanned Aircraft: Image Processing Workflows and Applications for Rangeland Environments

Goforth²,

et al. 2011

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Using unmanned aircraft systems (UAS) as remote sensing platforms offers the unique ability for repeated deployment for acquisition of high temporal resolution data at very high spatial resolution. Multispectral remote sensing applications from UAS are reported in the literature less commonly than applications using visible bands, although light-weight multispectral sensors for UAS are being used increasingly. . In this paper, we describe challenges and solutions associated with efficient processing of multispectral imagery to obtain orthorectified, radiometrically calibrated image mosaics for the purpose of rangeland vegetation classification. We developed automated batch processing methods for file conversion, band-to-band registration, radiometric correction, and orthorectification. An object-based image analysis approach was used to derive a species-level vegetation classification for the image mosaic with an overall accuracy of 87%. We obtained good correlations between: (1) ground and airborne spectral reflectance (R 2 = 0.92); and (2) spectral reflectance derived from airborne and WorldView-2 satellite data for selected vegetation and soil targets. UAS-acquired multispectral imagery provides quality high resolution information for rangeland applications with the potential for upscaling the data to larger areas using high resolution satellite imagery.

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Imaging chlorophyll fluorescence with an airborne narrow-band multispectral camera for vegetation stress detection

Cited by 254 publications

References 61 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

Quantitative Estimation of Fluorescence Parameters for Crop Leaves with Bayesian Inversion

Multispectral Remote Sensing from Unmanned Aircraft: Image Processing Workflows and Applications for Rangeland Environments

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