G. Sepulcre‐Cantó

UCLouvain

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Pablo J. Zarco‐Tejada

Instituto de Agricultura Sostenible

Juan C. Jiménez‐Muñoz

Parc Científic de la Universitat de València

José A. Sobrino

Parc Científic de la Universitat de València

ÓP

Óscar Pérez‐Priego

University of Córdoba

Fermı́n Morales

Agrobiotechnology Institute

Pol Coppin

KU Leuven

Pieter Kempeneers

Joint Research Centre

Steve De Backer

Promotie Binnenvaart Vlaanderen

Eduardo de Miguel

Universidad Politécnica de Madrid

José A. Jiménez-Berni

Instituto de Agricultura Sostenible

Cooperative Institutions

Universitat de València

Consejo Superior de Investigaciones Científicas

University of Córdoba

Parc Científic de la Universitat de València

Joint Research Centre

Instituto de Agricultura Sostenible

University of Maryland, College Park

University of California, Davis

University of Twente

Centre National de la Recherche Scientifique

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Thermal remote sensing from Airborne Hyperspectral Scanner data in the framework of the SPARC and SEN2FLEX projects: an overview

José A. Sobrino Juan C. Jiménez‐Muñoz Pablo J. Zarco‐Tejada G. Sepulcre‐Cantó Eduardo de Miguel

Abstract. The AHS (Airborne Hyperspectral Scanner) instrument has 80 spectral bands covering the visible and near infrared (VNIR), short wave infrared (SWIR), mid infrared (MIR) and thermal infrared (TIR) spectral range. The instrument is operated by Instituto Nacional de Técnica Aerospacial (INTA), and it has been involved in several field campaigns since 2004. This paper presents an overview of the work performed with the AHS thermal imagery provided in the framework of the SPARC and SEN2FLEX campaigns, carried out respectively in 2004 and 2005 over an agricultural area in Spain. The data collected in both campaigns allowed for the first time the development and testing of algorithms for land surface temperature and emissivity retrieval as well as the estimation of evapotranspiration from AHS data. Errors were found to be around 1.5 K for land surface temperature and 1 mm/day for evapotranspiration.

VNIR

Thermal infrared

10.5194/hessd-6-4107-2009

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Citations (1)

Detección de estrés hídrico en olivar mediante datos hiperespectrales y térmicos del sensor AHS

Revista de teledetección: Revista de la Asociación Española de Teledetección (2005)

G. Sepulcre‐Cantó Pablo J. Zarco‐Tejada Juan C. Jiménez‐Muñoz José A. Sobrino Eduardo de Miguel

The Airborne Hyperspectral Scanner (AHS) was used to acquire images with 2.5 m spatiala resolution in the visible, near infrared and thermal spectral regions over an olive orchard in Cordoba (Spain) to study the spatial and temporal variability of water stress. The AHS thermal information enabled obtaining surface temperature images of the orchard at 7:30, 9:30 and 12:30 GMT in 25 july 2004. The experimental design consisted of applying three different irrigation treatments in randomly selected blocks during july, august and septemper, acquiring measurements of leaf water potential, stomatal conductance and photosynthesis to study the water stress effects on the trees. Infrared sensors IRT placed on top of the trees allowed to obtain continuously temperature measurements, providing validation data for the airborne thermal imagery. Results of this study are presented, suggesting that hyperspectral and high resolution remote sensing methods have important applicability in precision agriculture for management of controlled deficit irrigation methods.

Orchard

Water Stress

Stomatal Conductance

Thermal infrared

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Chlorophyll retrieval from canopy reflectance over orchards using hyperspectral techniques

Pieter Kempeneers Steve De Backer Pablo J. Zarco‐Tejada Stephanie Delalieux G. Sepulcre‐Cantó

This paper presents the retrieval of foliar chlorophyll from canopy reflectance, measured with the AHS hyperspectral airborne sensor over a peach orchard. First, it is shown that nutrient deficiencies that caused stress can be detected on hyperspectral spectra. Second, the chlorophyll retrieval via model inversion is found dependent on viewing and illumination conditions. Finally, a methodology is presented for a robust chlorophyll retrieval via inverse modelling using multiple angular information. During an extensive field campaign, foliar and crown reflectance have been measured with a portable field spectroradiometer. Airborne hyperspectral imagery was acquired over the orchard with the AHS hyperspectral sensor with different viewing conditions. Stress on the peach orchard was treated with iron chelates to recover from iron chlorosis conditions. Blocks of trees treated with iron chelates created a dynamic range of chlorophyll concentration as measured in leaves. A relationship was established between the measured spectra and estimated biochemical parameters via inversion of a linked directional homogeneous canopy reflectance model (ACRM) and the PROSPECT leaf model. Numerical model inversion was conducted by minimizing the difference between the measured reflectance samples and modelled values. Results were compared with a simple linear regression analysis, linking chlorophyll to the reflectance measured at the Top of Canopy.

Orchard

Spectroradiometer

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Improving evapotranspiration in land surface models by using biophysical parameters derived from MSG/SEVIRI satellite

Nicolas Ghilain A. Arboleda G. Sepulcre‐Cantó Okke Batelaan Jonas Ardö

Abstract. Vegetation parameters derived from the geostationary satellite MSG/SEVIRI have been distributed at a daily frequency since 2007 over Europe, Africa and part of South America, through the LSA-SAF facility. We propose here a method to handle two new remote sensing products from LSA-SAF, leaf area index and Fractional Vegetation Cover, noted LAI and FVC respectively, for land surface models at MSG/SEVIRI scale. The developed method relies on an ordinary least-square technique and a land cover map to estimate LAI for each model plant functional types of the model spatial unit. The method is conceived to be applicable for near-real time applications at continental scale. Compared to monthly vegetation parameters from a vegetation database commonly used in numerical weather predictions (ECOCLIMAP-I), the new remote sensing products allows a better monitoring of the spatial and temporal variability of the vegetation, including inter-annual signals, and a decreased uncertainty on LAI to be input into land surface models. We assess the impact of using LSA-SAF vegetation parameters compared to ECOCLIMAP-I in the land surface model H-TESSEL at MSG/SEVIRI scale. Comparison with in-situ observations in Europe and Africa shows that the results on evapotranspiration are mostly improved, and especially in semi-arid climates. At last, the use of LSA-SAF and ECOCLIMAP-I is compared with simulations over a North-South Transect in Western Africa using LSA-SAF radiation forcing derived from remote sensing, and differences are highlighted.

Land Cover

Forcing (mathematics)

Enhanced vegetation index

10.5194/hessd-8-9113-2011

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Citations (6)

Monitoring yield and fruit quality parameters in open-canopy tree crops under water stress. Implications for ASTER

Remote Sensing of Environment (2006)

G. Sepulcre‐Cantó Pablo J. Zarco‐Tejada Juan C. Jiménez‐Muñoz José A. Sobrino María-Auxiliadora Soriano

Orchard

Tree canopy

10.1016/j.rse.2006.09.014

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Citations (76)

Chlorophyll Fluorescence Detection with a High-Spectral Resolution Spectrometer through in-filling of the O2-A band as function of Water Stress in Olive Trees

Pablo J. Zarco‐Tejada Óscar Pérez‐Priego G. Sepulcre‐Cantó John R. Miller Elías Fereres Castiel

2nd International Workshop on Remote Sensing of Vegetation Fluorescence, 17-19 Nov. 2004, Montreal, Canada

Water Stress

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

Remote Sensing of Environment (2009)

Pablo J. Zarco‐Tejada José A. Jiménez-Berni Lola Suárez G. Sepulcre‐Cantó Fermı́n Morales

Photochemical Reflectance Index

10.1016/j.rse.2009.02.016

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Citations (260)

Combining land surface models and remote sensing data to estimate evapotranspiration for drought monitoring in Europe

Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE (2014)

Carmelo Cammalleri G. Sepulcre‐Cantó J. Vogt

The main hydrologic feedback from the land-surface to the atmosphere is the evapotranspiration, ET, which embraces the response of both the soil and vegetated surface to the atmospheric forcing (e.g., precipitation and temperature), as well as influences locally atmospheric humidity, cloud formation and precipitation, the main driver for drought. Actual ET is regulated by several factors, including biological quantities (e.g., rooting depth, leaf area, fraction of absorbed photosynthetically active radiation) and soil water status. The ET temporal dynamic is strongly affected by rainfall deficits, and in turn it represents a robust proxy of the effects of water shortage on plants. These characteristics make ET a promising quantity for monitoring environmental drought, defined as a shortage of water availability that reduces the ecosystem productivity. In the last few decades, the capability to accurately model ET over large areas in a spatial-distributed fashion has increased notably. Most of the improvements in this field are related to the increasing availability of remote sensing data, and the achievements in modelling of ET-related quantities. Several land-surface models exploit the richness of newly available datasets, including the Community Land Model (CLM) and the Meteosat Second Generation (MSG) ET outputs. Here, the potentiality of ET maps obtained by combining land-surface models and remote sensing data through these two schemes is explored, with a special focus on the reliability of ET (and derived standardized variables) as drought indicator. Tests were performed over Europe at moderate spatial resolution (3-5 km), with the final goal to improve the estimation of soil water status as a contribution to the European Drought Observatory (EDO, http://edo.jrc.ec.europa.eu).

Forcing (mathematics)

Water cycle

10.1117/12.2069578

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Citations (4)

Stress detection in orchards with hyperspectral remote sensing data

Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE (2006)

Pieter Kempeneers Steve De Backer Pablo J. Zarco‐Tejada Stephanie Delalieux G. Sepulcre‐Cantó

A technique is presented for detecting vegetation crop nutrient stress from hyperspectral data. Experiments are conducted on peach trees. It is shown that nutrient deficiencies that caused stress could an be detected reliably on hyperspectral spectra. During an extensive field campaign, foliar and crown reflectance has been measured with a portable field spectroradiometer. Airborne hyperspectral imagery is acquired over the orchard with the AHS hyperspectral sensor. The multi-level approach (leaf level and top of canopy) enabled the assessment of vegetation indices and their relationship with pigment concentration at both leaf and canopy levels, showing the potential and limitations of hyperspectral remote sensing on the different levels. Stress on the peach orchard is was treated with iron chelates to recover from iron chlorosis conditions. Blocks of trees treated with iron chelates created a dynamic range of chlorophyll concentration as measured in leaves. A relationship is obtained between the measured spectra and estimated biochemical parameters via inversion of a linked directional homogeneous canopy reflectance model (ACRM) and the PROSPECT leaf model. Numerical model inversion was conducted by minimizing the difference between the measured reflectance samples and modeled values. An improved optimization method is presented. Results are compared with a simple linear regression analysis, linking chlorophyll to the reflectance measured at the leaf level and Top of Canopy (TOC). Optimal band regions and bandwidths are analyzed.

Spectroradiometer

Orchard

Precision Agriculture

10.1117/12.687842

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Citations (2)

Modelling biochemical processes in orchards at leaf- and canopy-level using hyperspectral data

Stephanie Delalieux Pieter Kempeneers J. A. N. van Aardt Steve De Backer Pablo J. Zarco‐Tejada

This research was conducted to evaluate the potenti al and limitations of hyperspectral remote sensing to detect iron deficiency in capital-intensive multi-a nnual crop systems, e.g. peach orchards. The noted deficiency can be regarded as a proxy for deviation s from optimal plant functioning, while detection o f such deviations is in turn of significant importanc e to monitoring and modelling efforts of orchards a s production systems. Hyperspectral leaf, canopy, and airborne reflectance measurements were acquired in a peach ( Prunus persica L.) orchard in Zaragoza, Spain. Leaf- and canopy-l evel data were collected with a handheld spectroradiometer (ASD, Inc.), while the AHS-160 hyperspectral sensor provided airborne data. Blocks of trees were treated with different amount of iron chelates (Sequestrene) which created a dyna mic range of chlorophyll concentration as measured in l eaves. Hyperspectral measurements at leaf-level were carri ed out to characterize the physiological aspects of nutrient stress, as opposed to the evaluation of pl ant nutrient status at the complete plant-level. St ress- induced physiological changes make stress detection at the leaf-level possible at an early stage of su b- optimal photosynthetic functioning. Airborne imager y, however, is difficult to interpret due to alteri ng illumination angles, BRDF effects, and intervening atmospheric light interactions resulting in an alte ration of the vegetative reflectance spectrum. Although ma ny studies have implemented hyperspectral analysis of nutrient status at large scales, this research fiel d is still in its infancy phase, since the link bet ween airborne- and leaf-level measurements is lacking. This inevit ably makes the physiological interpretation of exis ting hyperspectral research more complex. The multi-level (leaf, canopy, and airborne) approach presented h ere enabled the assessment of vegetation indices and th eir relationship with pigment concentration at each monitoring level. Pertinent classical chlorophyll-r elated vegetation indices were tested and new indic es were extracted from the spectral profiles by means of band reduction techniques and narrow-waveband rationing, which involved all possible 2-band combi nations. Robustness was evaluated by studying the index performance for datasets of increasing comple xity, from leaf- to canopy- and airborne-level. Physiological interpretations extracted from leaf-l evel experiments were extrapolated to canopy- and airborne level. The measured spectra and estimated biochemical parameters were related via inversion of a linked directional homogeneous canopy reflectance model (ACRM) and the PROSPECT leaf model. Numerical model inversion was conducted by minimizing the difference between the measured reflectance samples and modelled reflectance values. An improved optimization method is presented. Results are compared with a simple linear regression analysis, linking c hlorophyll to the reflectance measured at the leaf level and at the Top of Canopy (TOC), while optimal band regions and bandwidths also were analyzed.

Orchard

Interception

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