Use of Field Spectroscopy for Exploring the Impact of Atmospheric Effects on Landsat 5 TM/7 ETM+ Satellite Images Intended for Hydrological Purposes in Cyprus

Papadavid, George; Hadjimitsis, Diofantos G.; Perdikou, Skevi; Michaelides, Silas; Toulios, Leonidas; Seraphides, Nicos

doi:10.2747/1548-1603.48.2.280

Cited by 11 publications

(7 citation statements)

References 21 publications

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“…However, as mentioned previously this limitation is bypassed using a spectroradiometer. The WDVI is defined as follows (Papadavid et al 2010):where NIR is the reflectance of the near-infrared band, RED is the reflectance of the visible red band and γ is the slope of the soil line (Papadavid et al 2011 b ). The WDVI index has the advantage of greatly reducing the influence of soil background on the surface reflectance values.…”

Section: Methodsmentioning

confidence: 99%

“…Ground spectro-radiometers have also been used to study the phenological cycle of groundnut (Papadavid 2012; Papadavid & Hadjimitsis 2014). Papadavid & Hadjimitsis 2009 and Papadavid et al (2010, 2011 b ) showed that field spectroscopy and empirical modelling, when successfully integrated, can develop new models of LAI and crop height during the phenological cycle of crops (Fig. 1).…”

Section: Introductionmentioning

confidence: 99%

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The use of earth observation methods for estimating regional crop evapotranspiration and yield for water footprint accounting

Papadavid

Toulios

2017

J. Agric. Sci.

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Remote sensing can efficiently support the quantification of crop water requirements included in the goal of assessing water footprints, which is to analyse how human activities or specific products relate to issues of water scarcity and pollution and identify how activities and products can become more sustainable from a water perspective. Remote sensing techniques have become popular in estimating actual crop evapotranspiration and hence crop water requirements in recent decades due to the advantages they offer to users, e.g. low cost, regional data and use of maps instead of point measurements as well as saving time. The use of earth observation data supports models’ accuracy in the procedure for assessing water footprint, since no average values are used: instead, users have real values for the specific parameters.The present study provides two examples of how remote sensing techniques are used essentially for estimating evapotranspiration along with crop yield, two basic parameters, for assessing water footprint. Two different case studies have been illustrated to define the methodology proposed, which refers to Mediterranean conditions and can be applied after inferring the necessary field data of each crop. The first case study refers to the application of Surface Energy Balance Algorithm for Land (SEBAL) for estimating evapotranspiration, while the second refers to the Crop Yield prediction. Both elements, such as evapotranspiration and crop yield, are vital for water footprint accounting. Firstly, the SEBAL was adopted, under the essential adaptations for local soil and meteorological conditions for estimating groundnut water requirements. Landsat-5 TM, Landsat-7 Enhanced Thematic Mapper+ and Landsat 8 OLI images were used to retrieve the required spectral data. The SEBAL model is enhanced with empirical equations regarding crop canopy factors, in order to increase the accuracy of crop evapotranspiration estimation. Maps were created for evapotranspiration (ET) using the SEBAL modified model for the area of interest. The results were compared with measurements from an evaporation pan, used as a reference. Statistical comparisons showed that the modified SEBAL can predict ETc in a very effective and accurate way and provide water footprint modellers with high-level crop water data. Yield prediction plays a vital role in calculating water footprint. Having real values rather than taking reference (or averaged) values from FAO is an advantage that Earth Observation means can provide. This is very important in econometric or any other prediction models used for estimating water footprint because using average data reduces accuracy. In this context, crop and soil parameters along with remotely sensed data can be used to develop models that can provide users with accurate yield estimations. In a second step, crop and soil parameters along with the normalized difference vegetation index were correlated to examine whether crop yield can be predicted and to define the actual time-window to predict the yield. Statistical and remote sensing techniques were then applied to derive and map a model that can predict crop yield. The algorithm developed for this purpose indicates that remote sensing observations can predict crop yields effectively and accurately. Using the statistical Student's t test, it was found that there was no statistically significant difference between predicted and real values for crop yield.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The use of earth observation methods for estimating regional crop evapotranspiration and yield for water footprint accounting

Papadavid

Toulios

2017

J. Agric. Sci.

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“…At this stage atmospheric correction algorithms are applied in order to remove the effects from the atmosphere. A considerable investigation on the effect of the atmosphere on dark targets, in the area of interest, has been already examined; the impact of atmospheric correction on vegetation indices as well on crops as separate targets has been raised and is investigated in this paper [5,[35][36][37][38].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The specific equation was applied to satellite images and LAI maps were created. Various studies have shown that empirical equations of LAI and Vegetation Indices can provide good estimates of LAI from the satellite images [4,37,[43][44][45][46]. c) Then the Crop Yield Prediction model has been applied by using atmospheric corrected and noncorrected available satellite imagery of April 3013 and 2014 mentioned earlier.…”

Section: Wdvi a Laimentioning

confidence: 99%

Atmospheric Effects on Spatial Crop Yield Modelling Using Landsat’s Imagery

Papadavid¹

2018

OAJAR

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Remote sensing techniques provide the opportunity for optimizing and predicting crop yields in the field of agriculture. Spatially Yield prediction plays a vital role in Agricultural Policy and provides useful data to policy makers. This paper aims at examining the use of field spectroscopy along with Landsat's satellite imagery to test the accuracy of raw satellite data and the impact of atmospheric effects on determining crop yield derived from models using remotely sensed data. Vegetation Indices are vital in Crop Yield modelling since they are used in stochastic or empirical models for describing or predicting crop yield. Leaf Area Index, which is also inferred using VI, is also compared to the real values of LAI that are measured using the SunScan instrument, during the satellite's overpass. The spectroradiometric retrieved Vegetation Indices(VI) of Durum wheat are directly compared to the corresponding VI of Landsat 7 ETM+ and 8 OLI, sourcing from both atmospherically corrected and not corrected satellite images in order to test the effects of atmosphere upon them. Crop Yield is finally determined using the Cyprus Agricultural Research Institute's Crop Yield model for Durum wheat, adapted to satellite data, and is used to examine the impact of atmospheric effects. The results indicate that if no atmospheric effects algorithms are applied, then there is statistically significant difference in the prediction from the real yield and hence a significant error regarding the model. The study's goal is to illustrate the need of atmospheric effects removal on remotely sensed data especially for models using satellite images.

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“…Some studies used multispectral image sensor system to measure crop canopy characteristics [11]. The use of vegetation indices for statistically describing LAI and CH is nowadays common in international literature and many empirical models are available depending on the conditions and the geographical location [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Image based remote sensing method for modeling black-eyed beans (Vigna unguiculata) Leaf Area Index (LAI) and Crop Height (CH) over Cyprus

Papadavid

Fasoula

Hadjimitsis³

et al. 2013

Open Geosciences

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Abstract:In this paper, Leaf Area Index (LAI) and Crop Height (CH) are modeled to the most known spectral vegetation index -NDVI -using remotely sensed data. This approach has advantages compared to the classic approaches based on a theoretical background. A GER-1500 field spectro-radiometer was used in this study in order to retrieve the necessary spectrum data for estimating a spectral vegetation index (NDVI), for establishing a semiempirical relationship between black-eyed beans' canopy factors and remotely sensed data. Such semi-empirical models can be used then for agricultural and environmental studies. A field campaign was undertaken with measurements of LAI and CH using the Sun-Scan canopy analyzer, acquired simultaneously with the spectroradiometric (GER1500) measurements between May and June of 2010. Field spectroscopy and remotely sensed imagery have been combined and used in order to retrieve and validate the results of this study. The results showed that there are strong statistical relationships between LAI or CH and NDVI which can be used for modeling crop canopy factors (LAI, CH) to remotely sensed data. The model for each case was verified by the factor of determination. Specifically, these models assist to avoid direct measurements of the LAI and CH for all the dates for which satellite images are available and support future users or future studies regarding crop canopy parameters.

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Use of Field Spectroscopy for Exploring the Impact of Atmospheric Effects on Landsat 5 TM/7 ETM+ Satellite Images Intended for Hydrological Purposes in Cyprus

Cited by 11 publications

References 21 publications

The use of earth observation methods for estimating regional crop evapotranspiration and yield for water footprint accounting

The use of earth observation methods for estimating regional crop evapotranspiration and yield for water footprint accounting

Atmospheric Effects on Spatial Crop Yield Modelling Using Landsat’s Imagery

Image based remote sensing method for modeling black-eyed beans (Vigna unguiculata) Leaf Area Index (LAI) and Crop Height (CH) over Cyprus

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