Application of ET-NDVI-relationship approach and soil-water-balance modelling for the monitoring of irrigation performance of treed horticulture crops in a key fruit-growing district of Australia

Abuzar, M.; Whitfield, D.M.; McAllister, Andy; Sheffield, Kathryn

doi:10.1080/01431161.2019.1573337

Cited by 7 publications

(5 citation statements)

References 30 publications

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“…Considering all the plots and statistics, NDVI was chosen for further processing, being much easier to compute, while it is region-independent, unlike SAVI [47]. Additionally, international literature always confirms the importance of NDVI as a remote sensing tool [9,11,[28][29][30][31][32]65,[68][69][70]. Figure 7d1 illustrates ET r F versus NDVI for the general case, corresponding to Equation (26), which is the formula used for the next steps of the study.…”

Section: Resultsmentioning

confidence: 99%

Hybrid Methodology for the Estimation of Crop Coefficients Based on Satellite Imagery and Ground-Based Measurements

Spiliotopoulos

Λουκάς

2019

Water

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The objective of the current study was the investigation of specific relationships between crop coefficients and vegetation indices (VI) computed at the water-limited environment of Lake Karla Watershed, Thessaly, in central Greece. A Mapping ET (evapotranspiration) at high Resolution and with Internalized Calibration (METRIC) model was used to derive crop coefficient values during the growing season of 2012. The proposed methodology was developed using medium resolution Landsat 7 ETM+ images and meteorological data from a local weather station. Cotton, sugar beets, and corn fields were utilized. During the same period, spectral signatures were obtained for each crop using the field spectroradiometer GER1500 (Spectra Vista Corporation, NY, U.S.A.). Relative spectral responses (RSR) were used for the filtering of the specific reflectance values giving the opportunity to match the spectral measurements with Landsat ETM+ bands. Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI) and Enhanced Vegetation Index 2 (EVI2) were then computed, and empirical relationships were derived using linear regression analysis. NDVI, SAVI, and EVI2 were tested separately for each crop. The resulting equations explained those relationships with a very high R2 value (>0.86). These relationships have been validated against independent data. Validation using a new image file after the experimental period gives promising results, since the modeled image file is similar in appearance to the initial one, especially when a crop mask is applied. The CROPWAT model supports those results when using the new crop coefficients to estimate the related crop water requirements. The main benefit of the new approach is that the derived relationships are better adjusted to the crops. The described approach is also less time-consuming because there is no need for atmospheric correction when working with ground spectral measurements.

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Section: Resultsmentioning

confidence: 99%

Hybrid Methodology for the Estimation of Crop Coefficients Based on Satellite Imagery and Ground-Based Measurements

Spiliotopoulos

Λουκάς

2019

Water

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“…NDVI from ASTER and Sentinel-2 as well as Ts from ASTER were re-sampled to a 30 m resolution using bilinear interpolation method and were adjusted to be comparable to Landsat equivalents. The adjustment factors used in this study were taken from our previous investigations [24,30].…”

Section: Step 1: Data Preparationmentioning

confidence: 99%

Remotely-Sensed Surface Temperature and Vegetation Status for the Assessment of Decadal Change in the Irrigated Land Cover of North-Central Victoria, Australia

Abuzar

McAllister

Whitfield

et al. 2020

Land

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Monitoring of irrigated land cover is important for both resource managers and farmers. An operational approach is presented to use the satellite-derived surface temperature and vegetation cover in order to distinguish between irrigated and non-irrigated land. Using an iterative thresholding procedure to minimize within-class variance, the bilevel segmentation of surface temperature and vegetation cover was achieved for each irrigation period (Spring, Summer and Autumn). The three periodic profiles were used to define irrigation land covers from 2008–2009 to 2018–2019 in a key agricultural region of Australia. The overall accuracy of identifying farms with irrigated land cover amounted to 95.7%. Total irrigated land cover was the lowest (approximately 200,000 ha) in the 2008–2009 crop year which increased more than three-fold in 2012–2013, followed by a gradual decline in the following years. Satellite images from Landsat series (L-5, L-7 and L-8), Sentinel-2 and ASTER were found suitable for land cover classification, which is scalable from farm to regional levels. For this reason, the results are desirable for a range of stakeholders.

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“…The use of not a simple relationship, but a normalized difference between the minimum and maximum reflections increases the measurement accuracy, reduces the effects of such phenomena as differences in the light of a picture, clouds, haze, radiation absorption by the atmosphere, etc. A standardized continuous gradient or discrete scale is used to display the NDVI index showing values in the range -1,..., 0, 0.1, ... 1 in% [13][14][15][16][17][18][19][20].…”

Section: Ndvimentioning

confidence: 99%

Implementation of the Ers for Yield Analyzing of Irrigated Lands of South Kazakhstan

Onglassynov¹,

Akylbekova²,

Sotnikov³

et al. 2019

OF THE ACADEMY OF SCIENCES OF THE REPUBLIC OF KAZAKHSTAN

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Application of ET-NDVI-relationship approach and soil-water-balance modelling for the monitoring of irrigation performance of treed horticulture crops in a key fruit-growing district of Australia

Cited by 7 publications

References 30 publications

Hybrid Methodology for the Estimation of Crop Coefficients Based on Satellite Imagery and Ground-Based Measurements

Hybrid Methodology for the Estimation of Crop Coefficients Based on Satellite Imagery and Ground-Based Measurements

Remotely-Sensed Surface Temperature and Vegetation Status for the Assessment of Decadal Change in the Irrigated Land Cover of North-Central Victoria, Australia

Implementation of the Ers for Yield Analyzing of Irrigated Lands of South Kazakhstan

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