Satellite based yield and water use targets for horticultural crops grown in SE Australia

O’Connell, M.; Whitfield, D.M.; McAllister, Andy; McClymont, L.; Abuzar, M.; Sheffield, Kathryn

doi:10.2495/si120231

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…This study used a single image as a representative of summer season. This is in accordance with a previous study in the irrigation regions of Victoria, Australia, which found the use of single 'midseason' image adequate to assess maximum crop cover because of strong temporal stability in NDVI response [53]. However, for further studies, there is a scope to test temporal aggregates of image-based variables from multiple sources including Landsat-8/9 and Sentinel-2.…”

Section: Discussionsupporting

confidence: 90%

Feasibility of Using SWIR Transformed Reflectance (STR) in Place of Surface Temperature (Ts) for the Mapping of Irrigated Landcover

Abuzar,

Sheffield,

McAllister

2024

Preprint

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(1) Background: A simple approach to map irrigated landcover has been introduced by using measures derived from optical spectral range as an alternative to thermal range. It has been demonstrated that substituting surface temperature (Ts, ‘thermal approach’) with SWIR transformed reflectance (STR, ‘optical approach’) to detect surface moisture is feasible with comparable results. (2) Methods: Using an iterative thresholding procedure to minimize within-class variance, the bilevel segmentation of variables derived from Landsat-8 representing surface moisture and vegetation cover was achieved for the 2020-21 summer for a key irrigation district in Australia. (3) Results: The results of irrigated landcover by optical approach found to be comparable with those by thermal approach. The classification accuracy was assessed using water delivery records at farm level. Though the overall accuracy is high in both cases, the optical approach (97.6%) performed slightly better than the thermal approach (93.9%). (4) Conclusions: The feasibility of using STR to map irrigated landcover has been confirmed by a high-level overall accuracy assessment. This has broader implications in terms of irrigated landcover assessment, as the use of satellite imagery in these applications may not necessarily be limited to microwave or thermal sensors.

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Section: Discussionsupporting

confidence: 90%

Feasibility of Using SWIR Transformed Reflectance (STR) in Place of Surface Temperature (Ts) for the Mapping of Irrigated Landcover

Abuzar,

Sheffield,

McAllister

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…This study used a single image as a representative of the summer season. This is in accordance with a previous study in the irrigation regions of Victoria, Australia, which found the use of a single 'mid-season' image adequate to assess maximum crop cover because of the strong temporal stability in NDVI response [53]. However, for further studies, there is scope to test temporal aggregates of image-based variables from multiple sources including Landsat-8/9 and Sentinel-2.…”

Section: Discussionsupporting

confidence: 89%

Feasibility of Using SWIR-Transformed Reflectance (STR) in Place of Surface Temperature (Ts) for the Mapping of Irrigated Landcover

Abuzar,

Sheffield,

McAllister

2024

Land

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(1) Background: A simple approach to map irrigated landcover has been introduced by using measures derived from the optical spectral range as an alternative to the thermal range. It has been demonstrated that substituting surface temperature (Ts, ‘thermal approach’) with SWIR-transformed reflectance (STR, ‘optical approach’) to detect surface moisture is feasible with comparable results. (2) Methods: Using an iterative thresholding procedure to minimize within-class variance, the bilevel segmentation of variables derived from Landsat-8 representing surface moisture and vegetation cover was achieved for the 2020–2021 summer for a key irrigation district in Australia. (3) Results: The results of irrigated landcover by the optical approach were found to be comparable with those obtained by the thermal approach. The classification accuracy was assessed using water delivery records at the farm level. Although the overall accuracy was high in both cases, the optical approach (97.6%) performed slightly better than the thermal approach (93.9%). (4) Conclusions: The feasibility of using STR to map irrigated landcover has been confirmed by a high-level overall accuracy assessment. This has broader implications in terms of irrigated landcover assessment, as the use of satellite imagery in these applications may not necessarily be limited to microwave or thermal sensors.

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“…Our decision to use a single image per irrigation period was in accordance with previous studies. In a study conducted previously on the perennial horticulture crops in the same region, it was found that the use of single 'midseason' image is adequate to assess maximum crop cover because of strong temporal stability in NDVI response (O'Connell 2011, p. 61) [44]. Quoting multiple studies, Velpuri et al (2009, p. 1384) [45] reported that "single date fine-resolution imagery, acquired at critical growth stages, is sufficient to identify irrigation".…”

Section: Discussionmentioning

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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Satellite based yield and water use targets for horticultural crops grown in SE Australia

Cited by 5 publications

References 12 publications

Feasibility of Using SWIR Transformed Reflectance (STR) in Place of Surface Temperature (Ts) for the Mapping of Irrigated Landcover

Feasibility of Using SWIR Transformed Reflectance (STR) in Place of Surface Temperature (Ts) for the Mapping of Irrigated Landcover

Feasibility of Using SWIR-Transformed Reflectance (STR) in Place of Surface Temperature (Ts) for the Mapping of Irrigated Landcover

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

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