Estimating actual evapotranspiration at field-to-continent scales by calibrating the CMRSET algorithm with MODIS, VIIRS, Landsat and Sentinel-2 data

Guerschman, Juan Pablo; McVicar, Tim R.; Vleeshower, Jamie; Niel, Thomas G. Van; Peña‐Arancibia, Jorge L.; Chen, Yongqin David

doi:10.1016/j.jhydrol.2021.127318

Cited by 34 publications

(16 citation statements)

References 80 publications

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“…The CMRSET algorithm has been recently updated (Guerschman et al, 2022) (i.e., CMRSET v2), and monthly Landsat‐resolution estimates of AET have been produced in Google Earth Engine (GEE) for the entire Australian continent (McVicar et al, 2022) making similar analysis easily achievable anywhere in Australia. For the conterminous United States, Senay et al (2022) used the SSEBop model to estimate Landsat‐resolution estimates of AET in GEE, and elsewhere, the required inputs to CMRSET, SSEBop and other remote sensing‐based AET algorithms are all accessible from GEE globally.…”

Section: Discussionmentioning

confidence: 99%

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Regional‐scale partitioning of transmission losses and groundwater recharge using satellite estimates of actual evapotranspiration in an arid environment

2022

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Rivers in arid regions often rely on flow generated from wetter regions upstream, leading to high transmission losses of downstream flows. These transmission losses support a range of ecosystems, but partitioning the volume of the transmission losses across the floodplain, riparian zone and in‐channel is difficult. This study presents a methodology relying primarily on multi‐decade satellite remotely sensed actual evapotranspiration estimates to partition these losses. The method was applied to the ~40,000 km2 floodplain of Cooper Creek in the central Australian arid zone, where first, the alluvial landscape was classified based on actual evapotranspiration rates, and second, both regional‐ (i.e., for the entire floodplain) and local‐scale (i.e., for each waterhole) water balances were calculated to partition these losses. Regional‐scale results estimated that 82% of transmission losses occurred on the floodplain, 13% in the riparian zone and 5% from open water in the river channel and waterholes. These results showed that a refinement of the conceptual model of recharge from the waterholes is necessary as vast areas of the riparian zone are likely to be accessing a shallow freshwater lens rather than a discrete freshwater lens below the permanent waterholes. This method can be used in other data‐poor arid river systems as it uses globally accessible data sources.

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Section: Discussionmentioning

confidence: 99%

“…The CMRSET algorithm has been recently updated (Guerschman et al, 2022) Nanson, 1994bNanson, , 2000.…”

Section: Source Of Aet Datamentioning

confidence: 99%

Regional‐scale partitioning of transmission losses and groundwater recharge using satellite estimates of actual evapotranspiration in an arid environment

2022

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“…Detailed field methods can be found in Benyon and Doody (2004) and Benyon et al (2006). The data for each site were used to bias correct a 30 m, monthly national ET product from 2000 to 2020 (referred to as ‘CMRSET’; Guerschman et al, 2022; McVicar et al, 2022) using a spatial model developed by Doody et al (2023) called AMLETT ( A ustralia‐wide M achine L earning ET for T rees). The CMRSET model was calibrated using 33 flux towers across the breadth of Australia, however no flux towers are in the vicinity of the Lower Limestone Coast.…”

Section: Methodsmentioning

confidence: 99%

Fine scale 20‐year timeseries of plantation forest evapotranspiration for the lower limestone coast

Doody

Benyon

Gao

2023

Hydrological Processes

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Commercial plantation forestry is a vital industry worldwide, providing natural resources for humans and playing an important role in carbon sequestration. Historically, impacts of plantation development to natural resources such as surface and groundwater was not considered, however demand for water and drying climates has focussed attention on sustainable water use and extraction. The Lower Limestone Coast region of south-east South Australia has an extensive softwood plantation estate. The region is characterised by minimal surface water and substantial, fresh karstic groundwater resources, often <6 m below ground. Rapid expansion over shallow groundwater of the plantation hardwood estate from 2000, prompted concern about impacts of deep-rooted plantations on water resources and notably contracting wetlands. To quantify plantation estate water use, an extensive evapotranspiration (ET) field study (1999-2008) identified plantation areas with <6 m to groundwater as groundwater users, leading to water policy whereby plantations are considered licenced water users. To enable plantation expansion in the region, fine resolution, broadscale ET data is required to ensure optimum use of water licences in the forest industry and account for plantation groundwater extraction. As continual field studies are not feasible, this 20-year, monthly 30 m ET dataset for the region was developed to improve estimates of plantation water use, enabling improved, dynamic estimates of groundwater extraction. The dataset was developed using 21 long-term field sites where ET was derived based on field measured transpiration, soil moisture, canopy interaction and rainfall data. Region specific field ET was combined with machine learning and additional remote sensing layers to estimate monthly ET (R 2 = 0.86) across the plantation estate, improving on a national remote sensing ET product. Currently, groundwater extraction is accounted for via modelling which excludes dynamic changes in plantation groundwater extraction, limiting understanding of the hydrological processes influencing available water resources and potentially impeding economic growth. | INTRODUCTIONGlobally, the commercial plantation forest industry provides vital wood fibre and products and substantial economic and social benefits to people. This is especially the case in regional areas with ideal growing conditions, water resource availability and fewer alternative economic options. The Lower Limestone Coast Prescribes Wells Area (LLC PWA) in the south-east of South Australia has supported significant commercial plantation forestry over the past 120 years across 10% (145 000 hectares) of its area, dominated by the softwood

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“…Notably, these indices are not isolated, but can be nested with each other. Guerschman et al (2011) proposed an open water likelihood index (OWLI) by integrating the NDWI, normalized difference vegetation index, SWIR band, and topographic relief index extracted from DEM data (MrVBF). And the effectiveness of extracting mountain water using OWLI has been significantly improved because this method reduces the uncertainty caused by terrain shadows.…”

Section: Index-based Water Extractionmentioning

confidence: 99%

Remote Sensing Big Data for Water Environment Monitoring: Current Status, Challenges, and Future Prospects

Chen

et al. 2022

Earth's Future

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Accurate water extraction and quantitative estimation of water quality are two key and challenging issues for remote sensing of water environment. Recent advances in remote sensing big data, cloud computing, and machine learning have promoted these two fields into a new era. This study reviews the operating framework and methods of remote sensing big data for water environment monitoring, with emphasis on water extraction and quantitative estimation of water quality. The following aspects were investigated in this study: (a) image data source and model evaluation metrics; (b) state-of-the-art methods for water extraction, including threshold-based methods, water indices, and machine learning-based methods; (c) state-of-the-art models for quantitative estimation of water quality, including empirical models, semi-empirical/semi-analytical models, and machine learning-based models; (d) some shortcomings and three challenges of current remote sensing big data for water environment monitoring, namely the new data gap caused by massive heterogeneous data, inefficient water environment monitoring due to "low spatiotemporal resolution," and low accuracy of water quality estimation models resulting from complex water composition and insufficient atmospheric correction methods for water bodies; and (e) five recommendations to solve these challenges, namely, using cloud computing and emerging sensors/platforms to monitor water changes in intensive time series, establishing models based on ensemble machine learning algorithms, exploring quantitative estimation models of water quality that couple physics and causality, identifying the missing elements in water environment assessments, and developing new governance models to meet the widespread applications of remote sensing of water environment. This review can help provide a potential roadmap and information support for researchers, practitioners, and management departments in the theoretical exploration and innovative application of remote sensing big data for water environment monitoring.

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Estimating actual evapotranspiration at field-to-continent scales by calibrating the CMRSET algorithm with MODIS, VIIRS, Landsat and Sentinel-2 data

Cited by 34 publications

References 80 publications

Regional‐scale partitioning of transmission losses and groundwater recharge using satellite estimates of actual evapotranspiration in an arid environment

Regional‐scale partitioning of transmission losses and groundwater recharge using satellite estimates of actual evapotranspiration in an arid environment

Fine scale 20‐year timeseries of plantation forest evapotranspiration for the lower limestone coast

Remote Sensing Big Data for Water Environment Monitoring: Current Status, Challenges, and Future Prospects

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