Performance assessment of an irrigation scheme using indicators determined with remote sensing techniques

Santos, C.; Lorite, Ignacio J.; Tasumi, Masahiro; Allen, Richard G.; Fereres, E.

doi:10.1007/s00271-010-0207-7

Cited by 50 publications

(35 citation statements)

References 75 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…During the last 20 years, satellite-based remote sensing (SBRS) tools have emerged as a low-cost, yet reliable alternative to estimate ETa over large areas and wide range of vegetation types [10,11,[20][21][22][23][24]. These remote sensing tools estimate ETa based on algorithms which compute the SEB components using remotely sensed surface reflectance in the visible (VIS) and near-infrared (NIR) wavebands and surface temperature (radiometric) from the infrared (IR) thermal bands [19,24,25].…”

Section: Introductionmentioning

confidence: 99%

Parameterization of the Satellite-Based Model (METRIC) for the Estimation of Instantaneous Surface Energy Balance Components over a Drip-Irrigated Vineyard

et al. 2014

View full text Add to dashboard Cite

Abstract:A study was carried out to parameterize the METRIC (Mapping EvapoTranspiration at high Resolution with Internalized Calibration) model for estimating instantaneous values of albedo (shortwave albedo) (αi), net radiation (Rni) and soil heat flux (Gi), sensible (Hi) and latent heat (LEi) over a drip-irrigated Merlot vineyard (location: 35°25′ LS; 71°32′ LW; 125 m.a.s. (l). The experiment was carried out in a plot of 4.25 ha, processing 15 Landsat images, which were acquired from 2006 to 2009. An automatic weather station was placed inside the experimental plot to measure αi, Rni and Gi. In the same tower an Eddy Covariance (EC) system was mounted to measure Hi and LEi. Specific 11343 sub-models to estimate Gi, leaf area index (LAI) and aerodynamic roughness length for momentum transfer (zom) were calibrated for the Merlot vineyard as an improvement to the original METRIC model. Results indicated that LAI, zom and Gi were estimated using the calibrated functions with errors of 4%, 2% and 17%, while those were computed using the original functions with errors of 58%, 81%, and 5%, respectively. At the time of satellite overpass, comparisons between measured and estimated values indicated that METRIC overestimated αi in 21% and Rni in 11%. Also, METRIC using the calibrated functions overestimated Hi and LEi with errors of 16% and 17%, respectively while it using the original functions overestimated Hi and LEi with errors of 13% and 15%, respectively. Finally, LEi was estimated with root mean square error (RMSE) between 43 and 60 W·m

show abstract

Section: Introductionmentioning

confidence: 99%

Parameterization of the Satellite-Based Model (METRIC) for the Estimation of Instantaneous Surface Energy Balance Components over a Drip-Irrigated Vineyard

et al. 2014

View full text Add to dashboard Cite

show abstract

“…However, water availability for agriculture continues to decline due to competing demands from other sectors such as municipal, industrial, and recently environmental and recreational [1,[3][4][5]. Assessment of irrigation performance is critically important for improving irrigation water management and the sustainability of irrigated agriculture [6].…”

Section: Introductionmentioning

confidence: 99%

Farm Level Assessment of Irrigation Performance for Dairy Pastures in the Goulburn-Murray District of Australia by Combining Satellite-Based Measures with Weather and Water Delivery Information

Abuzar

Whitfield

McAllister

2017

IJGI

View full text Add to dashboard Cite

Pasture performance of 924 dairy farms in a major irrigation district of Australia was investigated for their water use and water productivity during the 2015-2016 summer which was the peak irrigation period. Using satellite images from Landsat-8 and Sentinel-2, estimates of crop coefficient (Kc) were determined on the basis of a strong linear relationship between crop evapotranspiration (ETc) and vegetation index (NDVI) of pasture in the region. Utilizing estimates of Kc and crop water requirement (CWR), NDVI-dependent estimates of Irrigation Water Requirement (IWR) were derived based on the soil water balance model. In combination with daily weather information and seasonal irrigation water supply records, IWR was the key component in the understanding of current irrigation status at farm level, and deriving two irrigation performance indicators: (1) Relative Irrigation Water Use (RIWU) and (2) Total Irrigation Water Productivity (TIWP). A slightly higher proportion of farm irrigators were found to be either matching the irrigation requirement or under-watering (RIWU ≤ 1.0). According to TIWP, a few dairy farms (3%) were found to be in the category of high yield potential with excess water use, and very few (1%) in the category of limited water supply to pastures of high yield potential. A relatively high number of farms were found to be in the category where excess water was supplied to pastures of low-medium yield potential (27%), and farms where water supply compromised pastures with a sub-maximal vegetation status (15%). The results of this study will assist in objectively identifying where significant improvement in efficient irrigation water use can be achieved.

show abstract

“…The first approaches to exploit those data at large scale are shown in Romaguera et al [8,9], where the use of the different components of the water cycle is explored, together with the use of land surface models. Other works used remote sensing to evaluate irrigation performance at regional scale [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Towards the Improvement of Blue Water Evapotranspiration Estimates by Combining Remote Sensing and Model Simulation

et al. 2014

View full text Add to dashboard Cite

Abstract:The estimation of evapotranspiration of blue water (ET b ) from farmlands, due to irrigation, is crucial to improve water management, especially in regions where water resources are scarce. Large scale ET b was previously obtained, based on the differences between remote sensing derived actual ET and values simulated from the Global Land Data Assimilation System (GLDAS). In this paper, we improve on the previous approach by enhancing the classification scheme employed so that it represents regions with common hydrometeorological conditions. Bias between the two data sets for reference areas (non-irrigated croplands) were identified per class, and used to adjust the remote sensing products. Different classifiers were compared and evaluated based on the generated bias curves per class and their variability. The results in Europe show that the k-means classifier was better suited to identify the bias curves per class, capturing the dynamic range of these curves and minimizing their variability within each corresponding class. The method was applied in Africa and the classification and bias results were consistent with the findings in Europe. The ET b results were compared with existing literature and provided differences up to 50 mm/year in Europe, while the comparison in Africa was found to be highly influenced by the assigned cover type and the heterogeneity of the pixel. Although further research is needed to fully understand the ET b values found, this paper shows a more robust approach to classify and characterize the bias between the two sets of ET data.

show abstract

Performance assessment of an irrigation scheme using indicators determined with remote sensing techniques

Cited by 50 publications

References 75 publications

Parameterization of the Satellite-Based Model (METRIC) for the Estimation of Instantaneous Surface Energy Balance Components over a Drip-Irrigated Vineyard

Parameterization of the Satellite-Based Model (METRIC) for the Estimation of Instantaneous Surface Energy Balance Components over a Drip-Irrigated Vineyard

Farm Level Assessment of Irrigation Performance for Dairy Pastures in the Goulburn-Murray District of Australia by Combining Satellite-Based Measures with Weather and Water Delivery Information

Towards the Improvement of Blue Water Evapotranspiration Estimates by Combining Remote Sensing and Model Simulation

Contact Info

Product

Resources

About