Monitoring irrigation using landsat observations and climate data over regional scales in the Murray-Darling Basin

Bretreger, David; Yeo, In‐Young; Hancock, Greg; Willgoose, Garry

doi:10.1016/j.jhydrol.2020.125356

Cited by 20 publications

(8 citation statements)

References 67 publications

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“…Early studies focused on the use of satellite imagery to identify irrigated areas. Later, different approaches were developed using optical data [16][17][18][19][20][21][22][23][24][25][26][27][28]. These included the use of Landsat data and more recently Sentinel-2 data.…”

Section: Introductionmentioning

confidence: 99%

Irrigation Mapping on Two Contrasted Climatic Contexts Using Sentinel-1 and Sentinel-2 Data

Elwan

Page

Jarlan

et al. 2022

Water

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This study aims to propose an operational approach to map irrigated areas based on the synergy of Sentinel-1 (S1) and Sentinel-2 (S2) data. An application is proposed at two study sites in Europe—in Spain and in Italy—with two climatic contexts (semiarid and humid, respectively), with the objective of proving the essential role of multi-site training for a robust application of the proposed methodologies. Several classifiers are proposed to separate irrigated and rainfed areas. They are based on statistical variables from Sentinel-1 and Sentinel-2 time series data at the agricultural field scale, as well as on the contrasted behavior between the field scale and the 5 km surroundings. The support vector machine (SVM) classification approach was tested with different options to evaluate the robustness of the proposed methodologies. The optimal number of metrics found is five. These metrics illustrate the importance of optical/radar synergy and the consideration of multi-scale spatial information. The highest accuracy of the classifications, approximately equal to 85%, is based on training dataset with mixed reference fields from the two study sites. In addition, the accuracy is consistent at the two study sites. These results confirm the potential of the proposed approaches towards the most general use on sites with different climatic and agricultural contexts.

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Section: Introductionmentioning

confidence: 99%

Irrigation Mapping on Two Contrasted Climatic Contexts Using Sentinel-1 and Sentinel-2 Data

Elwan

Page

Jarlan

et al. 2022

Water

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“…Meeting these ends will require: (1) modeling irrigation water requirements at the global scale [9], (2) assessing irrigated food production [10], (3) quantifying the impact of irrigation on climate [11], river discharge [12], and groundwater depletion [13,14], and (4) building plans for an optimal water resource allocation so that managers can accurately account for water use [15][16][17]. Central to all these steps is accurate knowledge about the spatial extent of irrigated lands, the amount of water applied as irrigation, and the timing when irrigation is applied.…”

Section: Introductionmentioning

confidence: 99%

A Review of Irrigation Information Retrievals from Space and Their Utility for Users

et al. 2021

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Irrigation represents one of the most impactful human interventions in the terrestrial water cycle. Knowing the distribution and extent of irrigated areas as well as the amount of water used for irrigation plays a central role in modeling irrigation water requirements and quantifying the impact of irrigation on regional climate, river discharge, and groundwater depletion. Obtaining high-quality global information about irrigation is challenging, especially in terms of quantification of the water actually used for irrigation. Here, we review existing Earth observation datasets, models, and algorithms used for irrigation mapping and quantification from the field to the global scale. The current observation capacities are confronted with the results of a survey on user requirements on satellite-observed irrigation for agricultural water resources’ management. Based on this information, we identify current shortcomings of irrigation monitoring capabilities from space and phrase guidelines for potential future satellite missions and observation strategies.

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“…Correctly quantifying irrigation in Earth system models can serve two purposes. On the one hand, it can help improve water management (Le Page et al, 2020, Bretreger et al, 2020; on the other hand, it allows us to quantitatively assess its effects on the terrestrial water, carbon, and energy cycles (Haddeland et al, 2007;Breña-Naranjo et al, 2014;Hu et al, 2016;Qian et al 2020). Indeed, results of large-scale irrigation studies using land surface models (LSMs) have demonstrated that irrigation increases soil moisture and evapotranspiration (ET) and, consequently, latent heat flux with a decrease in sensible heat flux (i.e.…”

Section: Introductionmentioning

confidence: 99%

Optimizing a backscatter forward operator using Sentinel-1 data over irrigated land

Modanesi

Massari

Gruber

et al. 2021

Hydrol. Earth Syst. Sci.

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Abstract. Worldwide, the amount of water used for agricultural purposes is rising, and the quantification of irrigation is becoming a crucial topic. Because of the limited availability of in situ observations, an increasing number of studies is focusing on the synergistic use of models and satellite data to detect and quantify irrigation. The parameterization of irrigation in large-scale land surface models (LSMs) is improving, but it is still hampered by the lack of information about dynamic crop rotations, or the extent of irrigated areas, and the mostly unknown timing and amount of irrigation. On the other hand, remote sensing observations offer an opportunity to fill this gap as they are directly affected by, and hence potentially able to detect, irrigation. Therefore, combining LSMs and satellite information through data assimilation can offer the optimal way to quantify the water used for irrigation. This work represents the first and necessary step towards building a reliable LSM data assimilation system which, in future analysis, will investigate the potential of high-resolution radar backscatter observations from Sentinel-1 to improve irrigation quantification. Specifically, the aim of this study is to couple the Noah-MP LSM running within the NASA Land Information System (LIS), with a backscatter observation operator for simulating unbiased backscatter predictions over irrigated lands. In this context, we first tested how well modelled surface soil moisture (SSM) and vegetation estimates, with or without irrigation simulation, are able to capture the signal of aggregated 1 km Sentinel-1 backscatter observations over the Po Valley, an important agricultural area in northern Italy. Next, Sentinel-1 backscatter observations, together with simulated SSM and leaf area index (LAI), were used to optimize a Water Cloud Model (WCM), which will represent the observation operator in future data assimilation experiments. The WCM was calibrated with and without an irrigation scheme in Noah-MP and considering two different cost functions. Results demonstrate that using an irrigation scheme provides a better calibration of the WCM, even if the simulated irrigation estimates are inaccurate. The Bayesian optimization is shown to result in the best unbiased calibrated system, with minimal chances of having error cross-correlations between the model and observations. Our time series analysis further confirms that Sentinel-1 is able to track the impact of human activities on the water cycle, highlighting its potential to improve irrigation, soil moisture, and vegetation estimates via future data assimilation.

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Monitoring irrigation using landsat observations and climate data over regional scales in the Murray-Darling Basin

Cited by 20 publications

References 67 publications

Irrigation Mapping on Two Contrasted Climatic Contexts Using Sentinel-1 and Sentinel-2 Data

Irrigation Mapping on Two Contrasted Climatic Contexts Using Sentinel-1 and Sentinel-2 Data

A Review of Irrigation Information Retrievals from Space and Their Utility for Users

Optimizing a backscatter forward operator using Sentinel-1 data over irrigated land

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