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

Massari, Christian; Modanesi, Sara; Dari, Jacopo; Gruber, A.; Lannoy, Gabriëlle De; Girotto, Manuela; Quintana-Seguí, Pere; Page, Michel Le; Jarlan, Lionel; Zribi, Mehrez; Ouaadi, Nadia; Vreugdenhil, Mariëtte; Zappa, Luca; Dorigo, Wouter; Wagner, Wolfgang; Brombacher, Joost; Pelgrum, H.; Jaquot, Pauline; Freeman, Vahid; Volden, Espen; Prieto, D.F.; Tarpanelli, Angelica; Barbetta, Silvia; Brocca, Luca

doi:10.3390/rs13204112

Cited by 96 publications

(66 citation statements)

References 143 publications

(177 reference statements)

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“…One crucial question for managers is the precise identification of irrigated areas. Remote sensing has shown great potential for irrigation mapping [11][12][13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

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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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“…One crucial question for managers is the precise identification of irrigated areas. Remote sensing has shown great potential for irrigation mapping [11][12][13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

“…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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“…Three last aspects need to be highlighted: i) a robust evaluation analysis of land surface variables is not straightforward considering that RS observations at coarse spatial resolution (i.e., ASCAT) or constrained by reanalysis data (i.e., MODIS ET) do not necessarily provide accurate information on irrigation (Zaussinger et al, 2019); ii) additionally, evaluating irrigation estimates is also more challenging due to the scarce availability of information on the irrigation management (Massari et al, 2021); and iii) finally, the disagreement between in situ reference data and irrigation estimates obtained from the model-only can be partly explained by the actual in situ irrigation system management, which depends on water availability and policies unknown by the modelling system. In this context, although irrigation data are compared at bi-weekly time scale and DA is overall expected to improve the simulation of irrigation temporal dynamics, the temporal resolution of Sentinel-1 (~3 days over the study areas) could imply missing some irrigation events and affect the DA results.…”

Section: Limitations and Shortcomingsmentioning

confidence: 99%

“…Irrigation has been applied by humans for as long as they have been cultivating plants. However, in the last century irrigation has become one of the most impactful human activities on the terrestrial water cycle, accounting for nearly 85% of the global water consumption (Gleick, 2003;Kustu et al, 2010) and for the 40% of the world's food production (Foley et al, 2011;Massari et al, 2021;Siebert and Döll, 2010).…”

Section: Introductionmentioning

confidence: 99%

“…Beyond these efforts, the human influence on the water distribution is still poorly described in LSMs due to simplifying assumptions, such as limitations related to soil texture maps, static crop maps or irrigation intensity inputs (Modanesi et al, 2021;Monfreda et al, 2008;Ozdogan et al, 2010a;Salmon et al, 2015). Another key issue is related to the farmer's irrigation application decision, which is not necessarily related to crop irrigation requirements or based on the root-zone soil moisture availability but mainly on water resources availability (Massari et al, 2021). On the other hand, RS observations can indirectly disclose the presence of irrigation activities, when they sense the entire integrated soil-vegetation system.…”

Section: Introductionmentioning

confidence: 99%

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Challenges and benefits of quantifying irrigation through the assimilation of Sentinel-1 backscatter observations into Noah-MP

Modanesi

Massari

Bechtold

et al. 2022

Preprint

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Abstract. In recent years, the amount of water used for agricultural purposes has been rising due to an increase in food demand. However, anthropogenic water usage, such as for irrigation, is still not or poorly parameterized in regional and larger-scale land surface models (LSM). By contrast, satellite observations are directly affected by, and hence potentially able to detect, irrigation as they sense the entire integrated soil-vegetation system. By integrating satellite observations and fine-scale modelling it could thus be possible to improve estimation of irrigation amounts at the desired spatial-temporal scale. In this study we tested the potential information offered by Sentinel-1 backscatter observations to improve irrigation estimates, in the framework of a data assimilation (DA) system composed by the Noah-MP LSM, equipped with a sprinkler irrigation scheme, and a backscatter operator represented by a Water Cloud Model (WCM), as part of the NASA Land Information System (LIS). The calibrated WCM was used as an observation operator in the DA system to map model surface soil moisture and leaf area index (LAI) into backscatter predictions and, conversely, map observation-minus-forecast backscatter residuals back to updates in soil moisture and LAI through an ensemble Kalman filter (EnKF). The benefits of Sentinel-1 backscatter observation in two different polarizations (VV and VH) were tested in two separate DA experiments, performed over two irrigated sites, the first one located in the Po Valley (Italy) and the second one located in northern Germany. The results confirm that VV backscatter has a stronger link with soil moisture than VH backscatter, whereas VH backscatter observations introduce larger updates in the vegetation state variables. The backscatter DA introduced both improvements and degradations in soil moisture, evapotranspiration and irrigation estimates. The spatial and temporal scale had large impacts on the analysis, with more contradicting results obtained for the evaluation at the fine agriculture scale (i.e., field scale). Above all, this study sheds light on the limitations resulting from a poorly-parameterized sprinkler irrigation scheme which prevents improvements in the irrigation simulation due to DA, and points to future developments needed to improve the system.

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Is it Possible to Quantify Irrigation Water-Use by Assimilating a High-Resolution Soil Moisture Product?

Das

Jalilvand

Abolafia‐Rosenzweig

et al. 2022

Preprint

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Irrigation is the largest human intervention in the water cycle that can modulate climate extremes, yet global irrigation water use (IWU) remains largely unknown. Microwave remote sensing offers a practical way to quantify IWU by monitoring changes in soil moisture caused by irrigation. This study evaluates the ability to quantify IWU by assimilating high-resolution (1km) SMAP-Sentinel 1 (SMAP-S1) remotely sensed soil moisture with a physically-based land surface model (LSM) using a particle batch smoother (PBS). A suite of synthetic experiments is devised to evaluate different error sources. Results from the synthetic experimentation show that unbiased simulations with known irrigation timing can produce an accurate irrigation estimate with a mean annual bias of 0.45% and the mean R 2 of 96.5%, relative to observed IWU. Unknown irrigation timing can significantly deteriorate the model performance by increasing the mean annual bias to 23% and decreasing the mean R 2 to 36%. In realworld experiments, the PBS data assimilation approach provides a mean bias of -18.6% when the timing of irrigation water use is known. This underestimation is possibly attributable to missing part of the irrigation signal. Yet, significantly higher irrigation was estimated over the irrigated pixels compared to the non-irrigated pixels, indicating that data assimilation can skillfully convey irrigation signals to LSMs. LSM calibration provides a 10% improvement to soil moistrue RMSE relative to the open-loop simulation. PBS data assimilation provides an additional 50% improvement to simulated soil moisture RMSE by correcting the model state and superimposing the optimal (unmodeled) irrigation on precipitation forcing.

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

Cited by 96 publications

References 143 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

Challenges and benefits of quantifying irrigation through the assimilation of Sentinel-1 backscatter observations into Noah-MP

Is it Possible to Quantify Irrigation Water-Use by Assimilating a High-Resolution Soil Moisture Product?

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