One decade (2011–2020) of European agricultural water stress monitoring by MSG-SEVIRI: workflow implementation on the Virtual Earth Laboratory (VLab) platform

Figures & data

Table 1. SEVIRI ET satellite observations utilized in this study and their main characteristics.

ET products	LSA SAF product code	Number of observations	Temporal coverage	Spatial coverage and resolution
SEVIRI-ET0	METREF LSA-303	3634	1 January 2011–31 December 2020	MSG disk (3 km at nadir)
SEVIRI-ETa	DMET LSA-312	1752	1 January 2011–10 November 2015	Europe area (3 km at nadir)
		1875	11 November 2015–31 December 2020	MSG disk (3 km at nadir)

Table 2. Classification of drought severity based on ESI value.

ESI value	Class
ESI ≥ 2	Extreme wet
1.5 ≤ ESI < 2	Severe wet
1 ≤ ESI < 1.5	Moderate wet
0 ≤ ESI < 1	Mild wet
–1 ≤  ESI < 0	Mild drought
–1.5 ≤ ESI < –1	Moderate drought
–2 ≤ ESI < –1.5	Severe drought
ESI ≤ –2	Extreme drought

Source: Adopted from (Santos et al. 2019).

Figure 1. Monthly water stress workflow implemented in VLab.

Figure 2. Representative time series maps of water stress levels generated in VLab for the month of July across Europe in 2011 (a), 2012 (b), 2013 (c), 2014 (d), 2015 (e), 2016 (f), 2017 (g), 2018 (h), 2019 (i) and 2020 (j).

Table 3. A representative example (July 2018) of text report (table) output file at the national level generated from the monthly water stress model executed in VLab platform.

Country	Extreme wet	Severe wet	Moderate wet	Mild wet	Mild drought	Moderate drought	Severe drought	Extreme drought
Austria	0.16	3.37	11.12	36.95	32.41	10.28	4.36	1.35
Belgium	0	0	0	0.21	4.46	9.18	28.62	57.53
Bulgaria	0.97	2	10.6	66.83	19.32	0.28	0	0
Croatia	0.03	0.37	7.81	55.18	31.57	4.84	0.21	0
Cyprus	0	0.42	4.03	36.86	58.69	0	0	0
Czechia	0	0.05	0.37	3.04	12.12	20.82	33.99	29.61
Denmark	0	0	0.29	0.95	10.43	9	19.59	59.75
Estonia	0	0	0	6.89	18.92	17.35	29.4	27.44
Finland	0.07	0.55	3.27	26.71	33.01	18.26	13.33	4.8
France	0.01	0.27	2.23	19.73	45.13	18.42	11.81	2.39
Germany	0.01	0.13	0.3	1.87	11.14	16.19	24.85	45.49
Greece	0.18	3.32	7.79	55.02	33.04	0.63	0.03	0
Hungary	0.01	1.15	8.64	41.88	32.53	12.08	3.57	0.15
Ireland	0	0.19	1.13	13.01	42.19	19.62	15.06	8.8
Italy	0.16	0.7	2.88	29.89	51.49	10.93	2.61	1.34
Latvia	0	0	0.03	1.1	12.25	21.28	37.6	27.75
Liechtenstein	0	8.33	25	41.67	25	0	0	0
Lithuania	0	0	0.05	3.71	34.2	26.25	23.11	12.68
Luxembourg	0	0	0	0	26.24	57.92	15.35	0.5
Macedonia	0	2.45	8.16	53.21	33.51	2.51	0.17	0
Malta	0	0	0	0	100	0	0	0
Montenegro	0	0	2.54	82.33	15.13	0	0	0
Netherlands	0	0	0	0.86	8.36	11.14	31.31	48.33
Norway	0.26	2.06	6.21	26.9	19.12	10.45	11.96	23.06
Poland	0	0	0.05	8.34	51.33	22.1	13.6	4.57
Portugal	5.02	7.1	8.72	41.85	37.06	0.23	0.02	0
Romania	0.2	0.78	5.8	52.81	34.09	4.02	1.72	0.56
Slovakia	0	0.03	0.11	6.97	56.06	25.84	9.61	1.39
Slovenia	0	0.07	1.84	45.88	40.3	8.1	3.74	0.07
Spain	0.29	1.88	5.52	22.23	58.6	9.96	1.43	0.1
Sweden	0.03	0.31	1.13	6.51	21.83	21.46	24.52	24.22
Switzerland	0.36	3.04	5.15	20	32.05	13.73	10.1	15.58
Turkey	0.03	1.12	4.77	26.45	61.01	5.86	0.7	0.05
United Kingdom	0.02	0.46	1.95	15.31	20.69	11.91	14.28	35.38

Notes: The values in the table present the percentage of the land area exposed to various water stress conditions at national levels (i.e. the fraction of each stress class in the country).

Figure 3. Created a dashboard (a snapshot) for monthly water stress levels at the European scale. For more information, the reader is referred to the interactive dashboard at (https://geoessential.unepgrid.ch/mapstore/#/dashboard/105/; last access: 23 February 2022).

Santos, C. A. G., R. M. Brasil Neto, R. M. da Silva, and D. C. dos Santos. 2019. “Innovative Approach for Geospatial Drought Severity Classification: A Case Study of Paraíba State, Brazil.” Stochastic Environmental Research and Risk Assessment 33 (32): 545–562. doi:10.1007/s00477-018-1619-9.

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Data availability statement

All the needed inputs data that support the findings of this study are published and openly available through Zenodo repository (https://doi.org/10.5281/zenodo.6239078; last access: 23 February 2022). Moreover, the complete data is available and can be accessed through the EUMETSAT Satellite Application Facility on Land Surface Analysis (LSA SAF) database (https://landsaf.ipma.pt/en/data/; last access: 23 February 2022).