Assessment of map quality

A common method to assess the quality of a course scale data-set is to compare it with independent information at selected locations on a more detailed scale in order to draw conclusions with respect to the map quality at these locations and extrapolate it to the general map quality. Here, however, all available data on irrigated areas at appropriate scales were used to compile the map itself and therefore could not be used for a quality assessment. Apart from this, it is very difficult to compare the quantitative information as presented on the map (expressed e.g. as area equipped for irrigation in a 5-minute cell in ha) with point information collected for specific positions by ground-truthing (irrigated or rainfed).

To assess the quality of the Global Map of Irrigation Areas, two indicators were developed that take into account the geospatial information density of the base data:

  • Indicator A (IND_A), represents the density of the used subnational irrigation statistics
  • Indicator B (IND_B), represents the density of the available geospatial records on position and extent of irrigated areas

The combination of IND_A and IND_B is used to describe the overall map quality per country for the data layer of area equipped for irrigation. In contrast, map quality of the data layers on area actually irrigated or on the water sources used for irrigation was assessed based on IND_A only, because data were not disaggregated below the level of the subnational statistical units (except for a few countries for which well inventories were available) and therefore IND_B could not be computed.

The density of subnational irrigation statistics can be obtained by calculating the arithmetic mean of the size of the subnational units. However, there are some countries where irrigation is concentrated in some small subnational units while in other very large subnational units of the same country there is no or very little irrigation. To avoid that large subnational units without significant irrigation have a negative impact on indicator A, the size of each subnational statistical unit is weighted by the irrigation density in the subnational unit relative to the irrigation density in the entire country:

IND_A

with:

irridens

where IND_Acountry is the average weighted size of the subnational units in the specific country (ha), areacountry is the surface area of the country (ha), irridensadm is the irrigation density in subnational unit adm (-), irridenscountry is the irrigation density in region the country (-), n is the number of subnational units in the country, irareaadm is the irrigated area in subnational unit adm (ha) and areaadm is the surface area in subnational unit adm (ha).

IND_A equals the average size of all subnational units in a country if the irrigation density is the same all over the country the country. If all irrigated area is concentrated in only one subnational unit, IND_A is equal to the size of this subnational unit. IND_A is lower than the average size of the subnational units if the irrigation density is higher in small subnational units than in the larger subnational units. Lower values of IND_A indicate a better map quality. IND_A was computed for the three data layers (area equipped for irrigation, area actually irrigated, water source on area equipped for irrigation) separately to account for the different resolution of statistical data.

The second indicator (IND_B) was developed to give an estimate on the density of geospatial information used to assign area equipped for irrigation to specific cells within the sub-national units. IND_B was computed as the fraction of irrigated area that could be assigned to specific grid cells by using geospatial records on the position and extent of known irrigation schemes. Higher values of IND_B indicate a better map quality.

Both indicators were assigned a country mark according to the classification in the following table:

Mark    Indicator IND_A (ha)    Indicator IND_B (%)
Excellent< 100 00090 - 100
Very good100 000 - 250 00070 - 90
Good250 000 - 500 00050 - 70
Fair500 000 - 1 000 00025 - 50
Poor1 000 000 - 3 000 00010 - 25
Very poor > 3 000 000< 10

For area equipped for irrigation, a mark for the overall quality was given assuming that the types of information that are reflected by the two indicators can replace each other. The mark for the overall map quality was set to the better of the two marks given according to IND_A and IND_B. If, for example, the location and extent of almost all irrigation projects in a country is known then subnational statistics are not required in addition to prepare a reliable map . On the other hand, if the size of the subnational statistical units is very small (in an extreme case smaller than the map resolution of 5 arc minutes), the overall quality of the map should also be fine even if there are no geospatial records on the position of irrigation schemes within the sub-national units available. However, in version 5 of the global map of irrigation areas the overall map quality mark “excellent” was only assigned when the better mark according to IND_A and IND_B was “excellent” and the other mark “very good” or “excellent” as well.

The resulting marks for the quality of the different data layers were downgraded when the used statistical information referred to years before year 2000. In addition, the final marks assigned for the data layer on area equipped for irrigation were downgraded when there were doubts regarding the reliability of the information used for a specific country (e.g. when statistical data and geospatial data were not consistent, when maps were outdated or statistics from different sources were inconsistent).

The percentage of area equipped for irrigation or area actually irrigated assigned to countries with the marks excellent to very poor is shown in the table below. In general, the quality of the layer on area equipped for irrigation was higher than the quality of the other layers because the resolution of the statistics on area equipped for irrigation was higher and in addition, inventories of irrigation projects or irrigation maps were used to assign area equipped for irrigation to specific grid cells. In contrast, area actually irrigated or statistics on the water source for irrigation were often available at country level only so that the mark for the map quality was mainly depending on the size of the country. Therefore these data layers should only be used for analyses at global scale.

Percentage of total irrigated area assigned to regions of different map quality
Mark    Area equipped for irrigation    Area actually irrigated    Source of water for irrigation
Excellent2.22.44.0
Very good17.12.23.5
Good73.538.531.4
Fair4.81.29.7
Poor2.24.17.0
Very poor0.151.644.5

The quality marks for each specific country are listed in the table below. A more detailed table listing values for IND_A and IND_B and reporting reasons for a downgrading of the marks is available for download here.

Column headings in table below:

Area equipped (ha) = Area equipped for irrigation (ha); Map quality area equipped = Map quality area equipped for irrigation; map quality actually irrigated = Map quality area actually irrigated; map quality source of water = Map quality source of water for irrigation

Country
 
Area
equipped
(ha)
Map quality
area
equipped
Map quality
actually
irrigated
Map quality
source of water
Afghanistan3 199 070goodvery poorvery poor
Albania340 000goodvery goodpoor
Algeria569 418goodvery poorvery poor
Andorra150goodvery goodvery good
Angola80 000poorvery poorvery poor
Antigua and Barbuda130goodvery goodvery good
Argentina1 767 784goodvery goodvery good
Armenia273 530very goodpoorpoor
Australia4 068 965goodvery goodfair
Austria116 050very goodfairpoor
Azerbaijan1 426 000goodvery poorvery poor
Bahrain4 060very goodexcellentexcellent
Bangladesh5 049 400goodvery poorvery good
Barbados1 000goodvery goodvery good
Belarus114 100very poorvery poorvery poor
Belgium23 830goodgoodpoor
Belize3 548goodpoorgood
Benin12 258very goodvery poorvery poor
Bhutan27 685very goodvery poorvery poor
Bolivia (Plurinational
State of)
128 240poorvery poorvery poor
Bosnia and Herzegovina4 630goodvery poorvery poor
Botswana1 439fairvery poorvery poor
Brazil4 463 691goodvery poorexcellent
Brunei Darussalam1 000poorpoorpoor
Bulgaria545 160goodpoorpoor
Burkina Faso25 000fairvery poorvery poor
Burundi21 430fairpoorpoor
Cambodia506 775very goodvery poorvery poor
Cameroon25 654goodvery poorvery poor
Canada1 218 345very goodvery poorvery poor
Cape Verde2 780very goodvery goodfair
Central African Republic135goodvery poorvery poor
Chad30 273fairvery poorvery poor
Chile1 936 402very goodvery goodvery good
China62 392 392goodvery poorvery poor
Colombia900 000fairvery poorvery poor
Comoros130goodgoodvery good
Congo2 000goodvery poorvery poor
Costa Rica103 084poorvery poorvery poor
Côte d'Ivoire72 750fairvery poorvery poor
Croatia9 275goodvery goodvery good
Cuba870 319poorvery poorvery poor
Cyprus55 456excellentvery goodpoor
Czech Republic50 590goodfairpoor
Democratic People's
Republic of Korea
1 460 000poorvery poorvery poor
Democratic Republic
of the Congo
10 500goodvery poorvery poor
Denmark448 818goodgoodvery poor
Djibouti1 012fairgoodvery poor
Dominica0n.a.n.a.n.a.
Dominican Republic306 442goodvery poorvery poor
Ecuador853 332very goodpoorexcellent
Egypt3 422 178very goodvery poorgood
El Salvador52 452very goodvery goodvery good
Equatorial Guinea0n.a.n.a.n.a.
Eritrea21 590goodvery poorvery poor
Estonia1 363fairvery poorvery poor
Ethiopia290 729goodvery poorvery poor
Fiji4 000very poorvery poorvery poor
Finland103 800fairvery poorvery poor
France2 906 081very goodpoorpoor
French Guiana (France)5 931very poorvery poorvery poor
Gabon4 450fairvery poorvery poor
Gambia2 149very goodvery poorvery poor
Georgia432 790goodvery poorvery poor
Germany515 731goodvery goodvery good
Ghana59 000poorvery poorvery poor
Greece1 544 530goodpoorpoor
Grenada219very goodexcellentvery good
Guadeloupe (France)6 635very goodvery goodvery good
Guam (USA)312goodexcellentexcellent
Guatemala142 499fairvery poorgood
Guinea94 914fairvery poorvery poor
Guinea-Bissau22 558fairvery poorvery poor
Guyana150 134goodvery poorvery poor
Haiti91 502goodfairvery poor
Honduras81 631fairvery poorgood
Hungary292 147goodgoodpoor
Iceland0n.a.n.a.n.a.
India61 907 846goodgoodgood
Indonesia6 722 299fairvery poorvery poor
Iran (Islamic
Republic of)
8 847 818very goodvery poorvery poor
Iraq3 525 000goodvery poorvery poor
Ireland1 100fairvery poorvery poor
Israel183 407very goodpoorpoor
Italy3 892 202very goodgoodpoor
Jamaica26 650very goodvery poorvery poor
Japan2 834 956goodfairvery poor
Jordan83 450very goodvery poorgood
Kazakhstan2 482 500goodvery poorvery poor
Kenya103 203fairvery poorvery poor
Kuwait10 142very goodvery poorpoor
Kyrgyzstan1 045 131very goodvery poorvery poor
Lao People's
Democratic Republic
309 657goodfairvery poor
Latvia1 150fairvery poorvery poor
Lebanon104 010goodpoorvery good
Lesotho2 638fairvery poorvery poor
Liberia2 100very poorvery poorvery poor
Libya470 000goodvery poorvery poor
Liechtenstein0n.a.n.a.n.a.
Lithuania4 416fairvery poorvery poor
Luxembourg27fairfairfair
Macao0n.a.n.a.n.a.
Madagascar1 086 291poorvery poorvery poor
Malawi56 390fairvery poorvery poor
Malaysia362 687goodvery poorvery poor
Maldives0n.a.n.a.n.a.
Mali235 791fairvery poorvery poor
Malta2 300very goodexcellentexcellent
Martinique (France)6 170very goodvery goodvery good
Mauritania45 012goodvery poorvery poor
Mauritius21 543very goodvery goodvery good
Mexico6 817 240excellentexcellentexcellent
Monaco0n.a.n.a.n.a.
Mongolia84 300poorvery poorvery poor
Montenegro2 115poorpoorpoor
Morocco1 484 160goodvery poorpoor
Mozambique118 120very goodvery poorvery poor
Myanmar2 110 000fairvery poorvery poor
Namibia7 573very goodvery poorvery poor
Nepal1 168 349goodvery poorvery good
Netherlands476 315very goodgoodfair
New Zealand619 294goodgoodpoor
Nicaragua94 240fairvery poorvery poor
Niger73 663poorvery poorvery poor
Nigeria293 117fairvery poorvery poor
Northern Marianna
Islands (USA)
159goodexcellentexcellent
Norway134 396poorpoorpoor
Occupied
Palestinian Territory
23 484very goodexcellentfair
Oman58 850very goodvery poorvery poor
Pakistan16 725 843goodgoodfair
Panama34 626poorvery poorvery poor
Papua New Guinea0n.a.n.a.n.a.
Paraguay67 000very poorvery poorvery poor
Peru1 729 069goodvery poorfair
Philippines1 879 084goodvery poorgood
Poland134 050poorpoorvery poor
Portugal792 008goodgoodpoor
Puerto Rico (USA)36 997very goodexcellentexcellent
Qatar12 935excellentpoorpoor
Republic of Korea806 475goodvery poorvery poor
Republic of Moldova307 000goodvery poorvery poor
Réunion (France)8 811very goodgoodfair
Romania2 149 903goodpoorvery poor
Russian Federation2 375 200poorvery poorvery poor
Rwanda8 500very goodpoorpoor
Saint Kitts and Nevis18very goodvery goodvery good
Saint Lucia3 321very goodexcellentvery good
Saint Vincent and the Grenadines0n.a.n.a.n.a.
San Marino0n.a.n.a.n.a.
Sao Tome and Principe9 700goodvery goodvery good
Saudi Arabia1 348 696very goodvery poorvery poor
Senegal119 680fairvery poorvery poor
Serbia86 311poorpoorvery poor
Seychelles260very goodexcellentexcellent
Sierra Leone29 360very poorvery poorvery poor
Singapore0n.a.n.a.n.a.
Slovakia225 310fairgoodfair
Slovenia15 643very goodpoorpoor
Somalia200 000goodvery poorvery poor
South Africa1 498 000very goodvery poorvery poor
Spain3 575 488very goodvery poorvery poor
Sri Lanka600 730very goodvery poorgood
Sudan and South Sudan1 863 000goodvery poorvery poor
Suriname51 180very poorvery poorvery poor
Swaziland49 843very goodpoorpoor
Sweden188 470fairpoorpoor
Switzerland55 000goodgoodvery poor
Syrian Arab Republic1 489 000goodpoorpoor
Tajikistan742 051very goodvery poorvery poor
Thailand6 414 880very goodvery poorfair
The former Yugoslav
Republic of Macedonia
127 800very goodpoorpoor
Timor-Leste33 698goodpoorpoor
Togo7 300poorvery poorvery poor
Trinidad and Tobago3 600very goodpoorpoor
Tunisia455 070goodvery poorvery poor
Turkey5 215 144very goodpoorpoor
Turkmenistan1 990 800very goodvery poorvery poor
Uganda9 150goodvery poorvery poor
Ukraine2 395 500fairvery poorvery poor
United Arab Emirates230 841goodpoorvery poor
United Kingdom246 894goodpoorpoor
United Republic
of Tanzania
189 047fairvery poorpoor
United States of America28 375 752goodgoodgood
United States
Virgin Islands (USA)
185very goodexcellentexcellent
Uruguay243 419fairvery poorvery poor
Uzbekistan4 198 000very goodvery poorvery poor
Venezuela (Bolivarian
Republic of)
759 524very goodexcellentvery poor
Viet Nam4 585 500goodvery poorvery poor
Yemen813 951goodpoorpoor
Zambia155 912fairvery poorvery poor
Zimbabwe173 513poorvery poorvery poor


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