Global irrigated area map (GIAM), derived from remote sensing, for the end of the last millennium

Thenkabail, Prasad S.; Biradar, Chandrashekhar; Noojipady, Praveen; Dheeravath, Venkateswarlu; Li, Yuanjie; Velpuri, Manohar; Gumma, Murali Krishna; Reddy, G. P. Obi; Turral, Hugh; Cai, Xueliang; Vithanage, Jagath; Schull, Mitchell A.; Dutta, Ratna

doi:10.1080/01431160802698919

Cited by 368 publications

(336 citation statements)

References 25 publications

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“…Category I occupies 0.8% of all land surface. A detailed world irrigation map was produced by Thenkabail et al (2009), whose results are similar in relation to the arid regions identified in this study. Category I does not always accurately present cultivated areas.…”

Section: Category I (Irrigated Areas and Oases)mentioning

confidence: 99%

Classification of arid lands, including soil degradation and irrigated areas, based on vegetation and aridity indices

Gamo

Shinoda

Maeda

2013

International Journal of Remote Sensing

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It is preferable to prepare internally consistent maps of arid regions on a global scale in order to understand the present conditions of arid regions, especially deserts and soil degradation areas. We attempted to delimit arid regions at a global scale by combining climate data, i.e. aridity index (AI), and vegetation data, i.e. vegetation index. The annual AI was estimated by the ratio of mean annual precipitation to mean annual potential evapotranspiration, using the Thornthwaite method. The long-term mean of yearly maximum normalized difference vegetation index (NDVI ymx ) was used as an indicator of the vegetation condition. Arid regions of the world were classified into four categories, namely A, severe deserts, where both aridity and vegetation indices are very small; G, semi-arid regions, where the vegetation index is proportionally related to the AI; I, irrigated areas and oases, where the vegetation is relatively abundant despite severe dryness; and S, soil degradation areas, where the vegetation is poor despite relatively humid conditions. The Sahel from Niger to Chad, the Sahel in Darfur, and the Ordos Plateau in China are within Category S. The standard deviation of NDVI ymx is very small/large in severe deserts/semi-arid areas, respectively. Thus, the Sahara desert was clearly distinguished from the Sahel; the latter belongs to Category G and drought occurs frequently here. In Category S zones, the standard deviation of NDVI ymx is relatively small compared with that within the Category G zone because the return rainfall does not seem to promptly restore productivity. Category S was divided into three subdivisions according to the degree of degradation, expressed by the ratio of the AI to vegetation index. Category G was also divided into four classes, according to degree of vegetation (or aridity). The distribution of Category S is comparable to the soil degradation areas mapped by Global Assessment of Human-Induced Soil Degradation (GLASOD) data. True deserts, where the standard deviation of NDVI ymx is very small, were selected from the 'severe desert' group. Desert areas were classified as true deserts, severe deserts, grassland deserts (Category G), and soil degradation deserts (Category S).

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Section: Category I (Irrigated Areas and Oases)mentioning

confidence: 99%

Classification of arid lands, including soil degradation and irrigated areas, based on vegetation and aridity indices

Gamo

Shinoda

Maeda

2013

International Journal of Remote Sensing

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“…Remote sensing is widely used to classify and map croplands over regional areas (Thenkabail et al 2009;Thenkabail, Gamage, and Smakhin 2004). The methods exploit both the absolute greenness as well as the greenness dynamics, or land surface phenology, of the disparate crop types (Chang et al 2007;Shao et al 2010;Turker and Arikan 2005;Wardlow, Egbert, and Kastens 2007;Zhong, Gong, and Biging 2014).…”

Section: Introductionmentioning

confidence: 99%

Fallow-land Algorithm based on Neighborhood and Temporal Anomalies (FANTA) to map planted versus fallowed croplands using MODIS data to assist in drought studies leading to water and food security assessments

Wallace

Thenkabail

Rodriguez

et al. 2017

GIScience & Remote Sensing

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An important metric to monitor for optimizing water use in agricultural areas is the amount of cropland left fallowed, or unplanted. Fallowed croplands are difficult to model because they have many expressions; for example, they can be managed and remain free of vegetation or be abandoned and become weedy if the climate for that season permits. We used 250 m, 8-day composite Moderate Resolution Imaging Spectroradiometer normalized difference vegetation index data to develop an algorithm that can routinely map cropland status (planted or fallowed) with over 75% user's and producer's accuracies. The Fallow-land Algorithm based on Neighborhood and Temporal Anomalies (FANTA) compares the current greenness of a cultivated pixel to its historical greenness and to the greenness of all cultivated pixels within a defined spatial neighborhood, and is therefore transportable across space and through time. This article introduces FANTA and applies it to California from 2001 to 2015 as a case study for use in data-poor places and for use in historical modeling. Timely and accurate knowledge of the extent of fallowing can provide decision makers with insights and knowledge to mitigate the impacts of drought and provide a scientific basis for effective management response. This study is part of the WaterSMART (Sustain and Manage America's Resources for Tomorrow) project, an interdisciplinary and collaborative research effort focused on improving water conservation and optimizing water use.

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“…To secure the environmental flow towards the lower stream under different hydrological conditions, a water allocation plan has been implemented since 2000. For example, in a normal year (i.e., the annual flow from Yingluoxia reaches 1580 million m 3 ), the flow through Zhengyixia should be no less than 950 million m 3 . Since 2000, diversion of the In the midstream area, the most common crops are corn and winter wheat.…”

Section: Study Areamentioning

confidence: 99%

“…Currently, the irrigated cropland produces more than 40% of the total cereal yield worldwide [1,2]. Globally, about 54% of the total irrigated area is dependent on surface water (SW) such as rivers, lakes, artificial canals and reservoirs [3]. Meanwhile, in arid and semi-arid regions of the world where surface water is not abundantly available, groundwater (GW) abstraction is often required for irrigation purposes.…”

Section: Introductionmentioning

confidence: 99%

Joint Operation of Surface Water and Groundwater Reservoirs to Address Water Conflicts in Arid Regions: An Integrated Modeling Study

Tian

Xiong

et al. 2018

Water

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At the basin scale, the operation of surface water reservoirs rarely takes groundwater aquifers into consideration, which can also be regarded as reservoirs underground. This study investigates the impact of reservoir operation on the water cycle and evaluates the effect of the joint operation of surface water and groundwater reservoirs on the water conflict in arid regions through an integrated modeling approach. The Heihe River Basin (HRB) in northwestern China is selected as the study area. Our results show that the ecological operational strategies of a reservoir under construction in the upper HRB have a direct impact on the agricultural water uses and consequently affect other hydrological processes. The ecological operation strategy with a smaller water release and a longer duration is beneficial to securing the environmental flow towards the downstream area and to replenishing aquifers. With the joint operation of surface water and groundwater reservoirs, a balance among the agriculture water need, the groundwater sustainability in the Middle HRB and the ecological water need in the Lower HRB can be flexibly achieved. However, the joint operation can hardly improve the three aspects simultaneously. To resolve the water conflict in HRB, additional engineering and/or policy measures are desired.

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Global irrigated area map (GIAM), derived from remote sensing, for the end of the last millennium

Cited by 368 publications

References 25 publications

Classification of arid lands, including soil degradation and irrigated areas, based on vegetation and aridity indices

Classification of arid lands, including soil degradation and irrigated areas, based on vegetation and aridity indices

Fallow-land Algorithm based on Neighborhood and Temporal Anomalies (FANTA) to map planted versus fallowed croplands using MODIS data to assist in drought studies leading to water and food security assessments

Joint Operation of Surface Water and Groundwater Reservoirs to Address Water Conflicts in Arid Regions: An Integrated Modeling Study

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