Irrigated area mapping in heterogeneous landscapes with MODIS time series, ground truth and census data, Krishna Basin, India

Biggs, Trent W.; Thenkabail, Prasad S.; Gumma, Murali Krishna; Scott, Christopher A.; Parthasaradhi, G.; Turral, Hugh

doi:10.1080/01431160600851801

Cited by 151 publications

(127 citation statements)

References 29 publications

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“…This estimated increase in base flow during the post-monsoon season is supported by observed cropping and irrigation activities during this period. Irrigation in double-cropped irrigated areas fed by water from large reservoirs was seen during field visits in the downstream area of the Krishna river basin and from independent observations using remote sensing of vegetation phenology (Biggs et al, 2006).…”

Section: Discussionmentioning

confidence: 99%

Detecting the long-term impacts from climate variability and increasing water consumption on runoff in the Krishna river basin (India)

Bouwer

Aerts

Droogers³

et al. 2006

Hydrol. Earth Syst. Sci.

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Abstract. Variations in climate, land-use and water consumption can have profound effects on river runoff. There is an increasing demand to study these factors at the regional to river basin-scale since these effects will particularly affect water resources management at this level. This paper presents a method that can help to differentiate between the effects of man-made hydrological developments and climate variability (including both natural variability and anthropogenic climate change) at the basin scale. We show and explain the relation between climate, water consumption and changes in runoff for the Krishna river basin in central India. River runoff variability due to observed climate variability and increased water consumption for irrigation and hydropower is simulated for the last 100 years using the STREAM water balance model. Annual runoff under climate variability is shown to vary only by about 14-34 millimetres (6-15%). It appears that reservoir construction after 1960 and increasing water consumption has caused a persistent decrease in annual river runoff of up to approximately 123 mm (61%). Variation in runoff under climate variability only would have decreased over the period under study, but we estimate that increasing water consumption has caused runoff variability that is three times higher.

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Section: Discussionmentioning

confidence: 99%

Detecting the long-term impacts from climate variability and increasing water consumption on runoff in the Krishna river basin (India)

Bouwer

Aerts

Droogers³

et al. 2006

Hydrol. Earth Syst. Sci.

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“…However, mapping of irrigated areas proved to be a challenge due to the diverse range of irrigated plot sizes, crops and water sources used by farmers [13,14]. The use of Landsat imagery proved to be fast, cheap and successful in mapping small irrigated areas.…”

Section: Introductionmentioning

confidence: 99%

Mapping Irrigated Areas of Ghana Using Fusion of 30 m and 250 m Resolution Remote-Sensing Data

et al. 2011

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Maps of irrigated areas are essential for Ghana's agricultural development. The goal of this research was to map irrigated agricultural areas and explain methods and protocols using remote sensing. Landsat Enhanced Thematic Mapper (ETM+) data and time-series Moderate Resolution Imaging Spectroradiometer (MODIS) data were used to map irrigated agricultural areas as well as other land use/land cover (LULC) classes, for Ghana. Temporal variations in the normalized difference vegetation index (NDVI) pattern obtained in the LULC class were used to identify irrigated and non-irrigated areas. First, the temporal variations in NDVI pattern were found to be more consistent in long-duration irrigated crops than with short-duration rainfed crops due to more assured water supply for irrigated areas. Second, surface water availability for irrigated areas is dependent on shallow dug-wells (on river banks) and dug-outs (in river bottoms) that affect the timing of crop sowing and growth stages, which was in turn reflected in the seasonal NDVI pattern. A decision tree approach using Landsat 30 m one time data fusion with MODIS 250 m OPEN ACCESSRemote Sens. 2011, 3 817 time-series data was adopted to classify, group, and label classes. Finally, classes were tested and verified using ground truth data and national statistics. Fuzzy classification accuracy assessment for the irrigated classes varied between 67 and 93%. An irrigated area derived from remote sensing (32,421 ha) was 20-57% higher than irrigated areas reported by Ghana's Irrigation Development Authority (GIDA). This was because of the uncertainties involved in factors such as: (a) absence of shallow irrigated area statistics in GIDA statistics, (b) non-clarity in the irrigated areas in its use, under-development, and potential for development in GIDA statistics, (c) errors of omissions and commissions in the remote sensing approach, and (d) comparison involving widely varying data types, methods, and approaches used in determining irrigated area statistics using GIDA and remote sensing. Extensive field campaigns to help in better classification and validation of irrigated areas using high (30 m ) to very high (<5 m) resolution remote sensing data that are fused with multi temporal data like MODIS are the way forward. This is especially true in accounting for small yet contiguous patches of irrigated areas from dug-wells and dug-outs.

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“…Thus, thematic improvement of global land cover databases is of great importance. There have been ongoing efforts to extend MODIS land cover databases (Biggs et al, 2006;Potgieter et al, 2007;Wardlow et al, 2007;Wardlow and Egbert, 2008;Pittman et al, 2010;He and Bo, 2011;Gumma et al, 2011). For natural vegetation, global high-resolution databases are becoming available (e.g.…”

Section: Discussionmentioning

confidence: 99%

Deriving a per-field land use and land cover map in an agricultural mosaic catchment

Seo

Bogner

Poppenborg

et al. 2014

Earth Syst. Sci. Data

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Abstract. Detailed data on land use and land cover constitute important information for Earth system models, environmental monitoring and ecosystem services research. Global land cover products are evolving rapidly; however, there is still a lack of information particularly for heterogeneous agricultural landscapes. We censused land use and land cover field by field in the agricultural mosaic catchment Haean in South Korea. We recorded the land cover types with additional information on agricultural practice. In this paper we introduce the data, their collection and the post-processing protocol. Furthermore, because it is important to quantitatively evaluate available land use and land cover products, we compared our data with the MODIS Land Cover Type product (MCD12Q1). During the studied period, a large portion of dry fields was converted to perennial crops. Compared to our data, the forested area was underrepresented and the agricultural area overrepresented in MCD12Q1. In addition, linear landscape elements such as waterbodies were missing in the MODIS product due to its coarse spatial resolution. The data presented here can be useful for earth science and ecosystem services research. The data are available at the public repository Pangaea

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Irrigated area mapping in heterogeneous landscapes with MODIS time series, ground truth and census data, Krishna Basin, India

Cited by 151 publications

References 29 publications

Detecting the long-term impacts from climate variability and increasing water consumption on runoff in the Krishna river basin (India)

Detecting the long-term impacts from climate variability and increasing water consumption on runoff in the Krishna river basin (India)

Mapping Irrigated Areas of Ghana Using Fusion of 30 m and 250 m Resolution Remote-Sensing Data

Deriving a per-field land use and land cover map in an agricultural mosaic catchment

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