Changes in agricultural cropland areas between a water-surplus year and a water-deficit year impacting food security, determined using MODIS 250 m time-series data and spectral matching techniques, in the Krishna River basin (India)

Gumma, Murali Krishna; Thenkabail, Prasad S.; Muralikrishna, Iyyanki V.; Velpuri, Manohar; Gangadhararao, Parthasarathi T.; Dheeravath, Venkateswarlu; Biradar, Chandrashekhar; Nalan, Sreedhar Acharya; Gaur, Anju

doi:10.1080/01431161003749485

Cited by 39 publications

(52 citation statements)

References 23 publications

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“…Even in case of high herbaceous cover, the active period of the steppes in the Kashkadarya Province is too short for higher signals in the annual NDVI averages. More sophisticated rule sets become necessary in more heterogeneous landscapes with other climate conditions and subsequently more vegetation classes, as previously shown by Gumma et al [14].…”

Section: Knowledge-based Detection Of Irrigated Landmentioning

confidence: 68%

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Reconstructing the Spatio-Temporal Development of Irrigation Systems in Uzbekistan Using Landsat Time Series

Edlinger

Conrad

Lamers³

et al. 2012

Remote Sensing

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Abstract:The expansion of irrigated agriculture during the Soviet Union (SU) era made Central Asia a leading cotton production region in the world. However, the successor states of the SU in Central Asia face on-going environmental damages and soil degradation that are endangering the sustainability of agricultural production. With Landsat MSS and TM data from 1972/73, 1977, 1987, 1998, and 2000 the expansion and densification of the irrigated cropland could be reconstructed in the Kashkadarya Province of Uzbekistan, Central Asia. Classification trees were generated by interpreting multitemporal normalized difference vegetation index data and crop phenological knowledge. Assessments based on image-derived validation samples showed good accuracy. Official statistics were found to be of limited use for analyzing the plausibility of the results, because they hardly represent the area that is cropped in the very dry study region. The cropping area increased from OPEN ACCESS Remote Sens. 2012, 4 3973 134,800 ha in 1972/73 to 470,000 ha in 2009. Overlaying a historical soil map illustrated that initially sierozems were preferred for irrigated agriculture, but later the less favorable solonchaks and solonetzs were also explored, illustrating the strategy of agricultural expansion in the Aral Sea Basin. Winter wheat cultivation doubled between 1987 and 1998 to approximately 211,000 ha demonstrating its growing relevance for modern Uzbekistan. The spatial-temporal approach used enhances the understanding of natural conditions before irrigation is employed and supports decision-making for investments in irrigation infrastructure and land cultivation throughout the Landsat era.

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Section: Knowledge-based Detection Of Irrigated Landmentioning

confidence: 68%

“…Frequently moderateresolution sensors such as MODIS or SPOT Vegetation have been used for mapping irrigated croplands (among others [12][13][14][15]). These sensors cover extensive areas over long periods thus permitting to contribute to global area coverage even in intervals of several days.…”

Section: Introductionmentioning

confidence: 99%

Reconstructing the Spatio-Temporal Development of Irrigation Systems in Uzbekistan Using Landsat Time Series

Edlinger

Conrad

Lamers³

et al. 2012

Remote Sensing

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“…Decision tree (DT) algorithms [24,39] involve factors such as NDVI, individual band reflectivity, and thermal temperatures to identify and label a class and/or resolve a mixed class. A rule-based DT algorithm (e.g., Figure 3) helps in identifying, grouping, and labeling many classes.…”

Section: Grouping Of Classes With Decision Tree Algorithmsmentioning

confidence: 99%

“…The data were acquired in 12-bit (0 to 4,096 levels), and were stretched to 16-bit (0 to 65,536 levels). Further processing steps are described in [22,24]. …”

Section: Modis 250-m Datamentioning

confidence: 99%

“…Biggs et al [13] used MODIS time series combined with ground truth data, agricultural census data and Landsat Thematic Mapper (TM) data to map surface-water irrigation, groundwater irrigation and rainfed ecosystems of the Krishna River basin in the southern Indian peninsula. Quansah et al [24] stressed the importance of NDVI time series to identify and separate different types of irrigation, including surface water, shallow dug wells, lift irrigation and groundwater irrigation.…”

Section: Introductionmentioning

confidence: 99%

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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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Impact of flooded rice paddy on remotely sensed evapotranspiration in the Krishna River basin, India

Teluguntla

Ryu

George

et al. 2020

Hydrological Processes

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Evapotranspiration (ET) is one of the major water exchange processes between the earth's surface and the atmosphere. ET is a combined process of evaporation from open water bodies, bare soil and plant surfaces, and transpiration from vegetation. Remote sensing-based ET models have been developed to estimate spatially distributed ET over large regions, however, many of them reportedly underestimate ET over semi-arid regions (Jamshidi et al., Journal of Hydrometeorology, 2019, 20, 947-964). In this work, we show that underestimation of ET can occur due to the open water evaporation from flooded rice paddies ignored in the existing ET models. To address the gap in ET estimation, we have developed a novel approach that accounts for the missing ET component over flooded rice paddies. Our method improved ET estimates by a modified Penman-Monteith algorithm that considered the fraction of open water evaporation from flooded rice paddies. Daily ET was calculated using ground based meteorological data and the MODIS satellite data over the Krishna River Basin. Seasonal and annual ET values over the Krishna Basin were compared with two different ET algorithms. ET estimates from these two models were also compared for different crop combinations. Results were validated with flux tower-based measurements from other studies. We have identified a 17 mm/year difference in average annual ET over the Krishna River Basin with this new ET algorithm. This is very critical in basin scale water balance analysis and water productivity studies. K E Y W O R D S evapotranspiration, flooded rice paddy, Krishna River basin, MODIS, remote sensing ET

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Changes in agricultural cropland areas between a water-surplus year and a water-deficit year impacting food security, determined using MODIS 250 m time-series data and spectral matching techniques, in the Krishna River basin (India)

Cited by 39 publications

References 23 publications

Reconstructing the Spatio-Temporal Development of Irrigation Systems in Uzbekistan Using Landsat Time Series

Reconstructing the Spatio-Temporal Development of Irrigation Systems in Uzbekistan Using Landsat Time Series

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

Impact of flooded rice paddy on remotely sensed evapotranspiration in the Krishna River basin, India

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