Journal of Geographical Sciences ›› 2015, Vol. 25 ›› Issue (12): 1479-1506.doi: 10.1007/s11442-015-1247-y
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USMAN M1, LIEDL R1, A SHAHID M2, ABBAS A3,2
Received:
2014-11-08
Accepted:
2015-06-10
Online:
2015-12-31
Published:
2015-12-31
About author:
Author: Ren Huiru (1983-), PhD Candidate, specialized in coastal environment and modeling. E-mail:
USMAN M, LIEDL R, A SHAHID M, ABBAS A. Land use/land cover classification and its change detection using multi-temporal MODIS NDVI data[J].Journal of Geographical Sciences, 2015, 25(12): 1479-1506.
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Table 1
Summary of selected regional to country level Land Use/Land Cover datasets"
Sr. No | Name of the dataset | Data description | Source | Coverage/ Spatial scale |
---|---|---|---|---|
1 | FAOSTAT | Agricultural lands | http://faostat3.fao.org/home/E | Country level |
2 | FORIS | Inland waters, forest and woodland | http://www.fao.org/forestry/site/fra/en | Country level |
3 | GLCC | Built-up areas, water resources, barren or sparsely vegetated areas, grasslands, open shrub lands, forests | http://edc2.usgs.gov/glcc/glcc.php | 1 km × I km |
4 | GLCC-2000 | Forests, cultivated and managed areas, bare areas, water bodies, urban and built-up areas | http://forobs.jrc.ec.europa.eu/ | 1 km × 1 km |
5 | MOD12Q1 Land Cover and Land Cover Dynamics products | Land cover change vectors | http://modis.gsfc.nasa.gov/about/ | 500 m × 500 m |
6 | GISS | Forests, cultivated land and grasslands | http://data.giss.nasa.gov/landuse/ | 1o (≈ 111 km) |
7 | GLCF | Land tree cover, forest cover change, Geo cover | http://glcfapp.glcf.umd.edu:8080/esdi/index.jsp | 500 m × 500 m |
8 | PELCOM | Coniferous, deciduous and mixed forests, grassland, rainfed and irrigated arable land, perennial crops, shrub, barren land, ice and snow cover, wetlands, inland waters, sea and urban area | www.geo-informatie.nl/projects/pelcom/public/index.htm | 1 km × 1 km (Covers only European countries) |
9 | Global land cover map | Cultivated areas, built-up lands, forests, barren lands, etc. | Yu et al. (2013); Gong et al. (2013) | 30 m × 30 m |
Table 2
Summary of producer’s and user’s accuracies for different classes of rabi and kharif seasons"
Year & Accuracy | Season & Class | ||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Producer’s accuracy (%) | User’s accuracy (%) | ||||||||||||||||
2005-06 | 2006-07 | 2007-08 | 2008-09 | 2009-10 | 2010-11 | 2011-12 | Avg. | 2005-06 | 2006-07 | 2007-08 | 2008-09 | 2009-10 | 2010-11 | 2011-12 | Avg. | ||
Rabi | 1 | 75 | 83.3 | 77.8 | 71.4 | 81.8 | 75 | 75 | 77.0 | 64.3 | 62.5 | 77.8 | 62.5 | 81.8 | 66.7 | 75.0 | 70.1 |
2 | 81.3 | 86.4 | 85.8 | 90.5 | 90.4 | 91.3 | 89.5 | 87.9 | 77.4 | 82.1 | 83.7 | 83.5 | 91.2 | 84.1 | 88.0 | 84.3 | |
3 | 70.8 | 71.1 | 81.3 | 82.5 | 71.7 | 76.0 | 86.2 | 77.1 | 75.6 | 76.2 | 77.6 | 81.3 | 73.3 | 80.9 | 82.4 | 78.2 | |
4 | 71.4 | 70.8 | 73.1 | 72.0 | 78.2 | 69.6 | 72.3 | 72.5 | 76.4 | 78.0 | 80.3 | 84.4 | 75.4 | 80.0 | 78.3 | 79.0 | |
Kharif | 5 | 88.0 | 94.7 | 90.0 | 86.4 | 88.2 | 94.1 | 90.2 | 68.8 | 66.7 | 69.2 | 76.0 | 71.4 | 80.0 | 72.0 | ||
6 | 75.6 | 93.0 | 74.5 | 74.5 | 71.4 | 74.0 | 77.2 | 70.8 | 72.7 | 74.5 | 76.1 | 83.3 | 72.5 | 75.0 | |||
7 | 72.2 | 72.7 | 83.3 | 84.9 | 84.8 | 75.7 | 79.0 | 76.5 | 78.4 | 74.1 | 71.4 | 77.8 | 77.9 | 76.0 | |||
8 | 76.7 | 74.5 | 75.2 | 73.1 | 75.0 | 86.0 | 76.8 | 81.2 | 90.5 | 86.4 | 82.9 | 80.5 | 87.1 | 84.7 | |||
9 | 73.7 | 78.1 | 77.8 | 73.0 | 81.3 | 75.8 | 76.6 | 77.8 | 73.5 | 75.7 | 75.0 | 74.3 | 78.1 | 75.7 |
Table 3
Summary of seasonal accuracies and K (Kappa coefficient)"
Sr. No. | Year | Rabi Season | Kharif Season | ||||||
---|---|---|---|---|---|---|---|---|---|
Avg. Prod. Accur. | Avg. User Accur. | Overall accuracy | K | Avg. Prod. Accur. | Avg. User Accur. | Overall accuracy | K | ||
1 | 2005-06 | 74.7 | 73.4 | 87.4 | 0.77 | ||||
2 | 2006-07 | 77.9 | 74.7 | 79.5 | 0.66 | 77.2 | 75.0 | 76.2 | 0.69 |
3 | 2007-08 | 80.6 | 80.4 | 83.6 | 0.74 | 82.6 | 76.4 | 79.3 | 0.73 |
4 | 2008-09 | 78.0 | 77.9 | 81.9 | 0.71 | 80.2 | 76.0 | 78.1 | 0.71 |
5 | 2009-10 | 79.5 | 79.9 | 81.2 | 0.71 | 78.4 | 76.3 | 77.0 | 0.70 |
6 | 2010-11 | 80.8 | 80.9 | 83.8 | 0.75 | 80.2 | 77.5 | 78.6 | 0.72 |
7 | 2011-12 | 79.0 | 77.9 | 82.5 | 0.74 | 81.1 | 79.1 | 80.1 | 0.74 |
Average | 78.6 | 77.9 | 82.8 | 0.73 | 79.9 | 76.7 | 78.2 | 0.71 |
Table 4
Comparison of accuracy values and K from earlier studies with present study"
Sr. No. | Classification accuracy | K | Type of data | Reference |
---|---|---|---|---|
1 | 91.5 | 0.89 | RADARSAT | Shao et al., 2001 |
2 | 77.2 | 0.736 | MODIS | Giri et al., 2005 |
3 | 84.4-87.1 | 82.3-83.6 | Landsat MSS. ETM+ | Reis, 2008 |
4 | 77 | 0.73 | SPOT | Cheema et al., 2010 |
5 | 94 | 0.93 | SPOT | Thi et al., 2012 |
6 | 78-99 | - | Landsat | Ding et al., 2013 |
7 | 84-93 | 0.78-0.92 | Landsat | Lu et al., 2013 |
8 | 78.2-82.8 | 0.71-0.73 | MODIS | Present study |
Table 5
Summary of soil texture, elevation and slope for each LULC"
Class Name | Texture Class | Texture | Elevation (m) | Slope (%) |
---|---|---|---|---|
Wheat | Moderately fine/ Moderately-coarse | Sandy-clay-loam, clay-loam, silty-clay-loam/sandy-loam, fine sandy-loam | 187 | 1.98 |
Rice | Moderately-fine | Sandy-clay-loam, clay-loam, silty-clay-loam | 192 | 2.40 |
Cotton | Moderately-coarse | Sandy-loam, fine sandy-loam | 176 | 1.14 |
Sugarcane | Moderately fine/Moderately-coarse | Sandy-clay-loam, clay-loam, silty-clay-loam/sandy-loam, fine sandy-loam | 169 | 2.00 |
Rabi fodder | Moderately-coarse | Sandy-loam, fine sandy-loam | 175 | 1.41 |
Kharif fodder | Moderately-coarse | Sandy-loam, fine sandy-loam | 179 | 1.91 |
Table 9
Pixel-by-pixel LULC change detection between maximum and minimum cropped areas for kharif seasons"
Crop class | Change matrix for area (ha) | Spatial change detection | |||||||
---|---|---|---|---|---|---|---|---|---|
Rice | 2008 | | |||||||
2009 | Fallow* | Cotton | S. cane | K. fodder | Rice | Total | |||
Fallow* | 18388 | 0 | 0 | 16913 | 1118 | ||||
Cotton | 0 | 75533 | 8266 | 32982 | 184 | ||||
S. cane | 0 | 23966 | 61450 | 928 | 821 | ||||
K. fodder | 3937 | 17799 | 672 | 275465 | 25388 | ||||
Rice | 131 | 13726 | 35831 | 88378 | 224125 | 362218 | |||
Total | 251654 | ||||||||
Cotton | 2007 | | |||||||
2011 | Fallow | Cotton | S. cane | K. fodder | Rice | Total | |||
Fallow | 12566 | 0 | 0 | 4395 | 190 | ||||
Cotton | 12 | 8623 | 81034 | 143959 | 25881 | 259529 | |||
S. cane | 0 | 23407 | 102895 | 7993 | 3485 | ||||
K. fodder | 8480 | 59 | 1701 | 154152 | 5156 | ||||
Rice | 1392 | 44662 | 12435 | 65239 | 218285 | ||||
Total | 76758 | ||||||||
Kharif fodder | 2011 | | |||||||
2008 | Fallow | Cotton | S. cane | K. fodder | Rice | Total | |||
Fallow | 14088 | 0 | 0 | 7826 | 541 | ||||
Cotton | 0 | 83704 | 40660 | 1290 | 4775 | ||||
S. cane | 0 | 13006 | 81129 | 36 | 12049 | ||||
K. fodder | 2866 | 154378 | 10104 | 152249 | 95069 | 414699 | |||
Rice | 196 | 8421 | 5888 | 7553 | 229578 | ||||
Total | 169562 |
Table 10
Irrigation-subdivision wise seasonal average evapotranspiration (mm) and percent of total cultivated area for each LULC class in the study area"
LULC | Sagar | Chuharkana | Paccadala | Mohlan | Buchiana | Tandla | Tarkhani | Kanya | Bhagat | Sultanpur | |
---|---|---|---|---|---|---|---|---|---|---|---|
Cotton | Evapotranspiration | 563.4 | 537.9 | 550.9 | 557.4 | 579.3 | 545.1 | 525.0 | 520.6 | 518.4 | 539.8 |
% area | 1.1 | 2.9 | 5.8 | 11.8 | 15.4 | 21.9 | 13.7 | 14.1 | 9.6 | 3.6 | |
Sugarcane | Evapotranspiration | 602.1 | 578.4 | 576.1 | 587.5 | 595.0 | 595.1 | 571.9 | 560.8 | 583.5 | 595.3 |
% area | 0.03 | 0.10 | 0.61 | 7.35 | 7.44 | 19.17 | 19.26 | 16.40 | 8.16 | 21.48 | |
Kharif fodder | Evapotranspiration | 529.3 | 525.6 | 513.1 | 534.3 | 544.7 | 516.5 | 482.9 | 505.1 | 460.7 | 530.5 |
% area | 4.1 | 4.6 | 16.7 | 9.9 | 16.6 | 10.2 | 11.7 | 6.9 | 17.8 | 1.6 | |
Rice | Evapotranspiration | 589.9 | 581.4 | 576.9 | 604.4 | 619.4 | 604.6 | 555.6 | 553.1 | 565.6 | 600.1 |
% area | 30.5 | 24.4 | 9.6 | 16.2 | 0.6 | 2.0 | 2.0 | 1.9 | 7.5 | 5.4 |
Table 11
LULC change scenarios and water utilization (ha-m)"
Sr. No. | Scenario | Type | Sagar | Chuharkana | Paccadala | Mohlan | Buchiana | Tandla | Tarkhani | Kanya | Bhagat | Sultanpur | CWU* |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 25% decrease in Rice area by replacing it with K.fodder & S.cane | Type-I | 100% Rice to K.fodder | 50% each to K.fodder & S.cane (600.7) | 100% Rice to K.fodder (37.5) | 100% Rice to S.cane (15.9) | 50% Rice each to K.fodder & S.cane | 4452.4 | |||||
1579.6 | 1163.8 | 524.3 | 47.1 | 32.7 | 280.2 | 170.5 | |||||||
2 | 25% decrease in Rice area by its major conversion to K.fodder | 100% Rice to K.fodder | 100% to S.cane (15.9) | 100% Rice to K.fodder | 4402.7 | ||||||||
1579.6 | 1163.8 | 524.3 | 484.2 | 37.5 | 60.7 | 39.1 | 337.8 | 159.7 | |||||
3 | 25% decrease in Rice area by replacing it with S.cane | 100% Rice to K.fodder | 100% to S.cane (116.4) | 100% to K.fodder (37.5) | 100% to S.cane (15.9) | 100% Rice to S.cane | 3370.8 | ||||||
1579.6 | 1163.8 | 524.3 | -13.6 | -6.4 | -57.6 | 10.8 | |||||||
4 | 50% decrease in Cotton area by its conversion to S.cane & K.fodder | 100% Cotton to S.cane | 50% Cotton each to K.fodder and S.cane | 100% to K.fodder (722.8) | 100% to S.cane (-263.0) | -139.7 | |||||||
-56.4 | -151.7 | 47.9 | -53.8 | 189.0 | -305.1 | -43.5 | -225.9 | ||||||
5 | 50% decrease in Cotton area by its major conversion to S.cane | Type II | 100% Cotton to S.cane | 100% to K.fodder (722.8) | 100% to S.cane (-263.0) | -1729.7 | |||||||
-56.4 | -151.7 | -94.9 | -230.9 | -157.6 | -711.6 | -418.2 | -368.3 | ||||||
6 | 50% decrease in Cotton area by its major conversion to K.fodder | Type-I | 100% to S.cane | 100% Cotton to K.fodder | 100% to S.cane (-263.0) | 1841.8 | |||||||
-56.4 | -151.7 | 142.8 | 177.1 | 346.6 | 406.4 | 374.7 | 142.4 | 722.8 | |||||
7 | 50% increase in K.fodder by replacing Rice, Cotton and S.cane | 100% K.fodder from Rice | 50% each from Rice and Cotton (718.3) | 50% K.fodder each from S.cane and Cotton | 50% each from Cotton & Rice (1225.7) | 50% K.fodder each from Cotton and Rice 55.2 | 4662.4 | ||||||
208.3 | 217.5 | 320.6 | 595.1 | 462.1 | 650.5 | 209.2 | |||||||
8 | 50% increase in K.fodder by major replacement of S.cane & Cotton | 100% K.fodder from Rice | 100% K.fodder from S.cane | 100% from K. fodder Cotton | 2838.6 | ||||||||
208.3 | 217.5 | 450.9 | 223.6 | 352.7 | 338.9 | 441.7 | 163.6 | 435.0 | 6.5 | ||||
9 | 50% increase in K.fodder area by major replacement of Cotton & Rice | 100% K.fodder from Rice | 100% K.fodder from Cotton | 100% from Rice (790.7) | 100% from Rice (48.6) | 2249.6 | |||||||
208.3 | 217.5 | 267.4 | 97.0 | 242.4 | 123.2 | 208.7 | 45.6 |
1 | Agricultural Outlook Forum, 2012. The world and United States cotton outlook. United States Department of Agriculture. |
2 | Anderson J R, 1977. Land use and land cover changes: A framework for monitoring.Journal of Research by the Geological Survey, 5: 143-153. |
3 |
Barraza V, Grings F, Salvia Met al., 2013. Monitoring and modelling land surface dynamics in Bermejo River Basin, Argentina: Time series analysis of MODIS NDVI data.International Journal of Remote Sensing, 34(15): 5429-5451. doi: 10.1080/01431161.2013.791759.
doi: 10.1080/01431161.2013.791759 |
4 | Bastiannssen W G M, 1998a. Remote sensing in water resources management: The state of the art. International Water Management Institute, Colombo, Sri Lanka. |
5 |
Bastiaanssen W G M, Menenti M, Feddes R Aet al., 1998b. A remote sensing surface energy balance algorithm for land (SEBAL) formulation.J. Hydrol., 212/213: 198-212.
doi: 10.1016/S0022-1694(98)00253-4 |
6 |
Black A, Stephen H, 2014. GIScience & remote sensing relating temperature trends to the normalized difference vegetation index in Las Vegas.GIScience and Remote Sensing, 51(4): 468-482.
doi: 10.5311/JOSIS.2014.9.204 |
7 | Campbell J B, 2002. Introduction to Remote Sensing. New York: The Guilford Press. |
8 |
Cheema M J M, Bastiaanssen W G M, 2010. Land use and land cover classification in the irrigated Indus Basin using growth phenology information from satellite data to support water management analysis.Agricultural Water Management, 97(10): 1541-1552. doi: 10.1016/j.agwat.2010.05.009.
doi: 10.1016/j.agwat.2010.05.009 |
9 | Congalton R, Green K, 1999. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. Boca Raton: CRC/Lewis Press, FL. 137 p. |
10 | Congalton R G, 1996. Accuracy assessment: A critical component of land cover mapping in gap analysis: A landscape approach to biodiversity planning. A Peer-Reviewed Proceedings of the ASPRS/GAP Symposium, February 27 - March 2, 1995, Charlotte, N.C. 119-131. |
11 | Dappen Patti R, Ratcliffe I C, Robbins C R et al., 2008. Mapping agricultural land cover for hydrologic modeling in the Platte River Watershed of Nebraska. Great Plains Research: A Journal of Natural and Social Sciences, Paper 926, . |
12 |
Ding H, Shi W, 2013. Land-use/land-cover change and its influence on surface temperature: A case study in Beijing City.International Journal of Remote Sensing, 34(15): 5503-5517. doi: 10.1080/01431161.2013.792966.
doi: 10.1080/01431161.2013.792966 |
13 |
Douglas K B, Mark A F, 2013. Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics.Agricultural and Forest Meteorology, 173: 74-84.
doi: 10.1016/j.agrformet.2013.01.007 |
14 |
Fang W, Chen J, Shi P, 2005. Variability of the phenological stages of winter wheat in the North China Plain with NOAA/AVHRR NDVI data (1982-2000).IEEE International Geoscience and Remote Sensing Symposium Proceedings, 5: 3124-3127.
doi: 10.1109/IGARSS.2005.1526499 |
15 | Fisher P F, 2010. Remote sensing of land cover classes as type 2 fuzzy sets.Remote Sensing of Environment, 114: 309-321. |
16 |
Foody G M, 2002. Status of land cover classification accuracy assessment.Remote Sensing of Environment, 80: 185-201.
doi: 10.1016/S0034-4257(01)00295-4 |
17 | Gao X, Huete A R, Ni Wet al., 2000. Optical-biophysical relationships of vegetation spectra without back-ground contamination.Remote Sensing of Environment, 74: 609-620. |
18 | Giri, Chandra, Jenkins C, 2005. Land cover mapping of greater Mesoamerica using MODIS data. Remote Sensing, 31(4): 274-282. Retrieved at . |
19 | Gong P, Wang J, Yu Let al., 2013. Finer resolution observation and monitoring of global land cover: First mapping results with Landsat TM and ETM+ data.International Journal of Remote Sensing, 34(7): 2607-2654. doi: 10.1080/01431161.2012.748992. |
20 |
Gumma M K, Nelson A, Thenkabail P Set al., 2011. Mapping rice areas of South Asia using MODIS multitemporal data.J. Applied Remote Sensing, 5(1): 53547. doi: 10.1117/1.3619838.
doi: 10.1117/1.3619838 |
21 |
Jensen J R, 1996. Introductory Digital Image Processing: A Remote Sensing Perspective. 2nd ed. New Jersey: Prentice-Hall, 316p.
doi: 10.1080/10106048709354084 |
22 | Jeong S, Jang K, Hong Set al., 2011. Detection of irrigation timing and the mapping of paddy cover in Korea using MODIS images data.Korean Journal of Agricultural and Forest Meteorology, 13: 69-78. |
23 |
Julien Y, Sobrino J A, 2009. Global land surface phenology trends from GIMMS database.International Journal of Remote Sensing, 30: 3495-3513.
doi: 10.1080/01431160802562255 |
24 |
Kim Y.2013. Drought and elevation effects on MODIS vegetation indices in northern Arizona ecosystems.International Journal of Remote Sensing, 34(14): 4889-4899. doi: 10.1080/2150704X.2013.781700.
doi: 10.1080/2150704X.2013.781700 |
25 | Kimaro T A, Tachikawa Y, Takara K, 2005. Distributed hydrologic simulations to analyze the impacts of land use changes on flood characteristics in the Yasu River Basin in Japan.Journal of Natural Disaster Sciences, 27(2): 85-94. |
26 |
Latifovic R, Olthof I, 2004. Accuracy assessment using sub-pixel fractional error matrices of global land cover products derived from satellite data.Remote Sensing of Environment, 90: 153-165.
doi: 10.1016/j.rse.2003.11.016 |
27 |
Leff B, Ramankutty N, Foley J A, 2004. Geographic distribution of major crops across the world. Global Biogeochem. Cycles, 18, GB 1009. doi: 10.1029/203GB002108.
doi: 10.1029/2003GB002108 |
28 |
Liang L, Gong P, 2013. Evaluation of global land cover maps for cropland area estimation in the conterminous United States.International Journal of Digital Earth: 1-16. doi: 10.1080/17538947.2013.854414.
doi: 10.1080/17538947.2013.854414 |
29 |
Lorencov´ A E, Fr´ Elichov´ A J, Nelson Eet al., 2013. Past and future impacts of land use and climate change on agricultural ecosystem services in the Czech Republic.Land Use Policy, 33: 183-194.
doi: 10.1016/j.landusepol.2012.12.012 |
30 |
Lu D, Li G, Moran Eet al., 2013. Spatiotemporal analysis of land use and land cover change in the Brazilian Amazon.International Journal of Remote Sensing, 34(16): 5953-5978. doi:10.1080/01431161.2013.802825.
doi: 10.1080/01431161.2013.802825 pmid: 24127130 |
31 |
Matthews E, 1983. Global vegetation and landuse: New high resolution data bases for climate studies.Journal of Climate and Applied Meteorology, 22: 474-487.
doi: 10.1175/1520-0450(1983)0222.0.CO;2 |
32 |
Mitrakis N E, Mallinis G, Koutsias Net al., 2011. Burned area mapping in Mediterranean environment using medium-resolution multi-spectral data and a neuro-fuzzy classifier.International Journal of Image and Data Fusion, 1-20.
doi: 10.1080/19479832.2011.635604 |
33 | Molden D, 1997. Accounting for water use and productivity. SWIM paper 1. Colombo, Srilanka. |
34 |
Morton D C, DeFries R S, Shimabukuro Y Eet al., 2006. Cropland expansion changes deforestation dynamics in the southern Brazilian Amazon.Proceedings of the National Academy of Sciences of the United States of America, 103(39): 14637-14641.
doi: 10.1073/pnas.0606377103 pmid: 16973742 |
35 |
Niu Z, Zhang H, Wang Xet al., 2012. Mapping wetland changes in China between 1978 and 2008.Chinese Science Bulletin, 57(22): 2813-2823. doi: 10.1007/s11434-012-5093-3.
doi: 10.1007/s11434-012-5093-3 |
36 |
Osborne P, Alonso J, Bryant R, 2001. Modelling landscape-scale habitat use using GIS and remote sensing: A case study with great bustards.Journal of Applied Ecology, 38: 458-471.
doi: 10.1046/j.1365-2664.2001.00604.x |
37 | Oslon J S, 1994. Global ecosystem framework definitions. USGS EROS Data Center Internal Report, Sioux Falls, SD, 37p. |
38 |
Peng D, Huete A R, Huang Jet al., 2011. Detection and estimation of mixed paddy rice cropping patterns with MODIS data.International Journal of Applied Earth Observation and Geoinformation, 13: 13-23.
doi: 10.1016/j.jag.2010.06.001 |
39 | Pettorelli N, 2013. The Normalized Difference Vegetation Index. Oxford: Oxford University Press. |
40 | Portmann F T, Siebert S, Döll P, 2010. MIRCA2000-Global monthly irrigated and rainfed crop areas around the year 2000: A new high-resolution data set for agricultural and hydrological modeling. Global Biogeochemical Cycles, 24: GB1011. |
41 | Prakasam C, 2010. Land use and land cover change detection through remote sensing approach: A case study of Kodaikanal taluk, Tamil nadu.International Journal of Geomatics and Geosciences, 1(2): 150-158. |
42 |
Reed B C, Brown J F, VanderZee Det al., 1994. Measuring phenological variability from satellite imagery.Journal of Vegetation Science, 5: 703-714.
doi: 10.2307/3235884 |
43 |
Reis S, 2008. Analyzing land use/land cover changes using remote sensing and GIS in Rize, North-East Turkey.Sensors, 8(10): 6188-6202. doi: 10.3390/s8106188.
doi: 10.3390/s8106188 |
44 | Schilling K E, Jha M K, Zhang Y et al., 2008. Impact of land use and land cover change on the water balance of a large agricultural watershed: Historical effects and future directions. Water Resources Research, 44(7): 1-12. Available at: [Accessed October 8, 2014]. |
45 |
Shao Y, Fan X, Liu Het al., 2001. Rice monitoring and production estimation using multitemporal RADARSAT.Remote Sensing of Environment, 76(3): 310-325. doi: 10.1016/S0034-4257(00)00212-1.
doi: 10.1016/S0034-4257(00)00212-1 |
46 |
Shi J, Huang J, Zhang F, 2013. Multi-year monitoring of paddy rice planting area in Northeast China using MODIS time series data. Journal of Zhejiang University (Science B), 14(10) (October): 934-946. doi: 10.1631/jzus.B1200352.
doi: 10.1631/jzus.B1200352 pmid: 24101210 |
47 |
Thi T, Nguyen H, De-Bie C A J Met al., 2012. Mapping the irrigated rice cropping patterns of the Mekong delta, Vietnam, through hyper-temporal SPOT NDVI image analysis.International Journal of Remote Sensing, 33(2): 415-434.
doi: 10.1080/01431161.2010.532826 |
48 | Tou J T, Gonzalez R C, 1974. Pattern Recognition Principles. London: Addison-Wesley, 1974. |
49 |
Tucker C J, 1979. Red and photographic infrared linear combinations for monitoring vegetation.Remote Sensing of Environment, 8: 127-150.
doi: 10.1016/0034-4257(79)90013-0 |
50 |
Tucker C J, Vanpraet C L, Sharman M Jet al., 1985. Satellite remote sensing of total herbaceous biomass production in the Senegalese Sahel: 1980-1984.Remote Sensing of Environment, 17: 233-249.
doi: 10.1016/0034-4257(85)90097-5 |
51 | Usman M, Liedl R, Awan U K, 2015a. Spatio-temporal estimation of consumptive water use for assessment of irrigation system performance and management of water resources in irrigated Indus Basin, Pakistan.J. Hydrol. doi: 10.1016/j.jhydrol.2015.03.031. |
52 |
Usman M, Liedl R, Kavousi A, 2015b. Estimation of distributed seasonal net recharge by modern satellite data in irrigated agricultural regions of Pakistan.Environ. Earth Sciences. doi: 10.1007/s12665-015-4139-7.
doi: 10.1007/s12665-015-4139-7 |
53 |
Usman M, Liedl R, Shahid M A M, 2014. Managing irrigation water by yield and water productivity assessment of a rice-wheat system using remote sensing.Journal of Irrigation and Drainage Engineering. doi: 10.1061/(ASCE)IR.1943-4774.0000732.
doi: 10.1061/(ASCE)IR.1943-4774.0000732 |
54 |
Wajid A, Ahmad A, Khaliq Tet al., 2010. Quantification of growth, yield and radiation use efficiency of promising cotton cultivars at varying nitrogen levels.Pakistan Journal of Botany, 42(3): 1703-1711.
doi: 10.3417/2008072 |
55 | Wajid A, Hussain K, Maqsood Met al., 2007. Simulation modeling of growth, development and grain yield of wheat under semi arid conditions of Pakistan.Pakistan Journal of Agricultural Sciences, 44(2): 194-199. |
56 | Wardlow B D, Egbert S L, Kastens J H, . |
57 |
Wegehenkel M, 2009. Modeling of vegetation dynamics in hydrological models for the assessment of the effects of climate change on evapotranspiration and groundwater recharge.Adv. Geosci., 21: 109-115. doi: 10.5194/adgeo-21-109-2009.
doi: 10.5194/adgeo-21-109-2009 |
58 | Wilson M, Henderson-Sellers A, 1985. A global archive of land cover and soils data for use in general circulation models.Journal of Climatology, 5: 119-143. |
59 | Xiao X, Boles S, Frolking S et al., 2006. Mapping paddy rice agriculture in South and South-east Asia using multi-temporal MODIS images. Remote Sensing of Environment, 100: 95-113 .. |
60 |
Yu L, Wang J, Gong P, 2013. Improving 30 m global land-cover map FROM-GLC with time series MODIS and auxiliary data sets: A segmentation-based approach.International Journal of Remote Sensing, 34(16): 5851-5867. doi: 10.1080/01431161.2013.798055.
doi: 10.1080/01431161.2013.798055 |
61 |
Zhao L, Xia J, Xu Cet al., 2013. Evapotranspiration estimation methods in hydrological models.J. Geogr. Sciences, 23(2): 359-369. doi: 10.1007/s11442-013-1015-9.
doi: 10.1007/s11442-013-1015-9 |
62 |
Zheng P Q, Baetz B W, 1999. GIS-based analysis of development options from a hydrology perspective.Journal of Urban Planning and Development, 125: 164-180.
doi: 10.1061/(ASCE)0733-9488(1999)125:4(164) |
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