Abstract
Cereals and grain legumes are the most important part of human diet and nutrition. The expansion of grain legumes with improved productivity to cater the growing population’s nutritional security is of prime importance and need of the hour. Rice fallows are best niche areas with residual moisture to grow short-duration legumes, thereby achieving intensification. Identifying suitable areas for grain legumes and cereal grains is important in this region. In this context, the goal of this study was to map fallow lands followed by rainy season (kharif) rice cultivation or post-rainy (rabi) fallows in rice-growing environments between 2005 and 2015 using temporal moderate-resolution imaging spectroradiometer (MODIS) data applying spectral matching techniques. This study was conducted in South Asia where different rice ecosystems exist. MODIS 16 day normalized difference vegetation index (NDVI) at 250 m spatial resolution and season-wise-intensive ground survey data were used to map rice systems and the fallows thereafter (rabi fallows) in South Asia. The rice maps were validated with independent ground survey data and compared with available subnational-level statistics. Overall accuracy and kappa coefficient estimated for rice classes were 81.5% and 0.79%, respectively, with ground survey data. The derived physical rice area and irrigated areas were highly correlated with the subnational statistics with R 2 values of 94% at the district level for the years 2005–2006 and 2015–2016. Results clearly show that rice fallow areas increased from 2005 to 2015. The results show spatial distribution of rice fallows in South Asia, which are identified as target domains for sustainable intensification of short-duration grain legumes, fixing the soil nitrogen and increasing incomes of small-holder farmers.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alexandratos N, Bruinsma J (2012) World agriculture towards 2030/2050: the 2012 revision. http://large.stanford.edu/courses/2014/ph240/yuan2/docs/ap106e.pdf. Accessed in 08 June 2015. ESA working paper 3
Ali M, Ghosh P, Hazra K (2014) Resource conservation technologies in rice fallow. Resource conservation technology in pulses, First edited by PK Ghosh, N Kumar, MS Venkatesh, KK Hazra, N Nadarajan, 01/2014: chapter 7: pp 83–88, Scientific publishers, ISBN: 978-81-7233-885-5
Badhwar GD (1984) Automatic corn-soybean classification using landsat MSS data. I. Near-harvest crop proportion estimation. Remote Sens Environ 14(1–3):15–29
Biggs TW, Thenkabail PS, Gumma MK, Scott CA, Parthasaradhi GR, Turral HN (2006) Irrigated area mapping in heterogeneous landscapes with MODIS time series, ground truth and census data, Krishna Basin, India. Int J Remote Sens 27(19):4245–4266
Biradar CM, Thenkabail PS, Noojipady P, Li Y, Dheeravath V, Turral H, Velpuri M, Gumma MK, Reddy GPO, Cai XL, Xiao X, Schull MA, Alankara RD, Gunasinghe S, Mohideen S (2009) A global map of rainfed cropland areas (GMRCA) at the end of last millennium using remote sensing. Int J Appl Earth Obs Geoinf 11(2):114–129
Choice H (2009) Agroecological zones. Available http://harvestchoice.org/production/biophysical/agroecology. Downloaded 01/05/ 2010
Cihlar J, Xiao Q, Chen J, Beaubien J, Fung K, Latifovic R (1998) Classification by progressive generalization: A new automated methodology for remote sensing multichannel data. Int J Remote Sens 19(14):2685–2704
Congalton RG (2001) Accuracy assessment and validation of remotely sensed and other spatial information. Int J Wildland Fire 10(4):321–328
Dheeravath V, Thenkabail PS, Chandrakantha G, Noojipady P, Reddy GPO, Biradar CM, Gumma MK, Velpuri M (2010) Irrigated areas of India derived using MODIS 500 m time series for the years 2001–2003. ISPRS J Photogramm Remote Sens 65(1):42–59
FAO (2015) FAOSTAT. http://faostat.fao.org/. Accessed on 02 June 2015
Fischer A (1994) A model for the seasonal variations of vegetation indices in coarse resolution data and its inversion to extract crop parameters. Remote Sens Environ 48(2):220–230
Garnett T, Appleby M, Balmford A, Bateman I, Benton T, Bloomer P, Burlingame B, Dawkins M, Dolan L, Fraser D (2013) Sustainable intensification in agriculture: premises and policies. Science 341(6141):33–34
Gray J, Friedl M, Frolking S, Ramankutty N, Nelson A, Gumma M (2014) Mapping Asian cropping intensity with MODIS. Sel Top Appl Earth Obs Remote Sens IEEE J PP 99:1–7
Gumma MK, Gauchan D, Nelson A, Pandey S, Rala A (2011a) Temporal changes in rice-growing area and their impact on livelihood over a decade: a case study of Nepal. Agric Ecosyst Environ 142(3–4):382–392
Gumma MK, Nelson A, Thenkabail PS, Singh AN (2011b) Mapping rice areas of South Asia using MODIS multitemporal data. J Appl Remote Sens 5:053547
Gumma MK, Thenkabail PS, Hideto F, Nelson A, Dheeravath V, Busia D, Rala A (2011c) Mapping irrigated areas of Ghana using fusion of 30 m and 250 m resolution remote-sensing data. Remote Sens 3(4):816–835
Gumma MK, Thenkabail PS, Muralikrishna IV, Velpuri MN, Gangadhararao PT, Dheeravath V, Biradar CM, Acharya Nalan S, Gaur A (2011d) 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). Int JRemote Sens 32(12):3495–3520
Gumma MK, Thenkabail PS, Maunahan A, Islam S, Nelson A (2014) Mapping seasonal rice cropland extent and area in the high cropping intensity environment of Bangladesh using MODIS 500m data for the year 2010. ISPRS J Photogramm Remote Sens 91(5):98–113
Gumma MK, Kajisa K, Mohammed IA, Whitbread AM, Nelson A, Rala A, Palanisami K (2015a) Temporal change in land use by irrigation source in Tamil Nadu and management implications. Environ Monit Assess 187(1):1–17
Gumma MK, Mohanty S, Nelson A, Arnel R, Mohammed IA, Das SR (2015b) Remote sensing based change analysis of rice environments in Odisha, India. J Environ Manag 148:31–41
Gumma MK, Thenkabail PS, Teluguntla P, Rao MN, Mohammed IA, Whitbread AM (2016) Mapping rice-fallow cropland areas for short-season grain legumes intensification in South Asia using MODIS 250 m time-series data. Int J Digital Earth 9(10):981–1003
Homayouni S, Roux M (2003) Material mapping from hyperspectral images using spectral matching in urban area. In: Landgrebe P (ed) IEEE Workshop in honour of Prof. Landgrebe, Washington DC, USA, October 2003
INDIASTAT (2015) State-wise net area irrigated by source in India and state-wise irrigated area under crops in India. www.indiastat.com. Accessed in 30 May 2015
Jensen JR (2004) IIntroductory digital image processing: a remote sensing perspective, 3rd edn. Upper Saddle River, Prentice Hall, p 544
Knight JF, Lunetta RL, Ediriwickrema J, Khorram S (2006) Regional scale land-cover characterization using MODIS-NDVI 250 m multi-temporal imagery: a phenology based approach. GISci Remote Sens 43(1):1–23
Lobell DB, Asner GP, Ortiz-Monasterio JI, Benning TL (2003) Remote sensing of regional crop production in the Yaqui Valley, Mexico: estimates and uncertainties. Agric Ecosyst Environ 94(2):205–220
LPDAAC (2014) The land processes distributed active archive center – See more at: https://lpdaac.usgs.gov/#sthash.MxeroLVs.dpuf. Accessed on 15 April 2014
Sakamoto T, Yokozawa M, Toritani H, Shibayama M, Ishitsuka N, Ohno H (2005) A crop phenology detection method using time-series MODIS data. Remote Sens Environ 96(3–4):366–374
Satyanarayana A, Seenaiah P, Sudhakara Babu K, Prasada Rao M (1997) Extending pulses area and production in rice fallow. In: Asthana AN, Ali M (eds) Recent advances in pulses research. Indian Society of Pulses Research and Development, Indian Institute of Pulses Research, Kanpur, pp 569–580
Shao Y, Fan X, Liu H, Xiao J, Ross S, Brisco B, Brown R, Staples G (2001) Rice monitoring and production estimation using multitemporal RADARSAT. Remote Sens Environ 76(3):310–325
Subbarao G, KumarRao J, Kumar C, Johansen U, Irshad A, KrishnaRao L, Venkataratnam K, Hebbar K, Sai M, Harries D (2001) Spatial distribution and quantification of rice-fallows in south Asia: potential for legumes. ICRISAT, Patancheru, p 316 ICRISAT
Thenkabail PS (2010) Global croplands and their importance for water and food security in the twenty-first century: towards an ever green revolution that combines a second green revolution with a blue revolution. Remote Sens 2(9):2305–2312
Thenkabail PS, Schull M, Turral H (2005) Ganges and Indus river basin land use/land cover (LULC) and irrigated area mapping using continuous streams of MODIS data. Remote Sens Environ 95(3):317–341
Thenkabail P, GangadharaRao P, Biggs T, Gumma M, Turral H (2007) Spectral matching techniques to determine historical land-use/land-cover (LULC) and irrigated areas using time-series 0.1-degree AVHRR pathfinder datasets. Photogramm Eng Remote Sens 73(10):1029–1040
Thenkabail PS, Biradar CM, Noojipady P, Dheeravath V, Li YJ, Velpuri M, Reddy G, Cai X, Gumma M, Turral H (2008) A global irrigated area map (GIAM) using remote sensing at the end of the last millennium. International Water Management Institute, Colombo
Thenkabail P, Biradar C, Noojipady P, Dheeravath V, Li Y, Velpuri M, Gumma M, Reddy GPO, Turral H, Cai X, Vithanage J, Schull M, Dutta R (2009) Global irrigated area map (GIAM) derived from remote sensing, for the end of last millennium. Int J Remote Sens 30(14):3679–3733
Thiruvengadachari S, Sakthivadivel R (1997) Satellite remote sensing for assessment of irrigation system performance: a case study in India, Research report 9. International Irrigation Management Institute, Colombo
Tou JT, Gonzalez RC (1975) Pattern recognition principles. Addison-Wesley Publishing Company, Reading
USDA (2010) United States department of agriculture. foreign agricultural service. www.fas.usda.gov/psdonline/. Accessed on 21 May 2015. USDA
Velpuri NM, Thenkabail PS, Gumma MK, Biradar CB, Noojipady P, Dheeravath V, Yuanjie L (2009) Influence of resolution in irrigated area mapping and area estimations. Photogramm Eng Remote Sens 75(12):1383–1395
World Bank (2015) PovcalNet. http://iresearch.worldbank.org/PovcalNet/povcalNet.html. Accessed on 25 May 2015
Acknowledgments
This research was supported by two CGIAR Research Programs: Grain Legumes and Dryland Cereals (GLDC) and (Water Land and Ecosystems (WLE)). The research was also supported by the global food security support analysis data at 30 m project (GFSAD30; http://geography.wr.usgs.gov/science/croplands/; https://croplands.org/) funded by the NASA MEaSUREs (Making Earth System Data Records for Use in Research Environments) funding obtained through NASA ROSES solicitation as well as by the Land Change Science (LCS), Land Remote Sensing (LRS), and Climate Land Use Change Mission Area Programs of the US Geological Survey (USGS). The authors would like to thank to the International Rice Research Institute (IRRI) for providing ground survey data and district-wise national statistics; Dr. Dheeravath Venkateshwarlu, Dr. Andrew Nelson, and Dr. Mitch Scull for supporting ground surveys in India; Dr. Saidul Islam for Bangladesh ground survey data and Dr. Nimal Desanayake for Sri Lanka ground survey data. Also thanks to Ms. Deepika Uppala for supporting data analysis.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this chapter
Cite this chapter
Gumma, M.K., Thenkabail, P.S., Teluguntla, P., Whitbread, A.M. (2018). Monitoring of Spatiotemporal Dynamics of Rabi Rice Fallows in South Asia Using Remote Sensing. In: Reddy, G., Singh, S. (eds) Geospatial Technologies in Land Resources Mapping, Monitoring and Management. Geotechnologies and the Environment, vol 21. Springer, Cham. https://doi.org/10.1007/978-3-319-78711-4_21
Download citation
DOI: https://doi.org/10.1007/978-3-319-78711-4_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-78710-7
Online ISBN: 978-3-319-78711-4
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)