Mapping Direct Seeded Rice in Raichur District of Karnataka, India

Across South Asia, the cost of rice cultivation has increased due to labor shortage. Direct seeding of rice is widely promoted in order to reduce labor demand during crop establishment stage, and to benefit poor farmers. To facilitate planning and to track farming practice changes, this study presents techniques to spatially distinguish between direct seeded and transplanted rice fields using multiple-sensor remote sensing imagery. The District of Raichur, a major region in northeast Karnataka, Central India, where irrigated rice is grown and direct seeded rice has been widely promoted since 2000, was selected as a case study. The extent of cropland was mapped using Landsat-8, Moderate Resolution Imaging Spectroradiometer (modis) 16-day normalized difference vegetation index (ndvi) time-series data and the cultivation practice delineated using risat-1 data for the year 2014. Areas grown to rice were mapped based on the length of the growing period detected using spectral characteristics and intensive field observations. The high resolution imagery of Landsat-8 was useful to classify the rice growing areas. The accuracy of land-use/landcover (lulc) classes varied from 84 percent to 98 percent. The results clearly demonstrated the usefulness of multiple-sensor imagery from mod09q1, Landsat-8, and risat-1 in mapping the rice area and practices accurately, routinely, and consistently. The low cost of imagery backed by ground survey, as demonstrated in this paper, can also be used across rice growing countries to identify different rice systems.

[1]  Prasad S. Thenkabail,et al.  Irrigated areas of India derived using MODIS 500 m time series for the years 2001–2003 , 2010 .

[2]  M. Imhoff,et al.  The derivation of a sub-canopy digital terrain model of a flooded forest using synthetic aperture radar , 1990 .

[3]  Prasad S. Thenkabail,et al.  Ganges and Indus river basin land use/land cover (LULC) and irrigated area mapping using continuous streams of MODIS data , 2005 .

[4]  G. Khush Breaking the yield frontier of rice , 1995 .

[5]  I. V. Muralikrishna,et al.  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) , 2011 .

[6]  P. Thenkabail,et al.  Irrigated area mapping in heterogeneous landscapes with MODIS time series, ground truth and census data, Krishna Basin, India , 2006 .

[7]  Vinay Kumar Dadhwal,et al.  Rice Crop Discrimination Using Single Date RISAT1 Hybrid (RH, RV) Polarimetric Data , 2015 .

[8]  John R. Jensen,et al.  Introductory Digital Image Processing: A Remote Sensing Perspective , 1986 .

[9]  Irshad A. Mohammed,et al.  Remote sensing based change analysis of rice environments in Odisha, India. , 2015, Journal of environmental management.

[10]  R. Naylor Labour-Saving Technologies in the Javanese Rice Economy: Recent Developments and a Look into the 1990s , 1992 .

[11]  Jai Singh Parihar,et al.  Methodology to classify rice cultural types based on water regimes using multi-temporal RADARSAT-1 data , 2012 .

[12]  Sushma Panigrahy,et al.  Initial results using RISAT-1 C-band SAR data , 2013 .

[13]  E. Nezry,et al.  Adaptive speckle filters and scene heterogeneity , 1990 .

[14]  Henri Laur,et al.  Derivation of the backscattering coefficient s0 in ESA ERS SAR PRI products , 1996 .

[15]  P. Teng,et al.  Multiple effects of two drivers of agricultural change, labour shortage and water scarcity, on rice pest profiles in tropical Asia , 2005 .

[16]  P. Thenkabail,et al.  Spectral Matching Techniques to Determine Historical Land-use/Land-cover (LULC) and Irrigated Areas Using Time-series 0.1-degree AVHRR Pathfinder Datasets , 2007 .

[17]  J. Townshend,et al.  Global land cover classifications at 8 km spatial resolution: The use of training data derived from Landsat imagery in decision tree classifiers , 1998 .

[18]  David B. Lobell,et al.  Remote sensing of regional crop production in the Yaqui Valley, Mexico: estimates and uncertainties , 2003 .

[19]  M. Ashton,et al.  Hyperion, IKONOS, ALI, and ETM+ sensors in the study of African rainforests , 2004 .

[20]  Ramaswamy Sakthivadivel,et al.  Satellite remote sensing for assessment of irrigation system performance: a case study in India , 1997 .

[21]  S. Singh,et al.  Weed management in direct-seeded rice , 2016 .

[22]  J. L. Barker,et al.  Landsat MSS and TM post-calibration dynamic ranges , 1986 .

[23]  Eric S. Kasischke,et al.  Monitoring South Florida Wetlands Using ERS-1 SAR Imagery , 1997 .

[24]  Obi Reddy P. Gangalakunta,et al.  Global irrigated area map (GIAM), derived from remote sensing, for the end of the last millennium , 2009 .

[25]  C. Robinson,et al.  Use of Radar Data to Delineate Palaeodrainage Flow Directions in the Selima Sand Sheet, Eastern Sahara , 2000 .

[26]  Russell G. Congalton,et al.  Assessing the accuracy of remotely sensed data : principles and practices , 1998 .

[27]  Philip A. Townsend,et al.  Mapping Seasonal Flooding in Forested Wetlands Using Multi-Temporal Radarsat SAR , 2001 .

[28]  Prasad S. Thenkabail,et al.  Mapping seasonal rice cropland extent and area in the high cropping intensity environment of Bangladesh using MODIS 500 m data for the year 2010 , 2014 .

[29]  D. Leckie Synergism of synthetic aperture radar and visible/infrared data for forest type discrimination. , 1990 .

[30]  Prasad S. Thenkabail,et al.  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 , 2010, Remote. Sens..

[31]  Hugh Turral,et al.  Water Scarcity Effects on Equitable Water Distribution and Land Use in a Major Irrigation Project—Case Study in India , 2008 .

[32]  Scott J. Goetz,et al.  APPLICATION OF MULTITEMPORAL LANDSAT DATA TO MAP AND MONITOR LAND COVER AND LAND USE CHANGE IN THE CHESAPEAKE BAY WATERSHED , 2004 .

[33]  Luca Gatti,et al.  Towards an Operational SAR-Based Rice Monitoring System in Asia: Examples from 13 Demonstration Sites across Asia in the RIICE Project , 2014, Remote. Sens..

[34]  Jagdish K. Ladha,et al.  Weed Management in Direct‐Seeded Rice , 2007 .

[35]  Prasad S. Thenkabail,et al.  Mapping rice areas of South Asia using MODIS multitemporal data , 2011 .

[36]  Prasad S. Thenkabail,et al.  Influence of Resolution in Irrigated Area Mapping and Area Estimation , 2009 .

[37]  T. L. Toan,et al.  Use of ENVISAT/ASAR wide-swath data for timely rice fields mapping in the Mekong River Delta , 2011 .

[38]  J. Chen,et al.  Classification by progressive generalization: A new automated methodology for remote sensing multichannel data , 1998 .

[39]  S. K. De Datta,et al.  Technology Development and the Spread of Direct-Seeded Flooded Rice in Southeast Asia , 1986, Experimental Agriculture.

[40]  Thuy Le Toan,et al.  Rice crop mapping and monitoring using ERS-1 data based on experiment and modeling results , 1997, IEEE Trans. Geosci. Remote. Sens..

[41]  T. Sakamoto,et al.  A crop phenology detection method using time-series MODIS data , 2005 .

[42]  Siamak Khorram,et al.  Regional Scale Land Cover Characterization Using MODIS-NDVI 250 m Multi-Temporal Imagery: A Phenology-Based Approach , 2006 .

[43]  Andrew Nelson,et al.  Temporal changes in rice-growing area and their impact on livelihood over a decade: A case study of Nepal , 2011 .

[44]  B. Brisco,et al.  Rice monitoring and production estimation using multitemporal RADARSAT , 2001 .

[45]  G. Badhwar,et al.  Automatic corn-soybean classification using Landsat MSS data. I - Near-harvest crop proportion estimation. II - Early season crop proportion estimation , 1984 .