Operational Crop Monitoring Using Synthetic Aperture Radar (SAR)

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Operational Crop Monitoring Using Synthetic Aperture Radar (SAR). C PATNAIK Space Applications Centre. SAR SYSTEMS. Frequent cloud cover during monsoon and sometimes in winter is a hindrance for using data from optical remote sensing - PowerPoint PPT Presentation

Text of Operational Crop Monitoring Using Synthetic Aperture Radar (SAR)

  • Operational Crop Monitoring Using Synthetic Aperture Radar (SAR) C PATNAIKSpace Applications Centre

  • *SAR SYSTEMS Frequent cloud cover during monsoon and sometimes in winter is a hindrance for using data from optical remote sensing SAR, due to its self illuminating beam, has all weather and day/night acquisition capability Current space borne SAR systems are available in 3 frequencies: C, L and X For agricultural crop monitoring C band SAR is found to be most suitable A variety of beam modes are available from these sensors based on the look angles Operational C band SAR systems currently available are Envisat ASAR and Radarsat Envisat ASAR has low swath coverage with an image size of not more than 8500 km2 Radarsat ScanSAR beam has coverage of 90000 km2 with Pixel spacing of 25 m For jute crop, due to small field sizes, Wide 2 beam is chosen. Image size is 22500 km2 and pixel spacing is 12.5 m Multi temporal SAR data is acquired to monitor the crop growth and use it for classification

  • *CROP DISCRIMINATION USING SAR The backscatter response is a function of the crop roughness, moisture and geometry

    Different crops have different backscattering properties based on their canopy structure and moisture content

    In the case of rice crop, the background standing water has a significant contribution to the backscatter till the PI stage

    In the case of crops where there is no background standing water, the backscatter is influenced by row orientation, density, canopy & soil moisture and roughness


    RICEJUTENumber of states covered13 (K) + 4 (R)03Remote Sensing SensorRadarsat SARMode of passDescendingBeam PositionScanSAR Narrow BWide 2Frequency (GHz) of C band5.3PolarisationHHSwath (km)300150Pixel Spacing (m)2512.5Incidence angle31-4631-38Number of repeat passes (acquisitions)03 (4)03Parameter measuredReturn Signal (backscatter)SeasonsKharif and RabiPre kharifForecasts Early Sept, Oct; DecMid July

  • *FASAL - RICEWet Season (Kharif)Total Coverage: 13 states accounting for>93 % rice production and>88 % acreage36 Radarsat ScanSAR Narrow B frames acquired on three dates. (108 scenes)(1999 )Winter Rice (Rabi)Total Coverage: 04 states accounting for>86 % rice production and>82 % acreage15 Radarsat ScanSAR Narrow B frames acquired on three dates. (45 scenes)(2007 )

  • *PARTICIPATING AGENCIESScientists from the following centres / agencies participate in the project:

    Space Applications Centre, AhmedabadNational Remote Sensing Centre (NRSC), HyderabadRemote Sensing Applications Centre (RS AC-UP), Lucknow, Uttar PradeshState Remote Sensing Applications Centre, Bhopal, Madhya PradeshInstitute of Environmental Studies and Wetland Management (IES&WM), Kolkata, West BengalOrissa Space Applications Centre (ORSAC), Bhubaneswar, OrissaBihar Remote Sensing Applications Centre (BIRSAC), Patna, BiharState Remote Sensing Applications Centre (JRSAC), JharkhandAssam Remote Sensing Applications Centre (ARSAC), Guwhati, AssamAP State Remote Sensing Applications Centre (APSRAC), Hyderabad, Andhra PradeshPunjab Remote Sensing Centre (PRSC), Ludhiana, PunjabDepartment of Agriculture, Chennai, Tamil NaduRemote Sensing Applications Centre, Raipur, ChhattisgarhKarnataka State Remote Sensing Applications Centre, Bangalore, KarnatakaHaryana Remote Sensing Applications Centre (HARSAC), Hissar, Haryana

  • *DATA ANALYSISGeneration of Decision Rules & ClassificationAccuracy CheckingSignature GenerationValidation and ForecastTransfer of sample segmentsTransfer of GT sites andAncillary Information to imageAggregation

  • *SECOND FORECASTOPTIMAL DATA SETPuddlingPeak Veg.TilleringTransplantingE. FloweringFIRST ACQ. SECOND ACQ. THIRD ACQ.Data is acquired based on the regions crop calendar. Normally three dates are acquired; however, in some critical cases a fourth date is acquired.FIRST FORECAST45 Days after transplanting. Accounts for more than 75% seasons rice30 days before harvesting

  • *MULTI TEMPORAL SAR DATAJuly 05, 2010 July 29, 2010 August 22, 2010Normal Transplanted Rice Late Transplanted Rice Very late transplanted rice

  • *RICE IN MULTI TEMPORAL SAR DATATwo date Composite Three date Composite(July 05, July 29, 2010) (July 05, 29 and Aug. 22, 2010)On two date composite, early transplanted rice would show as cyan and due to land preparation late transplanted rice would show red tonesOn three date composite, early rice is in blue tones and late transplanted rice is in magenta tones. Yellow tones are very late transplanted rice areasPlantationsUrbanWater body

  • *TransplantationPre-transplantationTilleringVegetativeMaturityHeading Peak-vegetative1234567CROP DISCRIMINATION WITH TEMPORAL SAR 23456

  • *CLASSIFICATION Mask out the non-agriculture area

    Within the agriculture area:Delineate the crop phenology based on ground informationTake multiple areas where soil conditions are similar this would help in correlating backscatter with canopy.Conversely, with crop condition being similar, study the effect of soil conditions on backscatterGenerate crop profiles based on the backscatter to help in the discriminationGenerate a knowledge baseCreate the decision rules (hierarchical)Classify the image and do accuracy check

  • *SAMPLE SEGMENT APPROACHDistrict/Zone wise classification based on ground truth is done to accommodate different management practices. For each run, image under the segments of a zone is classified.

  • *ACREAGE ESTIMATIONAfter classification and accuracy check: Crop proportion per segment is calculated Proportion is multiplied by N to get the crop area for the stratum Correct for the pseudo stratum area based on the factor derived from geographical area to N segments area. Aggregate the stratum wise figures to obtain state level area. Project to National rice area

  • *YIELD MODELINGYield for the season is modeled based on the following information.

    Daily weather dataStation latitudeRain fall, Tmax, Tmin and solar radiationGDDs to reach emergenceGDDs from emergence to flowering

    Historical yield database of the region for the past 15-20 years is considered.Production estimates are made and released along with acreage forecasts.

  • *PROGRESS OF RICE TRANSPLANTATION Part of W. Bengal Part of Orissa

    15th to 30th June; 1st to 8th July; 9th to 16th July; 17th to 23rd July; 24 th to 31st July;1st to 15th August.

  • *BIOMASS RETRIEVALRadarsatJul 23Aug 16Sep 09

    Sep 09Rice Biomass Map of the area3-Date FCC of the area

  • *DAMAGE ASSESSMENTFlood Affected Rice Area AssessmentSuper cyclone of Orissa, Oct 29, 1999Crop at soft dough stage.Crop lodging and submergence were the main causes of damage.Assessment made by Nov.06.

  • *DAMAGE ASSESSMENT2008 Normal Year2009 Drought year2009: Jul 08, Aug 01, Aug 25

    Yellow : Rice

    2008:Jul 13, Aug 06, Aug 30SAR Data FCCClassifiedDrought Affected Rice Area

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  • *FASAL - JUTEArea under jute: 7.71 lakh ha in 2008-09. Assam, Bihar, West Bengal and Orissa are the major jute growing states in the country, which account for about 98 % of jute area.The following table shows the state contribution to national jute area.Source:Agricultural Situation in India

    StatePer Cent ContributionMajor jute growing districtsWest Bengal78Nadia, Murshidabad, Uttar & Dakshin Dinajpur, Jalpaiguri, Cooch Behar, Hugli, N-24 Parganas, Bardhaman, MaldaBihar10Purnea, Katihar, Kishanganj, Supaul, Madhepura, Araria (Source: NIC)Assam09Dhubri, K.Anglong, Sonitpur, Dibrugarh, Kamrup, Darrang, Nagaon, Barpeta, Goalpara, MarigaonOrissa

  • *ROAD MAPAbout 85% of worlds jute cultivation is concentrated in Ganges delta.Sown by mid April and harvested by mid July. No row spacing.Crop identification was possible using 3 date SAR data. Other vegetation class found was mostly homesteads, forest and occasional patches of vegetables (
  • * Radarsat Wide beam 2 data of three dates is used. 11 frames x 3 dates = 33 scenes GT using GPS comprises of land cover, crop and soil parameters. Based on signatures of different features, decision rules are developed for jute crop discrimination. Average accuracy around 92 %. Overall accuracy around 91 %. Three major Jute growing states of Assam, Bihar and West Bengal are taken up. State and national level pre-harvest estimates are made. Final forecast of jute is given by mid July.


  • *JUTE SIGNATURESFCC and classified image of 3-date Radarsat Wide 2 SAR data (May 3, May 27 and June 20, 2008) showing Jute growing areas (Yellow)

  • *CONCLUSION Lack of cloud free data during the rice season necessitated the development of methodology to adopt SAR data for crop monitoring Operational crop acreage estimation currently being done for rice and jute crop using multi temporal SAR data Acquisition plan has been taken care of depending on each states crop calendar Stable signature banks have been developed for these crops Databases are regularly updated for the program Yield estimations done using in-season weather dataThree forecasts are given pre-harvest for rice and two preharvest forecasts for Jute Our estimates match well with the DES figures released post harvest.

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