Horticultural Crop Assessment using Satellite Data (Coordinated Horticulture Assessment and Management using geo-informatics: CHAMAN) 1 Mahalanobis National Crop Forecast Centre. DAC&FW, New Delhi 2 Space Applications Centre, ISRO, Ahmedabad, 3 Natinal Remote Sensing Centre and RRSCs,ISRO, Bengaluru 4 National Horticulture development Foundation, Nasik 5 North eastern Space Application Centre ,ISRO, Shillong 6 Department of Agriculture , cooperation and Farmers welfare S. Mamatha 1 , B. K. Bhattacharya 2 , Uday Raj 3 , H P Sharma 4 , B K Handique 5 , Mamta Saxena 6 & S S Ray 1 Email: [email protected]
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Horticultural Crop Assessment using Satellite Data
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Horticultural Crop Assessment using Satellite Data(Coordinated Horticulture Assessment and Management using geo-informatics: CHAMAN)
1Mahalanobis National Crop Forecast Centre. DAC&FW, New Delhi2Space Applications Centre, ISRO, Ahmedabad,3Natinal Remote Sensing Centre and RRSCs,ISRO, Bengaluru4National Horticulture development Foundation, Nasik5North eastern Space Application Centre ,ISRO, Shillong6Department of Agriculture , cooperation and Farmers welfare
S. Mamatha1, B. K. Bhattacharya2, Uday Raj3, H P Sharma4, B K Handique5, Mamta Saxena6 & S S Ray1
Only major crop growing districts of these states are considered
Total 185 Districts
Satellite Data Used for Crop Inventory
Satellite Sensor Resolution
Spectral Bands used
Sets of data required at a time
Crops
Resourcesat-2
AWIFS 56m NIR and Red Single date/ Multi-Date Potato.56m NDVI product Fortnightly Potato
LISSIII 23.5m NIR, Red and Green
Single date/ Multi-Date Potato, Onion, Banana
LISSIV 5.8m NIR, Red and Green
Single date/ Multi-Date Onion, Chili, Tomato, Mango, Citrus
IRS-P5 Cartosat 1 2.5m PAN Single date Mango, Citrus Landsat 8 OLI 30m SWIR, NIR, Red,
Green and BlueSingle date/ Multi-Date Potato, Onion
Sentinel-2A MSI 10m NIR, Red, Green and Blue
Single date/ Multi-Date Potato, Onion, Chili, Tomato
• Pre Processing of satellite data• Ground truth data collection• Satellite Image Classification • Post-classification analysis • Quality evaluation and Accuracy assessment• Area Estimation• Map Preparation • Bhuvan Interface
General Methodology
ClassificationTechniques
Characteristics Classifiers Crop
Pixel – basedtechniques
Each pixel isassumed pure andtypically labeled asa single land useland cover type
Horticultural Development using Geospatial Technology
S.N. Component Activity
1 Site Suitability Introduction/expansion of Horticulture development activities inNorth Eastern States (One district in each state)
2 Post-HarvestInfrastructure
Assessing the potential for new cold storage sites for in Bihar andUP State
3Crop Intensification Understanding the scope of improving crop intensity through
horticulture in selected districts of Haryana and Madhya Pradesh
4 GIS database creation Characterization of orchards and GIS database creation ofvarious layers and uploading on Bhuvan platform
5 Orchard Rejuvenation Identifying plantations /orchards that needs Rejuvenation in oneDistrict in UP and One district of Karnataka/Gujarat/WB usingremote Sensing data
6 Aqua-horticulture Developing plans for promoting aqua-horticulture in 1 –2 districtsin Bihar and Odisha state
Perspective view of Jhum land clusters
Land Suitability Analysis for Mango Plantation in Nuzvid mandal, AP
Site Suitability Analysis for Horticulture Expansion in NER -States
Study Area: Karsul Village, Niphad Taluk, NasikData Collected: Ground Spectral, UAV, High Resolution Satellite, Crop and Soil ParametersActivity: Phenology Mapping, Variability Assessment, Crop and Soil Parameter Retrieval
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Dates
Temporal NDVI Profiles of Grapes with Varying Vigour Grape-Nov Fruit Pruning Low Vigour Grape-Nov Fruit pruning High Vigour
Figure 5: Temporal spectral profile of grapes with varying vigour identified by stacking monthly NDVI Landsat imagery of 2013-14, 2014-15 and 2015-16 for Karsul village, Niphad block, Nasik district
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Temporal NDVI Profiles of Grapes with Varying Phenology Grape Young Orchard Grape-Sept Fruit Pruning Low Vigour
Grape-Oct Fruit pruning High Vigour Grape-Nov Fruit pruning High Vigour
Oct Fruit PruningSept Fruit
Pruning
Nov Fruit PruningOct
Fruit PruningSept
Fruit Pruning
Nov Fruit Pruning
Figure 7: Temporal spectral profile of grapes with varying phenology identified by stacking monthly NDVI Landsat imagery of 2013-14, 2014-15 and 2015-16 for Karsul village, Niphad block, Nasik district
Summary
• For crops like Potato, Mango, Citrus and Banana use of remote sensing data forassessment have been feasible, but for other crops accuracy is still an issue, due toscattered and small fields, mixed cropping, multiple seasons and short duration.
• Yield estimation for horticultural crops, especially fruit crops, is a problem due tomultiple picking.
• However use of satellite data and geospatial tools has shown a great promise forhorticultural development, especially for infrastructure development andhorticultural expansion.
Acknowledgment
• Indian Space Research Organization (SAC, NRSC, NESAC)• Department of Agriculture, Cooperation & Farmers’ Welfare (Hort Div)• National Horticultural Research & Development Foundation• India Meteorological Department• State Horticulture Departments• ICAR:NRCG
Team Members1. M M Kimothi2. Seema Sehgal3. Shreya Roy4. Aditi Srivastava5. Niti Singh6. Gargi Upadhyay7. Moreshwar Karale