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Remote sensing in agriculture

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In India, agriculture is one of the major application areas of the remote sensing technology. Various national level agricultural applications have been developed which showcases the use of remote sensing data provided by the sensors/satellites launched by the country’s space agency, Indian Space Research Organisation (ISRO)

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Remote sensing in agriculture

  1. 1. APPLICATION OF REMOTE SENSING IN INDIAN AGRICULTURE Chitra .p Second year MSc environmental science
  2. 2. REMOTE SENSING The science of acquiring information about an object, without entering in contact with it, by sensing and recording reflected or emitted energy and processing, analysing, and applying that information.
  3. 3. • Different objects based on their structural, chemical and physical properties reflect or emit different amount of energy in different wave length ranges of the E.M.S • The sensors measure the amount of energy reflected from that object. . BASIC PRINCIPLE
  4. 4. ESSENTIAL COMPONENT OF REMOTE SENSING 1. Signals from a source/light 2. Sensors on a plate form 3. Sensing (Signal reception, storage, processing, information extraction and decision making)
  5. 5. THE REMOTE SENSING PROCESS Visual Digital Reference data Air photos Digital data Maps Statistics GIS data sets User Decision Maker Data products Inter- pretation Information products Target audience
  6. 6. TYPES OF REMOTE SENSING 1.Passive remote sensing 2.Active remote sensing
  7. 7.
  8. 8. ELECTROMAGNETIC SPECTRUM VisibleLight Nearinfrared Midinfrared Thermalinfrared Microwave TVandradio blue red UV near infrared 110-110-210-310-410-510-6 10410310210 107106105 wavelength (mm) wavelength (mm) 0.4 0.5 0.6 0.7 mm
  9. 9. • Identification, area estimation and monitoring • Crop nutrient deficiency detection • Soil mapping • Crop condition assessment • Agricultural draught assessment • Reflectance modelling • Crop yield modelling and production forecasting APPLICATION OF RS IN AGRICULTURE
  10. 10. IDENTIFICATION, AREA ESTIMATION AND MONITORING • The specific requirement of climate and soil conditions coupled with the specialized management practices make the distribution of plantation crops rather more localized in comparison to other agricultural crops. • The identification, estimation of growing stock, analysis of distribution and monitoring at regular intervals are major aspects in plantation crops
  11. 11. CROP NUTRIENT DEFICIENCY DETECTION: • The nutrient deficiency in plants affects the colour, moisture content and internal structures of the leaves and as a result their reflecting power changes
  12. 12. SOIL MAPPING • Advancements in space technology opened application possibilities of remote sensing in soil mapping • Soil properties have also been inferred from optical and microwave data using physically-based and empirical methods. Soil properties that have been measured using remote or proximal sensing approaches include mineralogy, texture, soil iron, soil moisture, soil organic carbon, soil salinity and carbonate content.
  13. 13. CROP CONDITION ASSESSMENT • The physiological changes that occur in a plant due to stress may change the spectral reflectance characteristics resulting in the detection of stress amenable to remote sensing techniques. • Crop monitoring at regular intervals during the crop growth cycle is essential to take appropriate measures and to asses information on probable loss of production
  14. 14. AGRICULTURAL DRAUGHT ASSESSMENT • Draught assessment is yet another area wherein remote sensing data has been used at operational level. • The district level drought assessment and monitoring using NDVI generated from NOAA- AVHRR data helps in taking timely preventive and corrective measures for combating drought.
  15. 15. REFLECTANCE MODELLING • Physical reflectance models for crops serve the important purpose of understanding the complex interaction between solar radiation and plant canopies • . In order to obtain a reliable yield prediction, growth of crops has to be modelled by means of crop growth models. • Crop growth models describe the relation between physiological process in plants and environmental factors such as solar radiation, temperature, water and nutrient availability
  16. 16. CROP YIELD MODELLING AND PRODUCTION FORECASTING • The information on production of crops before the harvest is very vital to the national food policy planning and economy of the country. • Reliable crop yield estimate is one of the most important components of crop production forecasting
  17. 17. CASE STUDY AGRICULTURAL DROUGHT ANALYSIS USING THE NDVI AND LAND SURFACE TEMPERATURE DATA; A CASE STUDY OF RAICHUR DISTRICT S SRUTHI., M.A.MOHAMMED ASLAM • Agricultural drought is nothing but the decline in the productivity of crops due to irregularities in the rainfall as well as decrease in the soil moisture, which in turn affects the economy of the nation. • Raichur District, of Karnataka is a drought prone region and falls within the most arid band of the country. • The purpose of the study is to analyse the vegetation stress in the Raichur district with the calculation of NDVI values and the land surface temperature (LST). • The Combination of (NDVI) normalized difference vegetation index and LST, provides very useful information for agricultural drought monitoring and early warning system for the farmers.
  18. 18. • By Remote sensing we can check the help what fraction of the photosynthetically active radiation is absorbed by vegetation. • Since green vegetation had strong absorption of spectrum in red region and high reflectance in infrared region, vegetation index was thus generally formulated as various combinations of red and infrared bands. • This data can be used to obtain characterize the health of the vegetation there, relative to the norm with the calculation of NDVI, value of the region NDVI = (λNIR - λRED) / (λNIR + λRED) Where, λNIR and λRED are the reflectance in the near infrared (NIR) and Red bands respectively. • The digital numbers (DN) of LST data is converted to degree Celsius by using following formula Temperature = (DN * 0.02) - 273.15 ºc •
  19. 19. • It can be clearly noticed that both the parameters are inversely proportional to each other. When the temperature is greater, the NDVI value is lesser which points out the decrease in the vegetation density. • The decrease in soil moisture due to lack or untimely onset of rainfall along with the increased temperature causes the agricultural drought to be severe. • By calculating the correlation between LST and NDVI, it can be clearly noticed that they show a high negative correlation.
  20. 20. • NDVI is commonly used parameter due to its simple calculation and largely used for the vegetation studies in a regional as well as global level. • It is always advisable to combine the NDVI along with other parameters to get better results. • The LST when correlated with the vegetation index it can be used to detect the agricultural drought of a region.
  21. 21. CONCLUSION • Remote sensing technology has developed from balloon photography to aerial photography to multi-spectral satellite imaging. • Some of the benefits that can be gained from the use of remote sensing - • Early identification of crop health and stress • Ability to use this information to do remediation work on the problem • Improve crop yield • Crop yield predictions • Reduce costs • Reduce environmental impact • Crop management to maximise returns through the season • Crop management to maximise returns during harvest time.
  22. 22. REFERENCES • Aggarwal, Shefali. Princple Of Remote Sensing. Satellite Remote Sensing and GIS Applications in Agricultural Meteorology. pp. 23-38 • Campbell, J. B. (2002). Introduction to remote sensing (3rd ed.). The Guilford Press. ISBN 1-57230-640-8. • Mulder,V.L. de Bruin,S. Schaepman, M.E. etal . The use of remote sensing in soil and terrain mapping — A review. Received 25 November 2009, Revised 24 November 2010, Accepted 26 December 2010, Available online 5 February 2011. • Menon, A.R.R. Remote sensing application in Agriculture and forestry. Centre for Environment and Development. • Rai,Anil. Remote sensing application and GIS application in agriculture. Indian Agriculture Statistics Research Institute. • S.C. Santra . Remote Sensing. Environmental Science. (Second Edition,2005),page no-477-507, • S Sruthi., M.A.Mohammed Aslam . Agricultural Drought Analysis Using the NDVI and Land Surface Temperature Data; a Case Study of Raichur District. International Conference On Water Resources, Coastal and Ocean Engineering(ICWRCOE 2015). Pg1258 – 1264 • Shibendu Shankar Ray. S,Neetu. Mamtha etal . Use of Remote Sensing in Crop Forecasting and Assessment of Impact of Natural Disasters: Operational Approaches in India. Mahalanobis National Crop Forecast Centre. Department of Agriculture & Cooperation, Ministry of Agriculture. • Shibendu Shankar Ray. Remote sensing application: Indian Experience. Mahalanobis National Crop Forecast Centre. Department of Agriculture & Cooperation, Ministry of Agriculture. • Singh, J.S. Singh,S.P. Gupta,S.P. Gupta,S.R. Remote sensing and GIS. Ecology Environment Science and Conservation.Pg 715- 743.Chand,S publication • http://phenology.cr.usgs.gov/ndvi_avhrr.php

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