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remote sensing in agriculture


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remote sensing in agriculture

  1. 1. Professor Jayashankar Telangana State Agricultural University College of Agriculture, Rajendranagar, Hyderabad Remote Sensing in Agriculture Submitted to A.Madhavi lata Professor Department of Agronomy Submitted by M. Veerendra RAM/18-07 Department of Agronomy AGRON 501
  2. 2. Introduction • Remote sensing - sensing things from a distance. • Art and science of obtaining useful information about an object with out being in physical contact with it. without physically contact between the object and sensor”. • Remote sensing uses the electromagnetic spectrum to image the land, ocean and atmosphere. • All objects on the surface of the earth have their own characteristic spectral signature Process of Remote Sensing
  3. 3. Components of Remote sensing
  4. 4. A. Active Remote sensing • Energy leading to radiation received comes from sensors • Eg., Radar B. Passive Remote sensing • Energy leading to radiation comes from sun • Eg., Multi spectral scanners
  5. 5. Remote sensing platforms A. Ground based platforms : • Tripod stands B. Air based platforms : • Balloons • Air crafts • Drones C. Space based platforms : • Earth synchronous satellites • Sun synchronous satellites
  6. 6. Satellites used for remote sensing
  7. 7. Sensors • It refers to the device that record the electromagnetic radiation reflected from the object . • The detection of electromagnetic energy can be performed either photographically or electromagnetically.
  8. 8. Spectral resolution
  9. 9. Spatial resolution
  10. 10. SNO SATELLITE/SENSOR SPATIAL RESOLUTION TEMPORAL RESOLUTION 1 NOAA-AVHRR ( swath -2700km) 1100 Twice a day 2 IRS 1C/1D WiFS(810 km) 188 5 days 3 IRS P3 WiFS(810km) 188 5 days 4 RESOURCESAT 1 AWiFS(740km) 56 5 days Table 1.Satellites and Sensors being used for drought monitoring in NADAMS project
  11. 11. Electro Magnetic Spectrum Light energy is explained as EMR and can be classified according to the length of the wave. All possible energy channels called as Electro Magnetic Spectrum (EMS). Human eyes can only measure visible light but sensors can measure other portions of EMS.
  12. 12. How the Object is Identified by Sensor? The Basic principle of Remote Sensing is that each object reflect and emit energy of particular part of EMR in a unique way. Therefore, the signatures received from different objects is always different. This is called its Spectral signature. This is the key for interpretation in RS.
  13. 13. Spectral Reflectance Curve Is the plot between the Spectral reflectance (ratio of reflected energy to incident energy) and wave length. It depends upon the Chemical composition and Physical conditions. Typical Spectral reflectance curve for Vegetation, Water & Soil
  14. 14. Viewing images Three bands are viewable simultaneously
  15. 15. Radiation - Target interactions 1. Absorption (A) 2. Transmission (T) 3. Reflection (R) Immature leaves contain less chlorophyll than older leaves, they reflect more visible light and less infrared radiation.
  16. 16. There are different remote sensing indices available such as: 1.Normalized Difference Vegetation Index 2.Vegetation Condition Index 3.Temperature condition index 4.Vegetation health index
  17. 17. • The NDVI is a measure of the greenness or vigor of vegetation. The basic concept of NDVI is that the healthy vegetation reflects NIR radiation and absorbs RED radiation. This becomes reverse in case of unhealthy or stressed vegetation. NDVI is computed by the formula as: NDVI= (NIR-RED) /(NIR+RED) Absorbance and reflectance of radiation in a healthy and unhealthy plant NORMALIZED DIFFERENCE VEGETATION INDEX
  18. 18. It is an NDVI derived index. • It uses the NOAA-AVHRR NDVI data ,normalises the geographical differences and creates a possible comparision between different regions (it filters out the contribution of local geographic resources to the spatial variability of NDVI). • It can estimate the status of vegetation according to the best and worst vegetation vigour over a particular period in different years that gives more accurate results as compared to NDVI while monitoring the drought at regional scale. VEGETATION CONDITION INDEX
  19. 19. VCI= (NDVI j – NDVI min)/(NDVI max- NDVI min) Where, NDVIj = NDVI of date j ..it separates the long term ecological signal from the short term climate signal. • It is expressed in % ranging from 1 to 100. 50 to 100 = above normal vegetation 50 to 35 = drought condition below 35= severe drought condition.
  20. 20. • During the rainy season in general, it is common for overcast conditions to prevail for up to three weeks. When conditions last longer than this, the weekly NDVI values tend to be depressed, giving the false impression of water stress or drought conditions. • To remove the effects of contamination in satellite assessment of vegetation conditions, Kogan (1995, 1997) suggested the use of a Temperature Condition Index (TCI). • The TCI is calculated much in the same way as the VCI, but its formulation is modified to reflect the vegetation’s response to temperature (i.e. the higher the temperature the more extreme the drought). • Slight changes in vegetation health due to thermal stress could be monitored using the analysis of TCI data. TEMPERATURE CONDITION INDEX
  21. 21. TCI=100( BT max – BT)/(BT max-BT min) • where BT, BTmax and BTmin are the smoothed weekly brightness temperature, multi-year maximum and multi-year minimum, respectively, for each grid cell. VEGETATION HEALTH INDEX VHI= 0.5 (TCI) + 0.5(VCI) It is the combination of TCI and VCI The vegetation health index (VHI) has been developed using the VCI and TCI and is found to be more effective compared to other indices in monitoring vegetative drought (Kogan 1990, 2001; Singh et al. 2003).
  22. 22. Applications of Remote sensing in Agriculture Crop identification Crop acreage estimation Crop condition assessment and stress detection Identification of planting and harvesting dates Crop yield modeling and estimation Identification of pest and disease infestation Soil moisture estimation Irrigation monitoring and management Soil mapping Monitoring of droughts Land cover and land degradation mapping Identification of problematic soils
  23. 23. 1. Multi-temporal wheat disease detection by multi-spectral remote sensing For the implementation of site specific fungicide applications, the spatio temporal dynamics of crop disease must be well known. With in an experimental field, a 6ha plot of winter wheat was grown, containing leaf rust caused by Puccinia recondita. There was an accuracy of 56% during the 4th month and 65% accuracy during 5th month. The results showed that high resolution multi spectral data are generally suited to detect in field heterogeneities of crop vigour. Jonas et al., 2007
  24. 24. 2.Variable rate nitrogen fertilizer response in wheat using remote sensing Figure .2.aSpatial distribution of the randomly determined N treatments for the growing seasons 2008/09 Basso et al., 2016 Figure .2.bMap of mean grain yield for each N plot for the 2008/09 growing season
  25. 25. • N fertiliser application can lead to increased crop yields but its use efficiency remains generally low which can cause environmental problems related to nitrate leaching as well as nitrous oxide emissions. • This study evaluated three N fertiliser rates (30,70,90 kg N /ha) and their response on durum wheat yield across the field. • As per the remote sensing data treatments applied with 70 kg N shown good performance, which is in good correlation with the ground based yield data. Basso et al., 2016
  26. 26. Flood monitoring was conducted using optical imagery from the MODIS to delineate the extent of 2010 flood along the Indus river , Pakistan. A high correlation was found , as indicated by Pearson correlation coefficient of above 0.8 for the discharge guage stations located in the south west of Indus river basin. It can be concluded that RS data could be used to supplement stream guages in sparsely guaged large basins to monitor and detect floods. Sadiq et al., 2014 3.Flood monitoring Figure 3: Comparing the remote sensing imageries before the occurrence and after the occurrence of flood
  27. 27. • It was found that severe drought condition prevailed during kharif season of the year 2002 over a large area of Rajasthan. The onset and extent of drought can be clearly observed from the VCI maps. • Acute water stress is evident all over the state during the 1st fortnight of august 2002. From the second fortnight of august 2002 the condition was quite improved over the eastern part. Dipanwita et al., 2018 4. DROUGHT MONITORING Figure 4:Comparing VCI map of 2002 ( drought year) and 2003( normal year)
  28. 28. • The use of remote sensing data, followed by site observations is a powerful tool in detecting salt affected areas. • As nearly 80% of the saline areas could be delineated, it is a good indication for the validity of the model. Hence, this model can be used in similar areas that experience salinization problems. The simplicity of this model and acceptable degree of accuracy make it a promising tool for use in soil salinity prediction. • Combining these remotely sensed and ECe variables into one model yielded the best fit with R2 = 0.78. Engdawork et al., 2016 5. SOIL SALINITY MAPPING
  29. 29. France, Maize crop having weeds like Chenopodium album, Cirsium arvense Figure 6.Example of the spatial and spectral combination results using an SVM classifier. (a)Multispectral orthoimage; (b) Crop (green) and weed (red) location deduced from spatial information; (c) Weed (green) and crop (red) location deduced from spectral information; (d) Weed(green) and crop (red) location deduced from the combination of spatial and spectral information. Marine etal., 2018 6. Weed management
  30. 30. Norway, Carrot field having weeds like Chenopodium album, Poa annua, Stellaria. Figure 7.Visualization on Drop-on-Demand herbicide application Utstumo., 2019 7.Precision farming
  31. 31. • The label application for Glyfonova Plus ranges from 540 g/ha to 2880 g/ha depending on the types of weeds and weed pressure (Cheminova AS, 2015). • A treatment scheme with the robot and the DoD system, would consist of 2-3 treatments in combination with mechanical weed control in between the rows. Building on the experience from the lab and field trials, we would estimate a total application of 50 - 150 g/ha glyphosate. This represent a ten-fold reduction in applied herbicide.
  33. 33. Monitoring droughts using RS
  34. 34. Flood mapping
  35. 35. Detecting N deficiency with the help of RS techniques Detecting the spatial extent of soil erosion
  36. 36. Advantages of Remote sensing • Extent of coverage • Permanent and reliable record • Speed and consistency of interpretation of data • Reliable information • The data are available to multi-disciplinary use • The process of data acquisition and analysis is faster
  37. 37. Disadvantages of Remote sensing • Satellite data is too expensive. • Remotely sensed data is complicated to use. • Remotely sensed data is not readily available. • Satellite based remote sensing does not have sufficient resolution.
  38. 38. Problems of Remote sensing in Indian conditions • Small size of plots. • Diversity of crops sown in a particular area. • Variability of sowing and harvesting dates in different fields. • Inter cropping and mixed cropping practices. • Extensive cloud cover during the rainy season.
  39. 39. Conclusions • Remote sensing is a valuable tool in mapping and monitoring of biodiversity and provides valuable information. • It is useful for suggesting action plans and management strategies for agricultural sustainability of any region. • Remotely sensed images can be used to identify nutrient deficiencies, diseases, water deficiency, weed infestations, insect damage etc. • Remote sensing technology can be used to assess various abiotic and biotic stresses in different crop. • These technologies plays an important role in detecting and management of various crop issues even at a small land holding with high resolution. • Change detection is possible by these technologies in less time with better accuracy.
  40. 40. Kogan, F.N., 1990: Remote sensing of weather impacts on vegetation in non-homogeneous areas. International Journal of Remote Sensing, 11:1405–1419. Kogan, F.N., 1997: Global drought watch from space. Bulletin of the American Meteorological Society, 78:621–636. Aggarwal, S. Princple Of Remote Sensing. Satellite Remote Sensing and GIS Applications in Agricultural Meteorology. pp. 23-38 Mulder,V.L., Schaepman, S. 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. Santra , S.C. 2005: Remote Sensing. Environmental Science. 2:477-507 References