Monitoring croplands using Remote sensing
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1 Monitoring croplands using Remote sensing Murali Krishna Gumma and team ICRISAT Dryland agriculture, where ICRISAT operating Geospatial World Forum: Knowledge based agriculture: the linkages with sustainability, food security and climate change impacts 18 th Jan, 2018, HICC, Hyderabad
2 Overview Geospatial products for multi-disciplinary teams Monitoring croplands using multi-temporal imagery Improving crop water productivity
3 Simulated yield estimations and impact Geospatial products for SAT Crop type / intensity maps Land use changes Length of growing periods Abiotic stresses Tracking adoption of NRM Technologies Research groups Breeders System modelers Social scientists Hydrologists Planning departments Spatial modeling (Prioritization) Water productivity Impact assessment
4 Methods & Approach: Flowchart FAO statistics MODIS NDVI, EVI & LSWI/ Landsat Crop Extent/Mask Crop Signatures VHRI DT/SMT - Interpretation based on knowledge ACCA - Automated Decision Tree Annual Dynamic Map ( ) Reference Bank Baseline Map(2014 for now)
5 ICRISAT mandate crops Level5_mod250_2014_lulc_15cls.img 01.Rainfed-DC Maize/mixed crops 02.Rainfed-SC Maize/sorghum 03.Rainfed-SC tef, sorghum, Maize 04.Rainfee-SC-tef/wheat,barly 05.Rainfed mixed Crops 06.Irrigated-SC-sugarcane-VLS 07.Irrigated_mixedcrops 08.Rainfed_Rice 09.Rangeland/fallow 10.Range lands/shrublands 11.Shrublands/Wasteland tress 12.Barenlands/Sanddunes 13.Forest 14.Waterbodies 15.Builtup Major crops (2014) 01. Rainfed-sc-sorghum 02. Rainfed-sc-millets/sorghum 03. Rainfed-sc-groundnut 04. Rainfed-sc-pigeonpea 05. Rainfed-SC-maize/sorghum/millet 06. Other crops Land use / land cover Area (ha) 01.Rainfed-DC Maize/mixed crops Rainfed-SC Maize/sorghum Rainfed-SC tef, sorghum, Maize Rainfee-SC-tef/wheat,barly Rainfed mixed Crops (vegetables etc) Irrigated-SC-sugarcane-VLS Irrigated_mixedcrops Rainfed_Rice Rangeland/fallow Range lands/shrublands Shrublands/Wasteland tress Barenlands/Sanddunes Forest Waterbodies Builtup ,33,77,041
6 Spatial distribution of croplands Croplands ( ) 01. Irrigated-DC-rice-wheat 02. Irrigated-DC-rice-rice 03. Irrigated-DC-rice-pulses 04. Irrigated-TC-pulses-rice-rice 05. Irrigated-DC-soyabean-wheat 06. Irrigated-DC-pulses-wheat 07. Irrigated-DC-Pulses/maize-wheat 08. Irrigated-millet-wheat 09. Irrigated-DC-maize/potato-wheat/pulses 10. Irrigated-DC-soyabean/maize-wheat/chickpea 11. Irrigated-GW-DC-sesamum/millet-wheat/mustartd 12. Irrigated-DC-pulses/maize-maize 13. Rainfed-SC-sorghum 14. Rainfed-SC-rice-fallow 15. Rainfed-SC_pigeonpea 16. Rainfed-SC-cotton/groundnut 17. Rainfed-supplemental-SC-cotton 18. Rainfed-SC-millet 19. Rainfed-DC-sorghum-chickpea/fallow 20. Rainfed-SC-pulses 21. Rainfed-SC-fallow-chickpea 22. Irrigated-sugarcane 23. Groundnut 24. Irrigated-DC-Maize-Othercrop 25. Rainfed-SC-maize 26. Mixed crops 27. Other LULC
7 Spatial distribution of rice & wheat ( ) Rice Wheat
8 Spatial distribution of major dryland crops( ) Groundnut (0.8 Mha) Millet (5.35 Mha) Kharif + Rabi sorghum ( 4.32 Mh) Groundnut (1.67 Mha) Sorghum Millets Groundnut Pigeonpea Chickpea
9 Rice-fallows (rainfed agriculture) State Rainfed: rice-fallows (Mha) % of total rice-fallow Chhattisgarh % Madhya Pradesh % Orissa % Jharkhand % Gumma et al., (2016)
10 Rice-fallows (temporal changes)
11 Tracking adoption of Chickpea: Andhra Pradesh 1,62,362 ha 3,89361 ha 5,58,713 ha Districts Anantapur 34,777 51,304 84,493 YSR (Kadapa) 30,343 69, ,903 Kurnool 68, , ,793 Prakasam 35, , ,524 Gumma et al., Satellite imagery and household survey for tracking chickpea adoption in Andhra Pradesh, India. Int l Journal of Remote Sensing. 37(8):
12 Tracking NRM technologies: Anantapur GW-Irri. 537 ha GW-Irri ha GW-Irri ha Ahmed, MI, Murali Krishna Gumma, Shalander Kumar, Peter Craufurd, Ismail M Rafi, and Amare Haileslassie "Land use and agricultural change dynamics in SAT watersheds of southern India." Current Science 110 (09):
13 Temporal changes: MODIS 250m
14 Crop production and water use: Sakare, Dhule District Baseline scenario Improved scenario 40% water savings and 71% rice equivalent yield gain
15 Crop production and water use (A. Baseline scenario; B. Improved scenario) Crop type Percent of total cropland area in season 1 (%) Percent of total cropland area in season 2 (%) Water used for producing 1 kg of grain (liters) Yield per hectares in (kg/ha) Total water used by all crops in 2 season (billion liters) Total production ('000 tones) Rice equivalent yields ('000 tones) A. Baseline scenario Rice Sorghum Maize pulses Cropland fallow 0 73 Total Area of croplands (ha) Total area (croplands + non-croplands) (ha) Total B. Improved scenario Rice Sorghum Maize pulses Cropland fallow 0 62 Total Area of croplands (ha) Total Reduced water use in new scenario Water Savings 26 billion liters Increased rice equivalent yields Increased Production 47 ('000 tones) Note Yield Water use Prices
16 Conclusions Availability of multi-resolution/temporal data Contributed several publications Developed web portals for public use Further work Automated crop classification algorithms (GEE) Conducting ground surveys in major cropland areas in India using Mobile apps., Conducting research on spectral characteristics of ICRISAT mandate crops using high resolution multispectral data and hyperspectral data Supporting multi-disciplinary teams
17 Mobile application for ground data collection icrops Mobile application Android mobile application Captured geo-tag photo s Freely available software Data can be used for training and validation
18 Thank You For more information:
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