Multi temporal remote sensing for yield prediction in sugarcane crops

Transcription

1 Multi temporal remote sensing for yield prediction in sugarcane crops Dr Moshiur Rahman and A/P Andrew Robson (Principal Researcher) Precision Agriculture Research Group, University of New England, NSW, Australia

2 DPI025:Developing remote sensing as an industry wide yield forecasting, nitrogen mapping and research aid ( ). 11 growing regions

3 Importance of accurate yield prediction: At a regional level: provide essential pre harvest information to support decisions regarding harvesting, transporting, marketing and forward selling. At the farm level: provide information to growers that support improved management strategies that optimise productivity (i.e. PA).

4 What is Remote Sensing? To measure something without physically touching it. Satellite: Landsat, SPOT, Worldview, GeoEYE etc. Airborne: Airplane, drone, balloon, birds. Ground based: Greenseeker, Cropcircle, Yara N, field spectrometer etc.

5 Commercial satellite remote sensing platforms: Landsat SPOT (5 and 6) RapidEYE IKONOS GeoEYE WorldView (2 and 3) QuickBird

6 Source of imagery: Regional Scale LANDSAT : 30 m spatial resolution 8 band spectral resolution Free of cost 16 days revisit time but 8 days offset between Landsat 7 and 8 34,200 km 2 tile encompasses the majority of crops in each region. SPOT5 : 10 m spatial resolution 4 band spectral resolution (NIR, red, green, blue) cost ~ $1/ km revisit day improved possibility of capture 3600 km 2 tile encompasses the majority of crops in each region.

7 Optimal timing of imagery capture January to early March generally restricted by cloud cover, for all regions. This limits the opportunity capture image earlier in the growing season. Therefore, recent studies by Robson et al. (2012) reported some incidence of high inaccuracy, when predicting yield from single capture SPOT 5 image.

8 Objective of the study Develop a generic model from time series Landsat imagery to predict sugarcane yield in Bundaberg region. Develop a seasonal growth profiles using time series GNDVI values derived from Landsat imagery. Identify the annual timing of peak growth, therefore indicating the optimal timing of future prediction from a single image capture.

9 Study area (Bundaberg) ~ 7,500 individual sugarcane crops

10 Remote Sensing Data 98 Landsat images from 2001 to 2015 with 20% cloud cover were used. Images from Mid November to July were acquired to match with the sugarcane growing period. Image processing was done using ArcGIS 10.2 and ENVI 5.1 All the images were masked using the boundary layer shapefile. The vegetation index was calculated from the spectral information of masked area.

11 Vegetation index

12 Regional crop GNDVI extracted from 98 images (2001 and 2015: mid Nov July) Average GNDVI /11/2000 1/11/2001 1/11/2002 1/11/2003 1/11/ Average GNDVI /11/2005 1/11/2006 1/11/2007 1/11/2008 1/11/ Average GNDVI /11/2010 1/11/2011 1/11/2012 1/11/2013 1/11/2014 Time of year

13 The average GNDVI data from 2001 to 2014 in the growing period of sugarcane (Mid November to July) Average GNDVI y = -1E-05( x - 145) R² = Date of Year

14 Model

15 The model derived GNDVI values for different years

16 Model derived maximum GNDVI Vs annual harvested yield (t/ha) from 2001 to Yield (t/ha) y = x R² = 0.69 RMSE = 4.2 t/ha Model derived maximum GNDVI Benefits: Landsat is freely available. Ability to predict maximum GNDVI in years where consistent cloud prevents an image capture at the crucial April period. Potential to predict yield earlier in the growing season

17 Development of Yield maps from algorithm Yield (t/ha) y = x R² = 0.69 RMSE = 4.2 t/ha False colour image of a Bundaberg region Crop harvesting Model derived maximum GNDVI Correlation between GNDVI and Yield (t/ha) Model derived GNDVI = 0.61 Predicted Yield = 77.9 t/ha GNDVI (NIR Green)/(NIR+Green) Surrogate yield map derived by correlation algorithm

18 Generation and Distribution of Yield Maps at the Regional Scale.

19 Average GNDVI (Feb to April) Vs annual harvested yield (t/ha) from 2001 to Yield (ton/ha) y = x R² = 0.48 RMSE = 5.46 t/ha Avg. GNDVI (Feb to April)

20 Maximum GNDVI Vs annual harvested yield (t/ha) from 2001 to Yield (ton/ha) y = x R² = 0.58 RMSE = 4.89 t/ha Maximum GNDVI

21 Model Validation The annual harvested yield of 2015 is used as a model validation data. The actual yield was 87.3 t/ha. The predicted yield was overestimated by only 3.5 t/ha.

22 Conclusions This study identified the historic temporal pattern of sugarcane crop production in Bundaberg region. Any deviation from this trend can indicate the onset of a widespread abiotic or biotic constraint. The maximum crop vigour or GNDVI value indicates the optimal time of satellite imagery acquisition. The model predicted yield with R 2 = 0.69 with RMSE = 4.2 t/ha. In case of consistent cloud cover during peak period, maximum GNDVI can be calculated from the model using just only one image.

23 Acknowledgements The authors would like to thank: Sugar Research Australia for their on going support of remote sensing research. All collaborating research and commercial groups. Collaborating mills, productivity services and growers.

24 Thanks to all Questions? Or Comments