SatelliteManagement

Using Landsat TM Imagery to Predict Wheat Yield and to Define Management Zones: 

Using Landsat TM Imagery to Predict Wheat Yield and to Define Management Zones

Landsat TM Imagery: 

Landsat TM Imagery How can we use Landsat TM imagery to predict wheat yield? How accurately can imagery collected in near flowering (April 1 to May 15) predict wheat yields? Can satellite wheat yield estimates be used to prescribe management zones?

Slide3: 

Winter Wheat, Pond Creek in North Central Oklahoma April 23, 1998 Variability???

Slide4: 

April 23,1998 TM Scene over North Central Oklahoma

Slide5: 

Image Processing and NDVI Computation Clear-sky Thematic Mapper (TM) scenes of north-central Oklahoma, spanning the period 1991 to 1999, were obtained from Space Imaging with radiometric and geometric corrections. The TM scenes were chosen so that the satellite overpasses occurred at or near the heading stage of winter wheat in the area (mid April to early May).

Dates and TM scenes used in the study:: 

Dates and TM scenes used in the study: April 4, 1991 May 9, 1992 April 25, 1993 March 27, 1994 April 2, 1996 April 20, 1997 April 23, 1998 May 12, 1999

Slide7: 

OSU Wheat Pasture Research Unit Overlaid on top of April 23,1998 False Color TM Image (Green, Red, and NIR bands). Grain wheat Grazed out wheat N

Slide8: 

OSU Wheat Pasture Research Unit with NDVI from April 23,1998 TM Image7

Slide9: 

Calibration curve of wheat grain yield as a function of Landsat TM NDVI. Oklahoma State University Wheat Pasture Research Unit, Marshall, OK. 0 15 30 45 60 75 90 0.3 0.4 0.5 0.6 0.7 0.8 0.9 NDVI Wheat Yield (bu/ac) TM 93 (April 25) TM 97 (April 20) TM 98 (April 23) TM 99 (May 12) Predicted Yield 95% Pred. Lim.

Slide10: 

Cherokee Pond Creek OSU WPRU Marshall Relative Locations of Test Farms and OSU Wheat Pasture Research Unit

Slide11: 

Field-average wheat grain yield, as predicted from NDVI and measured by farmer cooperators. 0 15 30 45 60 75 0 15 30 45 60 75 Measured Yield (bu/ac) Predicted Yield (bu/ac) Cherokee, OK Pond Creek, OK Pond Creek-2, OK Marshall, OK 93 9 96 91 91 96 94 93 97 98 98 92 93 94 98 99 91 96 92 96 97 98

Slide12: 

Field-average wheat yield as a function of field-average NDVI, compared with the OSU WPRU prediction equation. 0 15 30 45 60 75 90 0.4 0.5 0.6 0.7 0.8 0.9 Field-Average NDVI Field-Average Yield (bu/ac) Cherokee, OK Pond Creek, OK Pond Creek-2, OK Marshall, OK Predicted Yield 95% Pred. Lim.

Combine Yield Monitor and Satellite Estimated Wheat Yield Maps: 

Combine Yield Monitor and Satellite Estimated Wheat Yield Maps

Normalizing Satellite Estimated Yield: 

Normalizing Satellite Estimated Yield Normalization tends to remove the effect of weather, disease and other factors on the average yield. This minimizes the effect of abnormally high or yields when yield variability is compared between years or averaged over years. Yields can be normalized by dividing by the average yield for the field.

Wheat - Landsat TM Image Taken During April to Mid-May –Red Rock, OK: 

Wheat - Landsat TM Image Taken During April to Mid-May –Red Rock, OK

Aerial Image vs. Average Yields: 

Linn Aerial Image Aerial Image vs. Average Yields Terrace Effects

Red Rock, OK - 7 Year Average Estimated Yield and Coefficient of Variation: 

Red Rock, OK - 7 Year Average Estimated Yield and Coefficient of Variation Floods

Wheat - Landsat TM Image Taken During April to Mid-May, Pond Creek, Ok: 

Wheat - Landsat TM Image Taken During April to Mid-May, Pond Creek, Ok 1996 1998

Pond Creek, Oklahoma: 

Pond Creek, Oklahoma Dale Silt Loam McLain Silt Loam Owner Identified Soil

Slide20: 

Pond Creek, OK - Normalized Estimated Yield and Temporal Coefficient of Variation for Seven Years of Data Water and Hay for Calves Field Drainage

Cherokee, Oklahoma: 

Cherokee, Oklahoma Reinach Very Fine Sandy Loam McLain Silt Loam Dale Silt Loam - SALINE

Cherokee, Oklahoma: 

Cherokee, Oklahoma Hayed for Demonstration Plots Drainage problem from moldboard dead furrow Salt Slick

Cherokee, OK – Two Varieties: 

Cherokee, OK – Two Varieties

Hitchcock, OK: 

Hitchcock, OK

Hitchcock, Ok – Yield 1992 & 1993: 

Hitchcock, Ok – Yield 1992 & 1993

Hitchcock, OK - 1994 & 1996: 

Hitchcock, OK - 1994 & 1996

Hitchcock, OK – 1998 & 1999: 

Hitchcock, OK – 1998 & 1999

Hitchcock, OK: 

Hitchcock, OK < 0.85 0.85 – 0.95 0.65 – 1.05 1.05 – 1.15 1.15 Field Boundary Average Normalized Yield Broken out of native grass pasture in 10 ac increments in the 1970’s Farm since homesteaded Low pH in 2001

Disease Effect on Estimated Yield -Enid, OK: 

Disease Effect on Estimated Yield -Enid, OK 5% Set-Aside Ground Chisholm 2180

Carrier, OK – 1999 Yield: 

Carrier, OK – 1999 Yield Hail Damage

Tonkawa, OK: 

Tonkawa, OK Sprayed with Metribuzin for Cheat Not sprayed for cheat Area was intensively grazed by 105 calves. Wheat yield was about 3 times greater than estimated Saline Soil

Tonkawa, OK: 

Tonkawa, OK

Slide33: 

What may be gained by even higher resolution sensing? 25 m Resolution (Re-sampled) Landsat TM 1m Resolution NDVI

Conclusions: 

Conclusions Satellite imagery can be used to predict yields. Normalized estimated yield can be used for management decisions: Define average relative yield Identify regions of high and low yield whose cause changes slowly over time Drainage Soil type Organic matter pH

Conclusions: 

Conclusions Images can be used to define management zones for the purpose of managing these variables. Imagery can complement yield monitor data or when yield data are not available can serve as a surrogate. Currently, Landsat TM images are the only source of historical data for the entire United States, and, despite the coarse resolution provides, a means to begin managing less than field size areas.

Conclusions: 

Conclusions Potentially, with sufficient data, harvester yield measurements can be used to obtain the same management data as satellites.