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Geostatistical Integration of Yield Monitor Data and Remote Sensing Improves Yield Maps

Dep. of Agron. and Hortic., Univ. of Nebraska, P.O. Box 830915, Lincoln, NE 68583-0915, USA

View larger version (159K): [in a new window]   Fig. 1. Maps of grain yield and selected vegetation indices of (Field A, left) maize and (Field B, right) soybean. Data shown are cleaned yield monitor data (top) and vegetation indices [green atmospherically resistant vegetation index (GARI) for Field A and green normalized difference vegetation index (GNDVI) for Field B] obtained from an IKONOS satellite image taken on 1 Sept. 2002. Symbols show the locations of the hand-harvested validation points in each field.

View larger version (48K): [in a new window]   Fig. 2. Experimental semivariograms and fitted variogram models of combine-harvested grain yield and selected vegetation indices of (Field A) maize and (Field B) soybean. All semivariograms were modeled as a nested model comprised of a nugget effect and two exponential (Field A) or two spherical (Field B) structures for which sills and effective ranges are given as inserts in each graph. NDVI, normalized difference vegetation index; GNDVI, green normalized difference vegetation index; GARI, green atmospherically resistant vegetation index.

View larger version (23K): [in a new window]   Fig. 3. Mean error (ME), mean absolute error (MAE), and root mean squared error (RMSE) referring to the validation data sets with different estimation methods and imagery information as secondary variables at two fields. Symbols indicate the different yield prediction methods used (OK, ordinary kriging; D, kriging with external drift; C, cokriging; S, simple kriging with varying local means). Numbers indicate the different vegetation measures used as secondary information for the yield prediction [1 = normalized difference vegetation index (NDVI), 2 = green normalized difference vegetation index (GNDVI), 3 = green atmospherically resistant vegetation index (GARI)].

View larger version (101K): [in a new window]   Fig. 4. Maps of (left) maize yield (Mg ha–1) in Field A and (right) soybean yield (Mg ha–1) in Field B obtained by ordinary kriging of yield monitor data (OK) or simple kriging with local means (SKLM). In SKLM, remotely sensed green atmospherically resistant vegetation index (GARI, Field A) or green normalized difference vegetation index (GNDVI, Field B) were used as a secondary variables.

View larger version (29K): [in a new window]   Fig. 5. Observed and predicted grain yields of (Field A) maize and (Field B) soybean at hand-harvested validation points and the distribution of the differences between predicted and observed values (residuals). Yield predictions were based on ordinary kriging (OK) of yield monitor data or simple kriging with local means (SKLM) of yield monitor data in combination with green atmospherically resistant vegetation index (GARI, Field A) or in combination with green normalized difference vegetation index (GNDVI, Field B).

View larger version (34K): [in a new window]   Fig. 6. Observed and predict grain yield of (Field A) maize and (Field B) soybean at combine-harvested locations and the distribution of differences between observed and predicted yields in relation to measured combine yields. Predicted yields were based on simple kriging with local means using green atmospherically resistant vegetation index (GARI, Field A) or green normalized difference vegetation index (GNDVI, Field B) as secondary variables.