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Spatial prediction of soil electrical conductivity using soil axillary data, soft data derived from general linear model and error measurement

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(13 صفحه - از 87 تا 99)

چکیده:

Indirect measurement of soil electrical conductivity (EC) has become a major data source in spatial/temporal monitoring of soil salinity. However, in many cases, the weak correlation between direct and indirect measurement of EC has reduced the accuracy and performance of the predicted maps. The objective of this research was to estimate soil EC based on a general linear model via using several soil properties. Through calibration equations, the error involved in such model-based data was calculated and employed in mapping soil EC using kriging with measurement errors (KME) method. The results were then compared with those of ordinary kriging (OK) and co-kriging (CK). Soil samples were taken from the depth of 0-20 cm in 78 points with spatial intervals of 500 m from an area of 40 km2, and they were analyzed for their electrical conductivity (EC) and certain other soil properties. Measured soil EC data (hard data) and auxiliary soil data were further used to develop the semi-variance and cross-semi-variance functions; moreover, soil salinity prediction was done on a grid of 100 m with OK and CK methods. Afterwards, the most optimal EC estimation model was developed using auxiliary soil data and GLM. As predicted values always involve uncertainty, the error involved with the predicted values was calculated and then the calibration equations were adjusted. Lastly, soil salinity was predicted using KME method. Results showed that the OK method had the lowest MSE and RMSE values, 0.65 and 0.8 dS m-1, respectively. Furthermore, among the auxiliary data, pH and silt content resulted in some of the best cross-semi-variance functions, among which, silt had a better performance regarding the spatial prediction of soil EC. The GLM model developed with the calculated error and KME resulted in predictions close to those of OK method (with MSE and RMSE of 0.74 and 0.86 dS m-1, respectively). KME method provided the possibility of merging error resulting from the use of soft data, derived from prediction equations; therefore, it successfully improved the spatial prediction of soil electrical conductivity

خلاصه ماشینی:

Through calibration equations, the error involved in such model-based data was calculated and employed in mapping soil EC using kriging with measurement errors (KME) method. KME method provided the possibility of merging error resulting from the use of soft data, derived from prediction equations; therefore, it successfully improved the spatial prediction of soil electrical conductivity. They further made use of the calibration equations between field and laboratory measured soil salinity and defined probability density functions to calculate the error of soft data used in prediction. Because estimated EC values always involve uncertainty, through calibration equations, their error was calculated and used in the spatial prediction of soil EC using kriging with measurement errors method (KME). Sodium adsorption ratio (SAR) was calculated using equation 1 (soil survey staff, 2014): −1 modeling type (linear/nonlinear) or any kind of data (soil/remote sensing), the estimated values were treated as error-free and used in spatial ���� = ��� ��� � � � (���� � ) √ ( � ��� �� (� + ) ( ��� � �−1 ) ) 2 (1) )VIew the image of this page) Fig. 1. 3. Geostatistical analysis In this study, ordinary kriging (OK), co- kriging (CK), and kriging with measurement errors (KME) were used to predict the soil salinity using soil properties and soft data derived from general linear model (GLM). However, when the data used in predictions are not the result of direct measurements (for example, calculated from a model developed from other soil properties), they are involved with uncertainty; moreover, although they might be highly correlated with direct measurements, they are not error-free (called soft data). Spatial prediction of soil salinity using kriging with measurement errors and probabilistic soft data.

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