Abstract
This study proposes the development of a landscape-scale multitemporal soil pattern analysis (MSPA) method for organic matter (OM) estimation using RapidEye time series data analysis and GIS spatial data modeling, which is based on the methodology of Blasch et al. The results demonstrate (i) the potential of MSPA to predict OM for single fields and field composites with varying geomorphological, topographical, and pedological backgrounds and (ii) the method conversion of MSPA from the field scale to the multi-field landscape scale. For single fields, as well as for field composites, significant correlations between OM and the soil pattern detecting first standardized principal components were found. Thus, high-quality functional OM soil maps could be produced after excluding temporal effects by applying modified MSPA analysis steps. A regional OM prediction model was developed using four representative calibration test sites. The MSPA-method conversion was realized applying the transformation parameters of the soil-pattern detection algorithm used at the four calibration test sites and the developed regional prediction model to a multi-field, multitemporal, bare soil image mosaic of all agrarian fields of the Demmin study area in Northeast Germany. Results modeled at the landscape scale were validated at an independent test site with a resulting prediction error of 1.4 OM-% for the main OM value range of the Demmin study area.
Highlights
Owing to the demand for qualitative and quantitative soil information at multiple scales in precision agriculture (PA) [1,2,3,4,5,6], accurate functional soil maps are needed for the concept of site-specific management (SSM) to balance a profitable and cost-effective crop production with environmental concerns and sustainability [7,8,9,10]
The potential of multitemporal soil pattern analysis (MSPA) for organic matter (OM) estimation is revealed for both single fields and field composites independent of the sample size/density, the number of bare soil images, and/or physical-geographical and land use characteristics
In the process of method development, the field-scale based MSPA was successfully applied to single fields and field composites with diverse physical-geographical location characteristics
Summary
Owing to the demand for qualitative and quantitative soil information at multiple scales in precision agriculture (PA) [1,2,3,4,5,6], accurate functional soil maps are needed for the concept of site-specific management (SSM) to balance a profitable and cost-effective crop production with environmental concerns and sustainability [7,8,9,10]. To evaluate site-specific spatiotemporal variable soil properties at the field-scale, many functional soil mapping models and approaches based on digital elevation models (DEM), proximal and/or remote sensing (RS) data have been designed. Due to the advantages of existing, accessible data archives, relatively low-cost and high-temporal, high-spatial resolution multispectral imagery and time series are available for qualitative and partly-quantitative soil information extraction, deduction of soil patterns, and mapping of SSM zones and soil surface units [7,15]. During the attempt to determine the optimal content of soil organic matter of agriculturally-used soils, Wessolek et al (2008) [27]
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