Designing and validating digital soil mapping (DSM) techniques can facilitate precision agriculture implementation. This study generates and validates a technique for the spatial prediction of soil properties based on C-band radar data. To this end, (i) we focused on working at farm-field scale and conditions, a fact scarcely reported; (ii) we validated the usefulness of Random Forest regression (RF) to predict soil properties based on C-band radar data; (iii) we validated the prediction accuracy of C-band radar data according to the coverage condition (for example: crop or fallow); and (iv) we aimed to find spatial relationship between soil apparent electrical conductivity and C-band radar. The experiment was conducted on two agricultural fields in the southern Argentine Pampas. Fifty one Sentinel 1 Level-1 GRD (Grid) products of C-band frequency (5.36 GHz) were processed. VH and VV polarizations and the dual polarization SAR vegetation index (DPSVI) were estimated. Soil information was obtained through regular-grid sample scheme and apparent soil electrical conductivity (ECa) measurements. Soil properties predicted were: texture, effective soil depth, ECa at 0-0.3m depth and ECa at 0-0.9m depth. The effect of water, vegetation and soil on the depolarization from SAR backscattering was analyzed. Complementary, spatial predictions of all soil properties from ordinary cokriging and Conditioned Latin hypercube sampling (cLHS) were evaluated using six different soil sample sizes: 20, 40, 60, 80, 100 and the total of the grid sampling scheme. The results demonstrate that the prediction accuracy of C-band SAR data for most of the soil properties evaluated varies considerably and is closely dependent on the coverage type and weather dynamics. The polarizations with high prediction accuracy of all soil properties showed low values of σ_VV^o and σ_VH^o, while those with low prediction accuracy showed high values of σ_VV^o and low values of σ_VH^o. The spatial patterns among maps of all soil properties using all samples and all sample sizes were similar. In conditions when summer crops demand large amount of water and there is soil water deficit backscattering showed higher prediction accuracy for most soil properties. During the fallow season, the prediction accuracy decreased and the spatial prediction accuracy was closely dependent on the number of validation samples. The findings of this study corroborates that DSM techniques at field scale can be achieved by using C-band SAR data. Extrapolation y applicability of this study to other areas remain to be tested.

设计和验证数字土壤测绘（DSM）技术可以促进精确农业的实施。这项研究生成并验证了基于C波段雷达数据的土壤特性空间预测技术。为此，（i）我们专注于在农田规模和条件下工作，这一事实鲜有报道；（ii）我们验证了基于C波段雷达数据的随机森林回归（RF）预测土壤性质的有效性；（iii）我们根据覆盖条件（例如：农作物或休耕地）验证了C波段雷达数据的预测准确性；（iv）我们的目的是寻找土壤表观电导率与C波段雷达之间的空间关系。该实验是在阿根廷南部南美大草原的两个农田上进行的。五十一个哨兵1处理了C波段频率（5.36 GHz）的Level-1 GRD（网格）产​​品。估算了VH和VV极化以及双极化SAR植被指数（DPSVI）。通过常规网格采样方案和表观土壤电导率（ECa）测量获得土壤信息。预测的土壤特性为：质地，有效土壤深度，0-0.3m深度的ECa和0-0.9m深度的ECa。分析了水，植被和土壤对SAR反向散射的去极化作用。使用六种不同的土壤样本大小：20、40、60、80、100和网格抽样方案的总和，评估了普通协同克里格法和条件拉丁超立方采样（cLHS）对所有土壤特性的补充空间预测。结果表明，对于评估的大多数土壤特性，C波段SAR数据的预测精度差异很大，并且密切取决于覆盖类型和天气动态。对所有土壤特性均具有较高预测精度的极化显示出较低的σ值_VV ^Ò和σ _VH ^ö，而那些具有低预测精度显示的σ高的值_VV ^ö和低值σ _VH ^ö。使用所有样本和所有样本量的所有土壤特性图之间的空间格局相似。在夏季作物需要大量水且土壤缺水的情况下，反向散射对大多数土壤特性显示出较高的预测精度。在休耕季节，预测精度下降，空间预测精度与验证样本数密切相关。这项研究的结果证实，通过使用C波段SAR数据可以在现场实现DSM技术。本研究在其他领域的外推适用性仍有待测试。