Incorporating Auxiliary Data of Different Spatial Scales for Spatial Prediction of Soil Nitrogen Using Robust Residual Cokriging (RRCoK)

Auxiliary data has usually been incorporated into geostatistics for high-accuracy spatial prediction. Due to the different spatial scales, category and point auxiliary data have rarely been incorporated into prediction models together. Moreover, traditionally used geostatistical models are usually sensitive to outliers. This study first quantified the land-use type (LUT) effect on soil total nitrogen (TN) in Hanchuan County, China. Next, the relationship between soil TN and the auxiliary soil organic matter (SOM) was explored. Then, robust residual cokriging (RRCoK) with LUTs was proposed for the spatial prediction of soil TN. Finally, its spatial prediction accuracy was compared with that of ordinary kriging (OK), robust cokriging (RCoK), and robust residual kriging (RRK). Results show that: (i) both LUT and SOM are closely related to soil TN; (ii) by incorporating SOM, the relative improvement accuracy of RCoK over OK was 29.41%; (iii) by incorporating LUTs, the relative improvement accuracy of RRK over OK was 33.33%; (iv) RRCoK obtained the highest spatial prediction accuracy (RI = 43.14%). It is concluded that the recommended method, RRCoK, can effectively incorporate category and point auxiliary data together for the high-accuracy spatial prediction of soil properties.

Prediction of Total Nitrogen Distribution in Surface Soil Based on Multi-source Auxiliary Variables and Random Forest Approach

<p>Soil total nitrogen is closely related to soil quality and fertility. It is of great significance to know the spatial distribution characteristics of soil total nitrogen for the implementation of precision agriculture management. The spatial distribution of total nitrogen in the surface soil of Xunwu County was predicted and mapped by using two methods: random forest and random forest plus residuals kriging. These methods were combined with multi-source auxiliary variables such as (i) terrain factors, (ii) geographical coordinate, (iii) remote sensing factors, (iv) climate factors, (v) distance factors, and (vi) soil physical or chemical factors. Also, the prediction accuracy of the two models was compared after 100 times of repeated operation. Our results show that the mean values of the decision coefficient (<em>R</em><sup>2</sup> = 0.6291) and concordance correlation coefficient (CCC = 0.7613) of the random forest model were higher than those of the random forest plus residual kriging method (<em>R</em><sup>2</sup> = 0.5719, CCC = 0.6881). Also, the mean values of the mean absolute error (MAE = 0.1570 g·kg<sup>-1</sup>) and root mean squared error (RMSE = 0.2108 g·kg<sup>-1</sup>) were lower than those of the random forest plus residual kriging method (MAE = 0.1682 g·kg<sup>-1</sup>, RMSE = 0.2267 g·kg<sup>-1</sup>). Importantly, adding residual to the random forest model did not improve its accuracy. These results suggest that the random forest model can be used as a new method for predicting soil properties, and it provides technical support for the implementation of agricultural management.</p>

Estimation of the distribution of the total net radiative flux from satellite and automatic weather station data in the Upper Blue Nile basin, Ethiopia

Net radiation is an important factor in studies of land–atmosphere processes, water resource management, and global climate change. This is particularly true for the Upper Blue Nile (UBN) basin, where significant parts of the basin are dry and evapotranspiration (ET) is a major mechanism for water loss. However, net radiation has not yet been appropriately parameterized in the basin. In this study, we estimated the instantaneous distribution of the net radiation flux in the basin using data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the Terra satellite and Automatic Weather Station (AWS) data. Downward shortwave radiation and air temperature usually vary with topography, so we applied residual kriging spatial interpolation techniques to convert AWS data for point locations into gridded surface data. Simulated net radiation outputs were validated through comparison with independent field measurements. Validation results show that our method successfully reproduced the downward shortwave, upward shortwave, and net radiation fluxes. Using AWS data and residual kriging spatial interpolation techniques makes our results robust and comparable to previous works that used satellite data at a finer spatial resolution than MODIS. The estimated net shortwave, longwave, and total radiation fluxes were in close agreement with ground truth measurements, with mean bias (MB) values of − 14.84, 5.7, and 20.53 W m−2 and root mean square error (RMSE) values 83.43, 32.54, and 78.07 W m−2, respectively. The method presented here has potential applications in research focused on energy balance, ET estimation, and weather prediction for regions with similar physiographic features to those of the Nile basin.

Conditional simulation of surface rainfall fields using modified phase annealing

The accuracy of quantitative precipitation estimation (QPE) over a given region and period is of vital importance across multiple domains and disciplines. However, due to the intricate temporospatial variability and the intermittent nature of precipitation, it is challenging to obtain QPE with adequate accuracy. This paper aims to simulate rainfall fields while honoring both the local constraints imposed by the point-wise rain gauge observations and the global constraints imposed by the field measurements obtained from weather radar. The conditional simulation method employed in this study is modified phase annealing (PA), which is practically an evolution from the traditional simulated annealing (SA). Yet unlike SA, which implements perturbations in the spatial field, PA implements perturbations in Fourier space, making it superior to SA in many respects. PA is developed in two ways. First, taking advantage of the global characteristic of PA, the method is only used to deal with global constraints, and the local ones are handed over to residual kriging. Second, except for the system temperature, the number of perturbed phases is also annealed during the simulation process, making the influence of the perturbation more global at initial phases and decreasing the global impact of the perturbation gradually as the number of perturbed phases decreases. The proposed method is used to simulate the rainfall field for a 30 min event using different scenarios: with and without integrating the uncertainty of the radar-indicated rainfall pattern and with different objective functions.

Estimations of net radiation flux distributions from automatic weather stations and satellite data in the Upper Blue Nile (UBN) basin, Ethiopia

<p>Net radiation is the main energy balance component of land surfaces. It is an important factor in the studies of land-atmosphere processes, water resources management and so on. This is particularly true in the UBN basin where significant parts of the basin are dry and evapotranspiration (ET) is a major path of water loss. In this paper, we have estimated instantaneous net radiation distributions in the basin from MODIS Terra satellite and Automatic Weather Station (AWS) data. As downward shortwave radiation and air temperature usually vary spatially due to topographic effects, which are common features of our study area, we had applied residual kriging spatial interpolation approaches in the conversion processes of point weather data to surface data. Validation attempts of the simulated net radiation outputs with an independent field measurement at Choke flux tower site, which is in the central part of the basin, has shown that our method were able to reproduce downward shortwave, upward shortwave., and net radiation flux with a statistical metrics of Mean bias (MB) and Root Mean Square (RMSE) lesser than other studies done in similar physiographic regions in several parts of the world. It looked that the use of AWS data and residual kriging spatial interpolation technique made our results robust and even comparable to works done using finer spatial resolution satellite data than MODIS. The estimated net shortwave, net longwave and overall net radiations were in close agreement with ground truth measurements with MB of -14.84, 5.7 & 20.53 Wm<sup>-</sup><sup>2</sup> and RMSE 83.43, 32.54 & 78.07 Wm<sup>-</sup><sup>2</sup> respectively. The method has potential applications in research works like energy balance, ET estimation, and weather predictions in regions with similar physiographic features as that of the Nile basin.</p>