Residual Kriging Research Articles

National, satellite-based land-use regression models for estimating long-term annual NO2 exposure across India

In India, scarcity of ground-based measurements of nitrogen dioxide (NO2) is a major challenge for estimating long-term exposure and associated health impacts. This study aimed to develop and validate a national-scale annual NO2 exposure model for India for 2019 and determine if model cross-validation predictive ability was improved by including non-continuous (manual) measurements along with reference-grade, continuous measurements.We used a supervised forward-addition linear regression method to fit land use regression (LUR) models developed with up to 804 Central Pollution Control Board ground monitoring stations (n = 157 continuous, n = 647 manual) and 209 spatial predictor variables, including satellite-based tropospheric NO2 columns. Two models were developed: one using continuous sites only and one using continuous and manual sites, with standard diagnostics and cross-validation (CV) methods. We also assessed if the kriging of final model residuals reduced spatial autocorrelation and improved model CV results. LUR coefficients for the best-performing model were applied to predictors for 2015–2021 and gridded at 100 m to estimate population-weighted exposure.The continuous sites-only model and combined continuous and manual sites models had CV-R2 values of 0.59 (root-mean-square error [RMSE]: 9.4 μg/m3) and 0.54 (RMSE: 8.3 μg/m3), respectively, and both included the satellite NO2 predictor. Kriging residuals increased the CV-R2 of the combined model to 0.70 (RMSE: 7.2 μg/m3) but offered no improvement for the continuous site model. National population-weighted average NO2 was 22.1 μg/m3 in 2019. We estimated over 92% of the Indian population was exposed to annual NO2 exceeding the WHO air quality guideline (10 μg/m3). In Delhi, Mumbai, and Kolkata, an estimated 45%, 100%, and 100% of the population, respectively, experienced annual NO2 levels that surpassed Indian standards (40 μg/m3). To our knowledge, this is the first such long-term NO2 LUR model specific to India, and predictions are available to interested researchers.

수치해석 접근법에 따른 지하수 공간분포 예측

This study applied the MODFLOW model using finite difference method and the NNRK model combining neural network and residual kriging for spatial groundwater level prediction in Jeju Island, South Korea. For the MODFLOW model, the differences between observed and predicted groundwater levels were 0.014-4.327 m and the relative errors were 0.50-50.82 %. For the NNRK model, the differences between observed and predicted groundwater levels were 0.022-6.185 m and the relative errors were 0.66-49.35 %. Based on MSRE, IDW model, which is a non-spatial statistical model, showed better spatial prediction results than SK and OK models, whereas RK and NNRK models outperformed the IDW model. The MODFLOW model showed relatively better results than the NNRK model in dense areas considering recharge and geological structure although the MODFLOW and NNRK models produced similar results. Furthermore, the MODFLOW model was found to effectively reflect groundwater flow by strata and temporal groundwater head fluctuations due to pumping compared to the NNRK model.

Soil organic carbon stock retrieval from Sentinel-2A using a hybrid approach.

Digital soil maps find application in numerous fields, making their accuracy a crucial factor. Mapping soil properties in homogeneous landscapes where the soil surface is concealed, as in forests, presents a complex challenge. In this study, we evaluated the spatial distribution of soil organic carbon stocks (SOCstock) under forest vegetation using three methods: regression kriging (RK), random forest (RF), and RF combined with ordinary kriging of residuals (RFOK) in combination with Sentinel-2A satellite data. We also compared their accuracies and identified key influencing factors. We determined that SOCstock ranged from 0.6 to 10.9 kg/m2 with an average value of 4.9 kg/m2. Among the modelling approaches, we found that the RFOK exhibited the highest accuracy (RMSE = 1.58 kg/m2, NSE = 0.33), while the RK demonstrated a lack of spatial correlation of residuals, rendering this method inapplicable. An analysis of variable importance revealed that the SWIR B12 band of the Sentinel-2A satellite contributed the most to RFOK predictions. Weconcluded that the RFOK hybrid approach outperformed the others, potentially serving as a foundation for digital soil mapping under similar environmental conditions. Therefore, it is essential to consider spatial correlations when mapping soil properties in ecosystems that are inaccessible for capturing the spectral response of the soil surface.

Flight Load Calculation Using Neural Network Residual Kriging

Flight load calculation, an important step in aircraft design and optimization, typically involves millions of computations and requires significant computing resources and time. Improving the efficiency of flight load calculations while maintaining accuracy is therefore of great significance for shortening research and development cycles. This study investigated and compared multiple algorithms, including the neural network model, the Kriging surrogate model, and the neural network residual Kriging (NNRK) model, for flight load analysis. The accuracies of all models were confirmed through evaluation, with NNRK being the most efficient, making it highly suitable for flight load analysis. The flight load data of a civil aircraft, including the total weight, the center of gravity, the pitch moment of inertia, the altitude, the Mach number, the airspeed, the velocity pressure, the pitch rate, the load factor, and the angle of attack as input parameters, were used as sample data to establish models, for predicting wing loads under different flight conditions. The accuracies of all regressions were confirmed through evaluation, with NNRK being the most efficient. The flight load calculation shows that NNRK can significantly improve analysis efficiency and provide new insights into efficient and comprehensive flight load analysis.

Mapping Topsoil Total Nitrogen Using Random Forest and Modified Regression Kriging in Agricultural Areas of Central China

Accurate understanding of spatial distribution and variability of soil total nitrogen (TN) is critical for the site-specific nitrogen management. Based on 4337 newly obtained soil observations and 33 covariates, this study applied the random forest (RF) algorithm and modified regression kriging (RF combined with residual kriging: RFK, hereafter) model to spatially predict and map topsoil TN content in agricultural areas of Henan Province, central China. According to the RFK prediction, topsoil TN content ranged from 0.52 to 1.81 g kg-1, and the farmland with the topsoil TN contents of 1.00-1.23 g kg-1 and 0.80-1.23 g kg-1 accounted for 48.2% and 81.2% of the total farmland area, respectively. Spatially, the topsoil TN in the study area was generally higher in the west and lower in the east. By using the Boruta variable selection algorithm, soil organic matter (SOM) and available potassium contents in topsoil, nitrogen deposition, average annual precipitation, livestock discharges, and topsoil pH were identified as the main factors driving the spatial distribution and variation of soil TN in the study area. The RF and RFK models used showed the expected performance and achieved acceptable TN prediction accuracy. In comparison, RFK performed slightly better than the RF model. The R2 and RMSE achieved by the RFK model were improved by 4.5% and 4.5%, respectively, compared with that by the RF model. However, the results suggest that RFK was inferior to the RF model in quantifying prediction uncertainty and thus may have a slight disadvantage in model reliability.

Mapping wood volume in seasonally dry vegetation of Caatinga in Bahia State, Brazil

The Caatinga biome in Brazil comprises the largest and most continuous expanse of the seasonally dry tropical forest (SDTF) worldwide; nevertheless, it is among the most threatened and least studied, despite its ecological and biogeographical importance. The spatial distribution of volumetric wood stocks in the Caatinga and the relationship with environmental factors remain unknown. Therefore, this study intends to quantify and analyze the spatial distribution of wood volume as a function of environmental variables in Caatinga vegetation in Bahia State, Brazil. Volumetric estimates were obtained at the plot and fragment level. The multiple linear regression techniques were adopted, using environmental variables in the area as predictors. Spatial modeling was performed using the geostatistical kriging approach with the model residuals. The model developed presented a reasonable fit for the volume m3 ha with r2 of 0.54 and Root Mean Square Error (RMSE) of 10.9 m3 ha–1. The kriging of ordinary residuals suggested low error estimates in unsampled locations and balance in the under and overestimates of the model. The regression kriging approach provided greater detailing of the global wood volume stock map, yielding volume estimates that ranged from 0.01 to 109 m3 ha–1. Elevation, mean annual temperature, and precipitation of the driest month are strong environmental predictors for volume estimation. This information is necessary to development action plans for sustainable management and use of the Caatinga SDTF in Bahia State, Brazil.

Spatial Gap-Filling of GK2A Daily Sea Surface Temperature (SST) around the Korean Peninsula Using Meteorological Data and Regression Residual Kriging (RRK)

Satellite remote sensing can measure large ocean surface areas, but the infrared-based sea surface temperature (SST) might not be correctly calculated for the pixels under clouds, resulting in missing values in satellite images. Early studies for the gap-free raster maps of satellite SST were based on spatial interpolation using in situ measurements. In this paper, however, an alternative spatial gap-filling method using regression residual kriging (RRK) for the Geostationary Korea Multi-Purpose Satellite-2A (GK2A) daily SST was examined for the seas around the Korean Peninsula. Extreme outliers were first removed from the in situ measurements and the GK2A daily SST images using multi-step statistical procedures. For the pixels on the in situ measurements after the quality control, a multiple linear regression (MLR) model was built using the selected meteorological variables such as daily SST climatology value, specific humidity, and maximum wind speed. The irregular point residuals from the MLR model were transformed into a residual grid by optimized kriging for the residual compensation for the MLR estimation of the null pixels. The RRK residual compensation method improved accuracy considerably compared with the in situ measurements. The gap-filled 18,876 pixels showed the mean bias error (MBE) of −0.001 °C, the mean absolute error (MAE) of 0.315 °C, the root mean square error (RMSE) of 0.550 °C, and the correlation coefficient (CC) of 0.994. The case studies made sure that the gap-filled SST with RRK had very similar values to the in situ measurements to those of the MLR-only method. This was more apparent in the typhoon case: our RRK result was also stable under the influence of typhoons because it can cope with the abrupt changes in marine meteorology.

A method for predicting hydrogen and oxygen isotope distributions across a region's river network using reach-scale environmental attributes

Abstract. Stable isotope ratios (isotope values) of surface water reflect hydrological pathways, mixing processes, and atmospheric exchange within catchments. Development of maps of surface water isotope values (isoscapes) is limited by methods to interpolate point measures across river networks. Catchment attributes that alter surface water isotope values affect downstream river reaches via flow, but some attributes such as artificial dams are no more likely to affect nearby unconnected catchments than distant ones. Hence, simple distance-based geospatial and statistical interpolation methods used to develop isoscapes for precipitation and terrestrial systems are less appropriate for river networks. We used a water-balance-based method to map long-term average δ2H and δ18O for New Zealand rivers, incorporating corrections using catchment environmental predictors. Inputs to the model are national rainfall precipitation isoscapes, a digital elevation layer, a national river water isotope monitoring dataset (3 years of monthly sampling at 58 sites), and river environmental databases covering around 600 000 reaches and over 400 000 km of rivers. Much of the spatial variability in δ2H and δ18O of New Zealand river water was explained using the initial combination of precipitation isoscapes and a simple water balance model. δ2H and δ18O isoscapes produced by subsequently applying residuals from the water balance model as a correction factor across the river network using regression kriging showed improved fits to the validation data compared to the correction using ordinary kriging. Predictors of high importance in the regression included upstream lake and wetland area, which was not strongly spatially autocorrelated nationally. Hence, additional hydrological process information such as evaporation effects can be incorporated into river isoscapes using regression kriging of residuals. The resulting isoscapes have potential applications in ecological, hydrological, and provenance studies that consider differences between surface water isotope values and those of other components of the hydrological cycle (e.g. subsurface runoff or local precipitation).

Machine Learning Techniques for Estimating Hydraulic Properties of the Topsoil across the Zambezi River Basin

It is critical to produce more crop per drop in an environment where water availability is decreasing and competition for water is increasing. In order to build such agricultural production systems, well parameterized crop growth models are essential. While in most crop growth modeling research, focus is on gathering model inputs such as climate data, less emphasis is paid to collecting the critical soil hydraulic properties (SHPs) data needed to operate crop growth models. Collection of SHPs data for the Zambezi River Basin (ZRB) is extremely labor-intensive and expensive, thus alternate technologies such as digital soil mapping (DSM) must be explored. We evaluated five types of DSM models to establish the best spatially explicit estimates of the soil water content at pF0.0 (saturation), pF2.0 (field capacity), and pF4.2 (wilting point), and of the saturated hydraulic conductivity (Ksat) across the ZRB by using estimates of locally calibrated pedotransfer functions of 1481 locations for training and testing the DSM models, as well as a reference dataset of measurements from 174 locations for validating the DSM models. We produced coverages of environmental covariates from various source datasets, including climate variables, soil and land use maps, parent materials and lithologic units, derivatives of a digital elevation model (DEM), and Landsat imagery with a spatial resolution of 90 m. The five types of models included multiple linear regression and four machine learning techniques: artificial neural network, gradient boosted regression trees, random forest, and support vector machine. Where the residuals of the initial DSM models were spatially autocorrelated, the models were extended/complemented with residual kriging (RK). Spatial autocorrelation in the model residuals was observed for all five models of each of the three water contents, but not for Ksat. On average for the water content, the R2 ranged from 0.40 to 0.80 in training and test datasets before adding kriged model residuals and ranged from 0.80 to 0.95 after adding model residuals. Overall, the best prediction method consisted of random forest as the deterministic model, complemented with RK, whereby soil texture followed by climate and topographic elevation variables were the most important covariates. The resulting maps are a ready-to-use resource for hydrologists and crop modelers to aliment and calibrate their hydrological and crop growth models.

Mapping the time-varying spatial heterogeneity of temperature processes over the urban landscape of Augsburg, Germany

Generating high-resolution spatial interpolations of temperature processes is a vital task for studying urban climate anomalies and their various consequences. Such processes often constitute a complex and demanding data environment: Anthropogenic and natural conditions of the urban landscape result in anisotropic spatial dependencies and trend patterns that often vary in diurnal and seasonal cycles. Two-step geostatistical methods such as residual kriging take spatial heterogeneity into account but ignore the temporal dimension, which can result in a significant loss of potentially useful information. In this study, we propose nonparametric spatial detrending to obtain a process that fulfills the assumptions of ordinary kriging. In our application to urban air temperature series from monitoring sites distributed over the urban and suburban area of Augsburg, Germany, we provide an in-depth analysis of time-varying spatial heterogeneity. By using sub-sampling to account for diurnal, seasonal, and spatial trends, we produce interpolation maps with a resolution of 100m×100m. The validation in a narrower sense is based on cross-validation and shows favorable behavior of the proposed method even when sub-samples are neglected. The broader sense validation is based on hold-out monitoring sites and provides further empirical support for our proposal.

Modeling exposure to airborne metals using moss biomonitoring in cemeteries in two urban areas around Paris and Lyon in France

Exposure of the general population to airborne metals remains poorly estimated despite the potential health risks. Passive moss biomonitoring can proxy air quality at fine resolution over large areas, mainly in rural areas. We adapted the technique to urban areas to develop fine concentration maps for several metals for Constances cohort's participants. We sampled Grimmia pulvinata in 77 and 51 cemeteries within ∼50 km of Paris and Lyon city centers, respectively. We developed land-use regression models for 14 metals including cadmium, lead, and antimony; potential predictors included the amount of urban, agricultural, forest, and water around cemeteries, population density, altitude, and distance to major roads. We used both kriging with external drift and land use regression followed by residual kriging when necessary to derive concentration maps (500 × 500 m) for each metal and region. Both approaches led to similar results. The most frequent predictors were the amount of urban, agricultural, or forest areas. Depending on the metal, the models explained part of the spatial variability, from 6% for vanadium in Lyon to 84% for antimony in Paris, but mostly between 20% and 60%, with better results for metals emitted by human activities. Moss biomonitoring in cemeteries proves efficient for obtaining airborne metal exposures in urban areas for the most common metals.

Physics-based Residual Kriging for dynamically evolving functional random fields

We present a novel approach named Physics-based Residual Kriging for the statistical prediction of spatially dependent functional data. It incorporates a physical model—expressed by a partial differential equation—within a Universal Kriging setting through a geostatistical modelization of the residuals with respect to the physical model. The approach is extended to deal with sequential problems, where samples of functional data become available along consecutive time intervals, in a context where the physical and stochastic processes generating them evolve, as time intervals succeed one another. An incremental modeling is used to account for both these dynamics and the misfit between previous predictions and actual observations. We apply Physics-based Residual Kriging to forecast production rates of wells operating in a mature reservoir according to a given drilling schedule. We evaluate the predictive errors of the method in two different case studies. The first deals with a single-phase reservoir where production is supported by fluid injection, while the second considers again a single-phase reservoir but the production is driven by rock compaction.

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.

High-Resolution North Sulawesi Drought Hazzard Mapping Based on Consecutive Dry Days (CDD)

North Sulawesi is one of the Province in northern Indonesia with high spatial annual rainfall variations and influenced by global climate anomaly that can lead to extreme events and disaster occurrence, such as flood, landslide, drought, etc. The purpose of this study is to generate high-resolution meteorological hazard map based on long-term historical consecutive dry days (CDD) over the North Sulawesi region. CDD was calculated based on observed daily precipitation data from Indonesia Agency for Meteorology, Climatology, and Geophysics (BMKG) surface observation station network (CDDobs) and the daily-improved Climate Hazards group Infrared Precipitation with Stations (CHIRPS) version 2.0 (CDDCHIRPS) during 1981 – 2010 period. The Japanese 55-year Reanalysis (JRA-55) data obtained from iTacs (Interactive Tool for Analysis of the Climate System) with the same time scale period also used to explain physical – dynamical atmospheric properties related to drought hazard over this region. The Geostatistical approach using regression kriging method was applied as spatial interpolation technique to generate high resolution gridded (0.05° × 0.05°) drought hazard map. This method combines a regression of CDDobs as dependent variable (target variable) on CDDCHIRPS as predictors with kriging of the prediction residuals. The results show that most of the areas were categorized as medium drought hazard level with CDD values ranging from 80-100 days. Meanwhile, small islands around main Sulawesi island such as Sangihe and Karakelong island are dominated by low drought hazard levels with CDD values ranging from 50-60 days. The highest levels of drought hazard area are located in South Bolaang Mongondow Regency.

Spatial extrapolation of topographic models for mapping soil organic carbon using local samples

Accurate prediction of soil organic carbon (SOC) at fine scales is frequently limited by data availability but fine-scale maps of soil properties are often of great importance for local agricultural land management and watershed processes assessment. Terrain attributes derived from elevation have been commonly used predictor variables in SOC mapping. The aim of this study is to map SOC density (0–30 cm) using hybrid methods that combine regional topographic models and a residual kriging procedure using local samples from an area of interest and evaluate the effects of different sampling designs for residual kriging on final map quality. The regional topographic model was developed based on light detection and ranging (lidar)-derived topographic covariates using random forest to map SOC density on a cropland site intensively sampled for SOC (n = 224). To test ability to generate a local calibration of this regional model, a second intensively sampled cropland site (n = 225) was used to generate a reference SOC density map where residual kriging was implemented involving different numbers of local samples (n = 10–255) and different sample selection schemes: stratified random sampling (SRS) and spatial coverage sampling with close-pairs inclusions (SCS+). To generate a population of local sample sets, 500 repeats were conducted for each scheme from the reference map. By analyzing the distribution of relative mean error (RME) of the overall map for the second site with repeated selection of local samples, we found a smaller variation in RME for SCS+ than that for SRS for a given sample size. However, the E(RME) estimates for SCS+ slightly varied with number of local samples used while the E(RME) for SRS was stabilized at zero over that range in local sampling intensity. There was negligible difference between the distributions of RME for the two sampling schemes at individual points, except a finding of somewhat lower RME for SCS+ based point estimates. These results indicate that SCS+ can generate smaller variation in overall map quality while SRS can provide unbiased mean estimate for an area of interest. This study demonstrates the utility of extrapolating a regional topographic model by use of a limited number of new samples for local calibration and provides insight for practitioners who are interested in mapping soil carbon distribution in sparsely sampled areas of interest.

Combining Geostatistics and Remote Sensing Data to Improve Spatiotemporal Analysis of Precipitation.

The wide availability of satellite data from many distributors in different domains of science has provided the opportunity for the development of new and improved methodologies to aid the analysis of environmental problems and to support more reliable estimations and forecasts. Moreover, the rapid development of specialized technologies in satellite instruments provides the opportunity to obtain a wide spectrum of various measurements. The purpose of this research is to use publicly available remote sensing product data computed from geostationary, polar and near-polar satellites and radar to improve space–time modeling and prediction of precipitation on Crete island in Greece. The proposed space–time kriging method carries out the fusion of remote sensing data with data from ground stations that monitor precipitation during the hydrological period 2009/10–2017/18. Precipitation observations are useful for water resources, flood and drought management studies. However, monitoring stations are usually sparse in regions with complex terrain, are clustered in valleys, and often have missing data. Satellite precipitation data are an attractive alternative to observations. The fusion of the datasets in terms of the space–time residual kriging method exploits the auxiliary satellite information and aids in the accurate and reliable estimation of precipitation rates at ungauged locations. In addition, it represents an alternative option for the improved modeling of precipitation variations in space and time. The obtained results were compared with the outcomes of similar works in the study area.

Spatially apportioning the source-oriented ecological risks of soil heavy metals using robust spatial receptor model with land-use data and robust residual kriging

Previous ecological risk assessments were mainly concentration-oriented rather than source-oriented. Moreover, land use is usually related to source emissions but was rarely used to improve the source apportionment accuracy. In this study, the land-use effects of heavy metals (HMs) in surface (0–20 cm) and subsurface (20–40 cm) soils were first explored using ANOVA in a suburb of Changzhou City, China; next, based on robust absolute principal component scores-robust geographically weighted regression (RAPCS/RGWR), this study proposed RAPCS/RGWR with land-use type (RAPCS/RGWR-LUT) and compared its source apportionment accuracy with those of basic RAPCS/RGWR and commonly-used absolute principal component scores/multiple linear regression (APCS/MLR); then, the source-oriented ecological risks were apportioned based on RAPCS/RGWR-LUT and Hakanson potential ecological risk index method; finally, this study proposed robust residual kriging with land-use type (RRK) for spatially predicting the source-oriented ecological risks, and compared its spatial prediction accuracy with those of robust ordinary kriging (ROK) and traditionally-used ordinary kriging (OK). Results showed that: (i) by incorporating land-use effects, RAPCS/RGWR-LUT obtained higher source apportionment accuracy than RAPCS/RGWR and APCS/MLR; (ii) the two most important external input sources of the ecological risks were 'atmospheric deposition' (PERIsurface = 47.11 and PERIsubsurface = 35.27) and 'agronomic measure' (PERIsurface = 28.93 and PERIsubsurface = 20.37); (iii) the biggest ecological risk factor was soil Cd (ERsurface = 57.14 and ERsubsurface = 47.62), which was mainly contributed by 'atmospheric deposition' (ERsurface=33.14 and ERsubsurface=25.71); (iv) RRK obtained higher spatial prediction accuracy than ROK and OK; (v) the high-risk areas derived from 'atmospheric deposition' were mainly located in the southwest of the study area, and the high-risk areas derived from 'agronomic measure' were scattered in the agricultural land in the north and south of the study area. The above information provided effective spatial decision support for reducing the source-oriented input of the ecological risks of soil HMs in a large-scale area.

Bias Correction of Ocean Bottom Temperature and Salinity Simulations From a Regional Circulation Model Using Regression Kriging

AbstractIt is well known that climate and circulation model simulation output are often systematically biased. However, existing bias correction methods typically ignore spatial autocorrelation of the biases and correct only the overall mean and variance, resulting in mismatched spatial variability between bias‐corrected simulations and observations. In this study, we propose using regression kriging (RK) to correct for biased spatial patterns and apply this method to Regional Ocean Modeling System (ROMS) simulated ocean bottom temperature and salinity for the Mid‐Atlantic Bight, USA. RK combines modeling non‐stationary trends using (generalized) regression with ordinary kriging (OK) of the regression residuals. We compared the performance of RK to a simpler OK method and a quantile mapping (QM) method often used for bias correction of such model output. These methods were evaluated using the Structural Similarity (SSIM) index that can simultaneously evaluate model accuracy, precision, and spatial similarities. Our results show that while both RK and QM can correct for overall biases of the mean and variation, only RK can effectively reduce the spatial‐temporal autocorrelation of the biases. The RK method was able to bias correct while preserving the spatial‐temporal trends of the ROMS simulated bottom temperature and salinity surfaces. The RK approach can easily be applied to any similar climate and circulation model simulation output. This study has profound implications for studies that use the output from such a model for fine‐scale mapping, habitat suitability modeling, species distribution modeling, or predicting the effects of climate change.

Integration of multispectral and hyperspectral data to map magnetic susceptibility and soil attributes at depth: A novel framework

The understanding of attributes and magnetic susceptibility (χ) at soil surface, mainly subsurface, is crucial due to their role to identify climate changes, soil degradation, soil classification systems, soil fertility, and pedogenesis. The integration of proximal sensing (PS) and remote sensing (RS) data sources could increase the efficiency of Digital Soil Mapping. Nevertheless, products of this integration need to be evaluated in hybrid, stochastic, and deterministic models to predict soil attributes and χ at surface and subsurface. This study investigates the PS and RS integration by applying four deterministic (e.g. Bayesian Regularised Neural Network, Generalised Linear Model, Random Forest and Cubist) and hybrid models (e.g. Regression Kriging of residuals of the best-fitted model) to create a new environmental variable, the Best Synthetic Soil Image (BSSI), at three soil depths (e.g. 0 – 20, 40 – 60 and 80 – 100 cm) that quantitatively represent the soil spectral signature. We also used the BSSI in a comparison with bare soil surface (e.g. SYSI - Synthetic Soil Image) to predict soil attributes and χ by performing the deterministic and hybrid models. We hypothesize that the BSSI, which integrates PS and RS data, enhances soil modelling predictions at subsurface by selecting the best model approach. The BSSI demonstrated original and valuable contribution to increase the predictive model power at deeper layers, while SYSI was effective at upper layers. The PS and RS integration helped to identify the main soil patterns horizontally and vertically, which traditional soil surveys have not been capable of representing.

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.