Digital mapping of GlobalSoilMap soil properties at a broad scale: A review

Chen, Songchao; Arrouays, Dominique; Mulder, Vera Leatitia; Poggio, Laura; Minasny, Budiman; Roudier, Pierre; Libohova, Zamir; Lagacherie, Philippe; Shi, Zhou; Hannam, Jacqueline; Meersmans, Jeroen; Richer-de-Forges, Anne C.; Walter, Christian

doi:10.1016/j.geoderma.2021.115567

Cited by 224 publications

(135 citation statements)

References 212 publications

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“…Conversely, the EPO projection matrix developed from the field data should scan the soil samples under both moist intact and dry ground conditions. However, this method is difficult in practical applications when collecting an adequate number of soil samples for EPO development is impossible because of sampling or economic difficulties [71]. Overall, in our case, in terms of prediction accuracy, P1 and P2 slightly differed (Table 2).…”

Section: Practical Implicationsmentioning

confidence: 68%

Comparing Two Different Development Methods of External Parameter Orthogonalization for Estimating Organic Carbon from Field-Moist Intact Soils by Reflectance Spectroscopy

Hong

Chen³

et al. 2022

Remote Sensing

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Visible and near-infrared (Vis–NIR) spectroscopy can provide a rapid and inexpensive estimation for soil organic carbon (SOC). However, with respect to field in situ spectroscopy, external environmental factors likely degrade the model accuracy. Among these factors, moisture has the greatest effect on soil spectra. The external parameter orthogonalization (EPO) algorithm in combination with the Chinese soil spectroscopic database (Dataset A, 1566 samples) was investigated to eliminate the interference of the external parameters for SOC estimation. Two different methods of EPO development, namely, laboratory-rewetting archive soil samples and field-collecting actual moist samples, were compared to balance model performance and analytical cost. Memory-based learning (MBL), a local modeling technique, was introduced to compare with partial least square (PLS), a global modeling method. A total of 250 soil samples from Central China were collected. Of these samples, 120 dry ground samples (Dataset B) were rewetted to different moisture levels to develop EPO P1 matrix. Seventy samples (Dataset C) containing field-moist intact and laboratory dry ground soils were used to establish EPO P2 matrix. The remaining 60 samples (Dataset D) also containing field-moist intact and laboratory dry ground soils were employed to validate the spectral models developed based on Dataset A. Results showed that EPO could correct the effect of external factors on soil spectra. For PLS, the validation statistics were as follows: no correction, validation R2 = 0.02; P1 correction, validation R2 = 0.56; and P2 correction, validation R2 = 0.57. For MBL, the validation results were as follows: no correction, validation R2 = 0.06; P1 correction, validation R2 = 0.65; and P2 correction, validation R2 = 0.69. The P2 consistently yielded better results than P1 did but simultaneously increased the sampling time and economic cost. The use of the P1 matrix and the MBL algorithm was recommended because it could reduce the cost of establishing in situ models for SOC.

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Section: Practical Implicationsmentioning

confidence: 68%

Comparing Two Different Development Methods of External Parameter Orthogonalization for Estimating Organic Carbon from Field-Moist Intact Soils by Reflectance Spectroscopy

Hong

Chen³

et al. 2022

Remote Sensing

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“…Mirás-Avalos et al [33] observed a proportionally lower prediction accuracy of kriging with a larger distance from the nearest soil sample, resulting in unrealistic smoothing due to extrapolation, which is exaggerated for agricultural parcels with non-regular shapes. Reduced accuracy of conventional interpolation methods with a larger heterogeneity in soil types [33] and ineffective stratification [36] are the additional components which indicate a necessity for improving soil prediction performed by conventional interpolation methods [37]. The importance of selecting the optimal interpolation method and its parameters for fertilization in precision agriculture is manifested by avoiding poor agricultural practices and reducing the consumption of mineral and/or organic fertilizers.…”

Section: Conventional Approach To Fertilization In Precision Agriculturementioning

confidence: 99%

“…Among the variety of statistical indicators of the interpolation accuracy, the coefficient of determination (R 2 ) and the root-mean-square error (RMSE) are two of the most commonly used values [37], also enabling complementary accuracy assessment [52]. For the purpose of the accuracy assessment of predicted soil properties used in fertilization in precision agriculture, these are calculated according to Equations ( 2) and (3) [47]:…”

Section: Conventional Approach To Fertilization In Precision Agriculturementioning

confidence: 99%

“…Despite the same spatial resolution of the P 2 O 5 and K 2 O rasters produced by the conventional and modern approaches, modern machine learning methods have resulted in much less smooth areas, retaining specific local information about field conditions, which are a backbone for precision agriculture [7]. Chen et al [37] noted the improved spatial resolution as one of the key advantages of the modern approach over the conventional interpolation methods, alongside improved prediction accuracy and time-and cost-efficiency. A total of twelve relevant covariates for the P2O5 and K2O prediction used as the basis of the modern prediction approach are presented in Table 4.…”

Section: A Representative Overview Of Modern and Conventional Approac...mentioning

confidence: 99%

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The Role of Remote Sensing Data and Methods in a Modern Approach to Fertilization in Precision Agriculture

2022

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The precision fertilization system is the basis for upgrading conventional intensive agricultural production, while achieving both high and quality yields and minimizing the negative impacts on the environment. This research aims to present the application of both conventional and modern prediction methods in precision fertilization by integrating agronomic components with the spatial component of interpolation and machine learning. While conventional methods were a cornerstone of soil prediction in the past decades, new challenges to process larger and more complex data have reduced their viability in the present. Their disadvantages of lower prediction accuracy, lack of robustness regarding the properties of input soil sample values and requirements for extensive cost- and time-expensive soil sampling were addressed. Specific conventional (ordinary kriging, inverse distance weighted) and modern machine learning methods (random forest, support vector machine, artificial neural networks, decision trees) were evaluated according to their popularity in relevant studies indexed in the Web of Science Core Collection over the past decade. As a shift towards increased prediction accuracy and computational efficiency, an overview of state-of-the-art remote sensing methods for improving precise fertilization was completed, with the accent on open-data and global satellite missions. State-of-the-art remote sensing techniques allowed hybrid interpolation to predict the sampled data supported by remote sensing data such as high-resolution multispectral, thermal and radar satellite or unmanned aerial vehicle (UAV)-based imagery in the analyzed studies. The representative overview of conventional and modern approaches to precision fertilization was performed based on 121 samples with phosphorous pentoxide (P2O5) and potassium oxide (K2O) in a common agricultural parcel in Croatia. It visually and quantitatively confirmed the superior prediction accuracy and retained local heterogeneity of the modern approach. The research concludes that remote sensing data and methods have a significant role in improving fertilization in precision agriculture today and will be increasingly important in the future.

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“…Similarly, Gautam et al [35] employed ensemble machine learning models to estimate the baseline and dynamics of topsoil carbon stocks at a spatial resolution of 100 m in the United States and showed a total loss of 1.80 Pg carbon under the high-emission scenario by 2100. However, previous studies using the data-driven models for SOC stock prediction mainly focused on the effects of climate and land-use change, and until recently, the ability of machine learning-based data-driven models to detect the salinity-induced SOC dynamics has not been addressed, especially in inland regions [36,37].…”

Section: Introductionmentioning

confidence: 99%

The Role of Soil Salinization in Shaping the Spatio-Temporal Patterns of Soil Organic Carbon Stock

Zhang

Liu

et al. 2022

Remote Sensing

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Soil salinization is closely related to land degradation, and it is supposed to exert a significant negative effect on soil organic carbon (SOC) stock dynamics. This effect and its mechanism have been examined at site and transect scales in previous studies while over a large spatial extent, the salinity-induced changes in SOC stock over space and time have been less quantified, especially by machine learning and remote sensing techniques. The main focus of this study is to answer the following question: to what extent can soil salinity exert an additional effect on SOC stock over time at a larger spatial scale? Thus, we employed the extreme gradient boosting models (XGBoost) combined with field site-level measurements from 433 sites and 41 static and time-varying environmental covariates to construct methods capable of quantifying the salinity-induced SOC changes in a typical inland river basin of China between the 1990s and 2020s. Results showed that the XGBoost models performed well in predicting the soil electrical conductivity (EC) and SOC stock at 0–20 cm, with the R2 value reaching 0.85 and 0.81, respectively. SOC stock was found to vary significantly with increasing soil salinity following an exponential decay function (R2 = 0.27), and salinity sensitivity analysis showed that soils in oasis were expected to experience the largest carbon loss (−137.78 g m−2), which was about 4.84, 14.37, and 25.95 times higher than that in the saline, bare, and sandy land, respectively, if the soil salinity increased by 100%. In addition, the decrease in the soil salinity (−0.32 dS m−1) from the 1990s to the 2020s was estimated to enhance the SOC stock by 0.015 kg m−2, which contributed an additional 10% increase to the total SOC stock enhancement. Overall, the proposed methods can be applied for quantification of the direction and size of the salinity effect on SOC stock changes in other salt-affected regions. Our results also suggest that the role of soil salinization should not be neglected in SOC changes projection, and soil salinization control measures should be further taken into practice to enhance soil carbon sequestration in arid inland river basins.

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Digital mapping of GlobalSoilMap soil properties at a broad scale: A review

Cited by 224 publications

References 212 publications

Comparing Two Different Development Methods of External Parameter Orthogonalization for Estimating Organic Carbon from Field-Moist Intact Soils by Reflectance Spectroscopy

Comparing Two Different Development Methods of External Parameter Orthogonalization for Estimating Organic Carbon from Field-Moist Intact Soils by Reflectance Spectroscopy

The Role of Remote Sensing Data and Methods in a Modern Approach to Fertilization in Precision Agriculture

The Role of Soil Salinization in Shaping the Spatio-Temporal Patterns of Soil Organic Carbon Stock

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