Mapping Soil Organic Matter Research Articles

A nature‐inclusive future with healthy soils? Mapping soil organic matter in 2050 in the Netherlands

AbstractNature‐inclusive scenarios of the future can help address numerous societal challenges related to soil health. As nature‐inclusive scenarios imply sustainable management of natural systems and resources, land use and soil health are assumed to be mutually beneficial in such scenarios. However, the interplay between nature‐inclusive land use scenarios and soil health has never been modelled using digital soil mapping. We predicted soil organic matter (SOM), an important indicator of soil health, in 2050, based on a recently developed nature‐inclusive scenario and machine learning in 3D space and time in the Netherlands. By deriving dynamic covariates related to land use and the occurrence of peat for 2050, we predicted SOM and its uncertainty in 2050 and assessed SOM changes between 2022 and 2050 from 0 to 2 m depth at 25 m resolution. We found little changes in the majority of mineral soils. However, SOM decreases of up to 5% were predicted in grasslands used for animal‐based production systems in 2022, which transitioned into croplands for plant‐based production systems by 2050. Although increases up to 25% SOM were predicted between 0 and 40 cm depth in rewetted peatlands, even larger decreases, on reclaimed land even surpassing 25% SOM, were predicted on non‐rewetted land in peat layers below 40 cm depth. There were several limitations to our approach, mostly due to predicting future trends based on historic data. Furthermore, nuanced nature‐inclusive practices, such as the adoption of agroecological farming methods, were too complex to incorporate in the model and would likely affect SOM spatial variability. Nonetheless, 3D‐mapping of SOM in 2050 created new insights and raised important questions related to soil health behind nature‐inclusive scenarios. Using machine learning explicit in 3D space and time to predict the impact of future scenarios on soil health is a useful tool for facilitating societal discussion, aiding policy making and promoting transformative change.

Mapping soil organic matter in cultivated land based on multi-year composite images on monthly time scales

Rapid and accurate acquisition of soil organic matter (SOM) information in cultivated land is important for sustainable agricultural development and carbon balance management. This study proposed a novel approach to predict SOM with high accuracy using multiyear synthetic remote sensing variables on a monthly scale. We obtained 12 monthly synthetic Sentinel-2 images covering the study area from 2016 to 2021 through the Google Earth Engine (GEE) platform, and reflectance bands and vegetation indices were extracted from these composite images. Then the random forest (RF), support vector machine (SVM) and gradient boosting regression tree (GBRT) models were tested to investigate the difference in SOM prediction accuracy under different combinations of monthly synthetic variables. Results showed that firstly, all monthly synthetic spectral bands of Sentinel-2 showed a significant correlation with SOM (P<0.05) for the months of January, March, April, October, and November. Secondly, in terms of single-monthly composite variables, the prediction accuracy was relatively poor, with the highest R2 value of 0.36 being observed in January. When monthly synthetic environmental variables were grouped in accordance with the four quarters of the year, the first quarter and the fourth quarter showed good performance, and any combination of three quarters was similar in estimation accuracy. The overall best performance was observed when all monthly synthetic variables were incorporated into the models. Thirdly, among the three models compared, the RF model was consistently more accurate than the SVM and GBRT models, achieving an R2 value of 0.56. Except for band 12 in December, the importance of the remaining bands did not exhibit significant differences. This research offers a new attempt to map SOM with high accuracy and fine spatial resolution based on monthly synthetic Sentinel-2 images.

Mapping soil organic matter and identifying potential controls in the farmland of Southern China: Integration of multi‐source data, machine learning and geostatistics

AbstractSoil organic matter (SOM) plays a critical role in terrestrial ecosystem functioning and is closely related to many global issues like soil fertility, soil health and climate regulation. Therefore, obtaining accurate information on the spatial distribution of SOM and its potential controlling factors is of global interest. However, this remains a great challenge since SOM is affected by numerous natural and anthropogenic factors and usually showed strong heterogeneity. In this study, we collected a total of 16,580 surface soil (0–20 cm) samples from the farmland throughout Jiangxi Province. And the Random Forest (RF), Cubist and gradient‐boosted models were compared and used to define the factor which is most associated with SOM. Then the ordinary kriging (OK) and machine learning‐ordinary co‐kriging (ML‐COK) were used to map SOM. We found that on average, 30.86 g kg−1 SOM was present in farmland soil of Jiangxi Province. Anthropogenic activities strongly affected SOM level, with five of the top 10 most important factors are anthropogenic related. The straw return amount was proved to have the largest importance (31.46%) for modelling SOM and a significant (p &lt; 0.001) positive relationship between SOM content and the amount of straw returned to farmland was detected. Additionally, returning straw improved crop production. Soil derived from the Quaternary Subred Sand has the highest SOM content (37.82 g kg−1). Crop rotation also improved SOM content and the rice‐bean rotation system has the highest SOM content (34.27 g kg−1). With the best performance, the RF algorithm (R2 = 0.49, RMSE = 6.77 g kg−1) was selected to identify the primary control of SOM and integrated with COK, which we termed as ML‐COK, to map the SOM in the farmland of Jiangxi Province. ML‐COK outperformed OK method for mapping the SOM in farmland of Jiangxi Province with R2 of 0.351 and Lin's concordance correlation coefficient of 0.549. Farmland distributed in the central part of the province had high SOM content. In contrast, farmland in the north, south and east parts had relatively low SOM. Our study offers new insight for mapping soil properties, identifying potential factors driving variation in SOM, and also provides valuable information for making more reasonable and environmentally friendly farmland management measures.

High-Precision Mapping of Soil Organic Matter Based on UAV Imagery Using Machine Learning Algorithms

Soil organic matter (SOM) is a critical indicator of soil nutrient levels, and the precise mapping of its spatial distribution through remote sensing is essential for soil regulation, precise fertilization, and scientific management and protection. This information can offer decision support to agricultural management departments and various agricultural producers. In this paper, two new soil indices, NLIrededge2 and GDVIrededge2, were proposed based on the sensitive spectral response characteristics of SOM in Northeast China. Nine parameters suitable for SOM mapping and modeling were determined using the competitive adaptive reweighted sampling (CARS) method, combined with spectrum reflectance, mathematical transformations of reflectance, vegetation indices, and so on. Then, utilizing unmanned aerial vehicle (UAV)-based multispectral images with centimeter-level resolution, a random forest machine learning algorithm was used to construct the inversion model of SOM and mapping SOM in the study area. The results showed that the random forest algorithm performed best for estimating SOM (R2 = 0.91, RMSE = 0.95, MBE = 0.49, and RPIQ = 3.25) when compared with other machine learning algorithms such as support vector regression (SVR), elastic net, Bayesian ridge, and linear regression. The findings indicated a negative correlation between SOM content and altitude. The study concluded that the SOM modeling and mapping results could meet the needs of farmers to obtain basic information and provide a reference for UAVs to monitor SOM.

Application of generalized linear geostatistical model for regional soil organic matter mapping: The effect of sampling density

The generalized linear geostatistical model (GLGM) is a formal approach of regression kriging that would be advantageous over commonly used modelling approaches for digital soil mapping (DSM). However, it has not been well explored in the literature, due to heavy computation. This study evaluates such formal approach for mapping soil organic matter at a regional scale (179,700 km2). We hypothesized that GLGM would be a better approach than other approaches as it can model nonlinear relationships and spatially consider the residuals. However, the accuracy would depend on sampling density. We compared GLGM with multiple linear regression (MLR), ordinary kriging (OK), regression kriging (RK), random forest (RF), generalized linear mixture model (GLMM), and generalized additive model (GAM). The effect of sampling density on the performance was also investigated by fitting the models based on resampling the samples with a series of sizes ranging from 100 to 1200. Results showed that GLGM generally improved the accuracy of DSM, compared with MLR, OK, RF, GLMM, and GAM, especially for large sample sizes, although the improvement was not significant. In a few cases, GLGM outperformed RK. The GLGM modelling and its prediction were largely influenced by sampling densities. Given small sample sizes, GLGM was unstable and the parameters were highly variable depending on the modelling realizations. Other influencing factors include linear and smooth correlations between soil and environmental covariates, spatial autocorrelation of the residuals, compatibility of spatial scales of soil samples and environmental covariates, and the scale of soil variation. For these factors, researchers with a more dense soil sampling are needed in future to explore the benefits of GLGM as a hybrid model for spatial prediction of soil.

Mapping soil organic matter content using Sentinel-2 synthetic images at different time intervals in Northeast China

ABSTRACT Mapping soil organic matter (SOM) content has become an important application of digital soil mapping. In this study, we processed all Sentinel-2 images covering the bare-soil period (March to June) in Northeast China from 2019 to 2022 and integrated the observation results into synthetic materials with four defined time intervals (10, 15, 20, and 30 d). Then, we used synthetic images corresponding to different time periods to conduct SOM mapping and determine the optimal time interval and time period before finally assessing the impacts of adding environmental covariates. The results showed the following: (1) in SOM mapping, the highest accuracy was obtained using day-of-year (DOY) 120 to 140 synthetic images with 20 d time intervals, as well as with different time intervals, ranked as follows: 20 d > 30 d > 15 d > 10 d; (2) when using synthetic images at different time intervals to predict SOM, the best time period for predicting SOM was always within May; and (3) adding environmental covariates effectively improved the SOM mapping performance, and the multiyear average temperature was the most important factor. In general, our results demonstrated the valuable potential of SOM mapping using multiyear synthetic imagery, thereby allowing detailed mapping of large areas of cultivated soil.

Mapping Soil Organic Matter Content during the Bare Soil Period by Using Satellite Data and an Improved Deep Learning Network

Soil function degradation has impaired global work in the implementation of sustainable development goals (SDGs), and soil organic matter (SOM) is a basic and the most important indicator. The deep learning neural network (i.e., DL network) has become a popular tool for mapping SOM content at a regional scale. However, outlier sample data caused by environmental factors (e.g., moisture and vegetation) and uncertain noise (e.g., random noise and instability effects) have interfered with the determination of the function mapping relationship between target soil properties and spectral features, leading to DL networks with low generalization capability. Therefore, we introduced a spatial association module into a deep neural network to remove outlier sample data in order to construct an optimal sample set for calibrating an improved deep learning (IDL) network for SOM mapping. A total of 707 soil samples and a Sentinel-2B multispectral image were acquired during the bare soil period in Weibei, China. The variable importance in the projection approach was used to select the SOM-responsive spectral features for model inputs. Measured SOM contents were taken as the dependent variable, and the IDL network was constructed and applied to map SOM at the regional scale. The results showed that Band 11 was the most important band for SOM prediction. The band difference transformation method was able to integrate multiple-band information and enhance the absorption signal of SOM. The optimal SOM-responsive spectral features included B11, B1–B11, B2−B11, B3−B11, B4−B11, and B1−B12. The IDL network exhibited better performance (R2 = 0.92; RPIQ = 4.57) regarding SOM estimation compared with the DL model performance (R2 = 0.84; RPIQ = 2.84), being improved by 9.52% (for R2) and 60.92% (for RPIQ). After introducing the spatial association module, the DL network generalization capability was enhanced. SOM distribution showed a high-value (&gt;20 g kg−1) area in the south, and a low-value (&lt;6 g kg−1) area in the north of the study area (the area affected by seawater intrusion). These results provide a strategy based on an IDL network and satellite data for effectively and accurately mapping SOM at the regional scale.

Mapping Soil Organic Matter Content Based on Feature Band Selection with ZY1-02D Hyperspectral Satellite Data in the Agricultural Region

Soil organic matter (SOM) is an essential nutrient for crop growth and development. Hyperspectral satellite images with comprehensive spectral band coverage and high spectral resolution can be used to estimate and draw a spatial distribution map of SOM content in the region, which can provide a scientific management basis for precision agriculture. This study takes Xinzheng City, Henan Province’s agricultural area, as the research object. Based on ZY1-02D hyperspectral satellite image data, the first derivative of reflectance (FDR) was processed on the original reflectance (OR). The SOM characteristic spectral bands were extracted using the correlation coefficient (CC) and least absolute shrinkage and selection operator (Lasso) methods. The prediction model of SOM content was established by multiple linear regression (MLR), partial least squares regression (PLSR), and random forest (RF) algorithms. The results showed that: (1) FDR processing can enhance SOM spectral features and reduce noise; (2) the Lasso feature band extraction method can reduce the model’s input variables and raise the estimation precision; (3) the SOM content prediction ability of the RF model was significantly better than that of the MLR and PLSR models. The FDR-Lasso-RF model was the best SOM content prediction model, and the validation set R2 = 0.921, MAEV = 0.512 g/kg, RMSEV = 0.645 g/kg; (4) compared with laboratory hyperspectral data-SOM prediction methods, hyperspectral satellite data can achieve accurate, rapid, and large-scale SOM content prediction and mapping. This study provides an efficient, accurate, and feasible method for predicting and mapping SOM content in an agricultural region.

Using PRISMA Hyperspectral Satellite Imagery and GIS Approaches for Soil Fertility Mapping (FertiMap) in Northern Morocco

Quickly and correctly mapping soil nutrients significantly impact accurate fertilization, food security, soil productivity, and sustainable agricultural development. We evaluated the potential of the new PRISMA hyperspectral sensor for mapping soil organic matter (SOM), available soil phosphorus (P2O5), and potassium (K2O) content over a cultivated area in Khouribga, northern Morocco. These soil nutrients were estimated using (i) the random forest (RF) algorithm based on feature selection methods, including feature subset evaluation and feature ranking methods belonging to three categories (i.e., filter, wrapper, and embedded techniques), and (ii) 107 soil samples taken from the study area. The results show that the RF-embedded method produced better predictive accuracy compared with the filter and wrapper methods. The model for SOM showed moderate accuracy (Rval2 = 0.5, RMSEP = 0.43%, and RPIQ = 2.02), whereas that for soil P2O5 and K2O exhibited low efficiency (Rval2 = 0.26 and 0.36, RMSEP = 51.07 and 182.31 ppm, RPIQ = 0.65 and 1.16, respectively). The interpolation of RF-residuals by ordinary kriging (OK) methods reached the highest predictive results for SOM (Rval2 = 0.69, RMSEP = 0.34%, and RPIQ = 2.56), soil P2O5 (Rval2 = 0.44, RMSEP = 44.10 ppm, and RPIQ = 0.75), and soil K2O (Rval2 = 0.51, RMSEP = 159.29 ppm, and RPIQ = 1.34), representing the best fitting ability between the hyperspectral data and soil nutrients. The result maps provide a spatially continuous surface mapping of the soil landscape, conforming to the pedological substratum. Finally, the hyperspectral remote sensing imagery can provide a new way for modeling and mapping soil fertility, as well as the ability to diagnose nutrient deficiencies.

A comparison of ordinary kriging, regression kriging and REML-EBLUP for mapping soil organic matter on a regional scale

A comparison of ordinary kriging, regression kriging and REML-EBLUP for mapping soil organic matter on a regional scale

Sample design optimization for soil mapping using improved artificial neural networks and simulated annealing

Optimization of the sampling design is significant for generating accurate soil maps based on samples. Simulated annealing (SA) with the mean squared prediction error (MSE) as objective function has been proven as an effective method for sampling optimization with prior samples for the MSE calculation. In this study, an improved artificial neural network (the improved ANN) was developed firstly and then utilized to calculate the objective function of SA for sampling optimization (named as the improved ANN_SA). The proposed approach was evaluated to generate optimized samples with a series of samples sizes (from 10 to 500 points) for mapping soil organic matter (SOM) content from the existing 5054 samples in a study area with 1067 km2 in Jiangsu Province, China. A commonly-used sampling optimization using SA with the objective function calculated by regression kriging (named as RK_SA) was taken as a reference method. The two sampling optimization approaches were compared in mapping SOM using three prediction methods, the improved ANN, regression kriging (RK) and random forest. The results showed that the proposed sampling optimization approach generally achieved more accurate prediction over different sampling sizes. The maximum improvement of prediction accuracy by using the proposed sampling strategy vs. the reference strategy was 12.5%, 53.6%, and 15.5% when using the improved ANN, RK, and random forest as soil mapping methods, respectively. Moreover, the improved ANN and random forest generated more accurate soil predictions than RK with the proposed sampling optimization approach. The superiority of the proposed sampling optimization was more obvious in low sampling densities (smaller than 0.3 points/km2). We conclude that the improved ANN_SA sampling is a potential effective sampling optimization approach, and machine learning is a promising method for soil mapping when using this sampling approach.

Comparison of feature selection methods for mapping soil organic matter in subtropical restored forests

• Filter, wrapper, embedded and ensemble feature selection approaches (FS) were evaluated. • The proposed ensemble FS incorporates ten individual selectors to identify top significant variables. • Ensemble FS produced the highest predictive variable subset for mapping SOM. • XGBoost outperformed RF in mapping SOM in subtropical restored forests. Mapping Soil organic matter (SOM) over a complex forest landscape is challenging due to the difficulty in selecting the most insightful variables from high-dimensional datasets in the recent explosion of geospatial-data. Feature selection (FS) is necessary to reduce data redundancy and noise as well as to achieve more reliable SOM spatial predictions. However, it is still unclear that which is most effective among various FS methods in mapping SOM. Therefore, four types of FS approaches (i.e., filter, wrapper, embedded and ensemble) were adopted to generate optimum variable subsets from an original variable dataset of 60 candidates, respectively, for mapping SOM of restored forest land in a typical subtropical region of southern China. The most used methods for each type of FS approaches were selected in this study, including three filters (Chi-square, InfoGain and pearson correlation analysis), three wrappers (genetic algorithm, simulated annealing algorithm and support vector machine-recursive feature elimination) and three embedded methods (Boruta, random forest (RF) and extreme gradient boosting (XGBoost)), as well as an ensemble method (robust rank aggreg algorithm (RRA)). Meanwhile, the RF and XGBoost models were applied with a 10-fold cross-validation method to compare the relative advantages of the different FS methods in SOM mapping, by utilizing the correlation coefficients R 2 between observed and predicted values and predicting errors of root mean square error (RMSE). The results show that the SOM prediction accuracies with optimized variable subsets generated by the different FS methods are better than those with full variables, yet the improvements of prediction performance are different among the four types of FS approaches. The ensemble method (RRA) is superior to the other three types of approaches with an average RMSE reduction of 9.16% comparing to that without using FS methods, followed by wrapper and embedded methods which obtained the average RMSE reduction by 7.81%, 7.32%, respectively, and the filter methods are the weakest in the RMSE reduction with slight decreases of 4.32%. The XGBoost model achieved a better performance in predicting SOM than the RF model regardless of input variables, and the XGBoost model combined with RRA FS method shows the greatest potential to map SOM in the restored forest land. This study provides a reference for obtaining more parsimonious and robust variable sets from the available big geo-data freely for soil mapping in other areas.

Mapping Soil Organic Matter and Analyzing the Prediction Accuracy of Typical Cropland Soil Types on the Northern Songnen Plain

Soil organic matter (SOM) plays a critical role in agroecosystems and the terrestrial carbon cycle. Thus, accurately mapping SOM promotes sustainable agriculture and estimations of soil carbon pools. However, few studies have analyzed the changing trends in multi-period SOM prediction accuracies for single cropland soil types and mapped their spatial SOM patterns. Using time series 7 MOD09A1 images during the bare soil period, we combined the pixel dates of training samples and precipitation data to explore the variation in SOM accuracy for two typical cropland soil types. The advantage of using single soil type data versus the total dataset was evaluated, and SOM maps were drawn for the northern Songnen Plain. When almost no precipitation occurred on or near the optimal pixel date, the accuracies increased, and vice versa. SOM models of the two soil types achieved a lower root mean squared error (RMSE = 0.55%, 0.79%) and mean absolute error (MAE = 0.39%, 0.58%) and a higher coefficient of determination (R2 = 0.65, 0.75) than the model using the total dataset and resulted in a mean relative improvement (RI) of 30.21%. The SOM decreased from northeast to southwest. The results provide reference data for the accurate management of cultivated soil and determining carbon sequestration.

High Spatial Resolution Topsoil Organic Matter Content Mapping Across Desertified Land in Northern China

Soil organic matter (SOM) content is an effective indicator of desertification; thus, monitoring its spatial‒temporal changes on a large scale is important for combating desertification. However, mapping SOM content in desertified land is challenging owing to the heterogeneous landscape, relatively low SOM content and vegetation coverage. Here, we modeled the SOM content in topsoil (0–20 cm) of desertified land in northern China by employing a high spatial resolution dataset and machine learning methods, with an emphasis on quarterly green and non-photosynthetic vegetation information, based on the Google Earth Engine (GEE). The results show: 1) the machine learning model performed better than the traditional multiple linear regression model (MLR) for SOM content estimation, and the Random Forest (RF) model was more accurate than the Support Vector Machine (SVM) model; 2) the quarterly information regarding green vegetation and non-photosynthetic were identified as key covariates for estimating the SOM content in desertified land, and an obvious improvement could be observed after simultaneously combining the Dead Fuel Index (DFI) and Normalized Difference Vegetation Index (NDVI) of the four quarters (R2 increased by 0.06, the root mean square error decreased by 0.05, the ratio of prediction deviation increased by 0.2, and the ratio of performance to interquartile distance increased by 0.5). In particular, the effects of the DFI in Q1 (the first quarter) and Q2 (the second quarter) on estimating low SOM content (&amp;lt;1%) were identified; finally, a timely (2019) and high spatial resolution (30 m) SOM content map for the desertified land in northern China was drawn which shows obvious advantages over existing SOM products, thus providing key data support for monitoring and combating desertification.

The stability of calibration model in measuring and mapping soil organic matter in a dry climatic area

The increase of atmospheric CO2 concentration from soil organic matter (SOM) decomposition may contribute to the global warming and climate change. So, sequestering this greenhouse gas into SOM may be used to mitigate climate change. However, tedious procedures in measuring and mapping SOM need to be replaced with a method which works based on the reliability of calibration model developed. This research aimed to test the reliability of the calibration model that was built from a separate soil sample group to be used to measure and map SOM on other validation soil sample group, in the mostly dry climatic area of Kayangan Sub-district, North Lombok Indonesia. For this purpose, 300 soil samples were collected from the area using grid method, which were then dried, ground, sieved, analysed for SOM content using the Walkley and Black method, and scanned using Near Infrared Spectroscopy. The model built using calibration sample group was able to reliably measure and map the SOM content of the spectral data collected from the validation sample set. This is shown by the coefficient of determination (R2 V = 0.79), root mean square error (RMSEV = 0.246%) and the ratio prediction to deviation (RPDV = 2.09). SOM maps generated from both laboratory and near infrared method can show very low, low and medium SOM content. These maps can be further used as a reference for applying organic fertilizers, avoiding excessive use of fertilizers, and monitoring soil carbon sequestration in mitigating climate change.

A comparison of importance of modelling method and sample size for mapping soil organic matter in Guangdong, China

Digital soil mapping (DSM) is the most widely used method for producing spatial information of soil organic matter (SOM). Accuracy of the information is generally determined by modelling methods and sample sizes used for DSM. However, different studies present different importance of modelling method and sample size on accuracy of DSM, while they do not explore various combinations of modelling method and sample size. Based on the studies, it is supposed that there exists an optimal combination of modelling method and sample size for producing information of SOM accurately and economically. With SOM data of 1861 soil samples collected in Guangdong, China, the present study first assessed importance of modelling method and sample size and then examined if an optimal combination of modelling method and sample size existed for the area. Six modelling methods were explored, while 12 sample sizes were used, ranging from 100 to 1200 with an interval of 100. For each size, 10 repeated samples were randomly taken from a data of 1311 samples which were randomly selected from all the 1861 soil samples based on the probability distribution of the SOM data. The results showed that, for small sample sizes, the modelling methods have a greater impact on accuracy of DSM. However, for large sample sizes, e.g., more than 1000, the sample sizes have a much greater impact. Due to the varying importance of modelling method and sample size, there exists an optimal combination of modelling method and sample size for spatial prediction of SOM in the area, i.e., the combination of regression kriging and a sample size of 800. Thus, for economically producing detailed and accurate information on spatial distribution of SOM, it is recommended that a series of modelling methods and sample sizes are tried to identify an optimal combination of modelling method and sample size.

Estimating and mapping of soil organic matter content in a typical river basin of the Qinghai-Tibet Plateau

Spectroscopy is a fast, non-destructive, and cheap method, which has been widely used in the estimation of soil organic matter (SOM) concentration. This study presented a methodology to estimate and map SOM content by crop canopy reflectance spectra combining with land parameters in the Yarlung Zangbo River (YZR) basin. The reflectance spectra of the oat canopy were collected in the field, and then were processed by savitzky-golay filtering (S-G), continuous removal (CR), and first derivative of reflectivity (FDR). The principal components were extracted from the processed spectral data. Land parameters such as elevation, slope, NDVI, and land surface temperature (LST) were selected to establish 18 models to estimate SOM content. The results showed that the estimation accuracy of SOM content could be improved by combining crop canopy reflectance spectra with land parameters. FDR-BPNN model with land parameters had the best estimation effect (R2 = 0.973, MAE = 0.847 g·kg−1).

A trustworthiness indicator to select sample points for the individual predictive soil mapping method (iPSM)

Observations of soil at georeferenced sample points are the indispensable input to digital soil mapping (DSM) models that relate soil properties and types to the soil-forming environment. Many existing DSM methods require soil samples to be collected following a pre-defined sampling pattern. This requirement is often not met due to various operational reasons. The individual sample-based predictive soil mapping (iPSM) method has been developed to avoid this requirement. However, the prediction accuracy of iPSM depends heavily on the reliability of the known sample points used in prediction. Unreliable sample points are those with unreliable soil-environment relationship: the target soil property value and environmental covariate data are not correctly paired at the sample point location. Such unreliable sample points will lead to poorly constructed models and low prediction accuracy. This paper presents a new method to estimate the reliability of soil-environment relationship at each sample location and to evaluate the trustworthiness of prediction at each unvisited location. Under the assumption that sample points with similar environmental conditions have similar soil property values, the method first identifies the sample points that are environmentally similar to the sample point to be evaluated, then uses their agreement on the targeted soil property to evaluate the reliability of soil-environment relationship at the sample point. The effectiveness of the method was assessed in a case study located in Anhui Province in China to map soil organic matter content (SOM, %) in the topsoil. When the reliability threshold increased from 0.5 to 0.8, more unreliable sample points are excluded from prediction: 41% of total sample points were excluded when the threshold was set to 0.5, and 88% were excluded when the threshold was set to 0.8. As a result, less unknown area could be predicted – only nine validation points were predictable with the 0.8 reliability threshold. However, the prediction accuracy was improved: the root mean squared error, RMSE, decreased from 1.37% to 0.63%, and R2 increased from 0.21 to 0.98. Prediction trustworthiness at each unvisited location was also produced along with prediction accuracy, which was negatively related to the absolute prediction residuals. This study shows that the reliability of individual sample points is an important determinant of the prediction accuracy of the iPSM method. When applying iPSM method, an optimal trade-off between prediction accuracy and completeness of the predicted map needs to be found by adjusting the reliability threshold on the sample points used in prediction.

PEMETAAN BAHAN ORGANIK TANAH PADA SAWAH IRIGASI DAN TADAH HUJAN DI KECAMATAN TUREN, MALANG

Each land use has a different vegetation density and the litter input as a source of soil organic matter. Vegetation density index can be analyzed based on the NDVI equation using the GIS approach. This study aims to determine the effect of different land uses and NDVI on soil organic matter content and the mapping of soil organic matter content. This research was conducted from April to August 2019 in Turen District, Malang Regency. The survey method used for collecting data in the field (36 observation points) based on differences in landform, relief, slope, land use (irrigated and rainfed rice fields), and vegetation density index classes (low, medium, high). Soil samples were taken at 0-20 cm depth and analyzed for soil organic matter content. Data interpolation using IDW was used for mapping soil organic matter. The results showed that there was a very significant effect between differences in land use and NDVI class on the content of soil organic matter (p &lt;0.001). NDVI value gives an effect of 81.5% on soil organic matter content. The distribution of soil organic matter content is classified into 5 classes, which are very low, low, moderate, high and very high.

A representativeness heuristic for mitigating spatial bias in existing soil samples for digital soil mapping

Digital soil mapping (DSM) often relies on existing soil samples obtained from various sources. However, the spatial distribution of such soil samples can be biased, for example, towards areas of better accessibility. Such biased coverage over the geographic space (i.e., spatial bias) often leads to biased coverage of the soil samples over the environmental covariate space. As a result, spatial bias degrades the correlation or statistical relationship between samples and covariates in the study area and impedes DSM accuracy. This paper presents a representativeness heuristic for mitigating spatial bias in existing soil samples for improving DSM accuracy. The key idea of the heuristic was to define and quantify sample representativeness as the goodness-of-coverage of the soil samples over the environmental covariate space. Spatial bias was then mitigated by weighting the samples towards maximizing their representativeness. Determination of the sample weights was conceived as an optimization problem and accordingly the optimal weights were determined using a genetic algorithm. To evaluate the effectiveness of the representativeness heuristic, a case study of mapping soil organic matter (SOM) content using existing soil samples was conducted in Heshan study area, northeastern China. Results showed that weighting soil samples using the optimal weights determined from the representativeness heuristic improved SOM content mapping accuracy. Moreover, a positive relationship between sample representativeness and mapping accuracy was observed, suggesting sample representativeness is an effective indicator of mapping accuracy. Additionally, the determined optimal weights were informative of individual sample importance and thus can be used as guidance to filter existing soil samples to improve DSM accuracy.