Forensic soil source identification: comparing matching by color, vis-NIR spectroscopy and easily-measured physio-chemical properties

Key factors influencing arsenic phytotoxicity thresholds in south China acidic soils

Arsenic (As) toxicity threshold values (TTVs) for plants are fundamental to both establishing regional As reference values in soil and performing risk assessment. However, TTVs vary with plant species and soil types. In this study, a hydroponic experiment with 16 plant species was conducted to screen the most As-sensitive plant species. The results showed that the EC20 (available As concentration at which shoot biomass or height is inhibited by 20%) values were 1.38-104.4 mg L-1 for shoot height and 0.24-42.87 mg L-1 for shoot fresh biomass. Rice was more sensitive to As toxicity than the other species. Therefore, it was chosen as the ecological receptor in the pot experiment on As phytotoxicity in nine types of soils collected from Fujian Province in South China. The EC10 and EC20 with respect to rice shoot height were 3.72-29.11 mg kg-1 and 7.12-45.60 mg kg-1, respectively. Stepwise regression analysis indicated that free iron oxide concentration is the major factor that affects As bioavailability in soil, and ECx (x = 10, 20, and 50) of soil available As for shoot height was positively related to free iron oxide concentration in soil. In addition, soil cation exchange capacity, clay (<0.002 mm) content, and exchangeable magnesium content are also important factors influencing As phytotoxicity in acidic soils. The regression models can be used to predict As phytotoxicity in acidic soils.

The choice of spectral similarity algorithms influences suspected soil sample provenance

Similarity algorithms are commonly used in soil forensic applications to help identify similar samples from an existing reference library as possible source locations of unknown target samples. These algorithms are well-suited to compare soil spectra. However, different similarity algorithms may lead to different clusters of similar samples, and thus different strengths of evidence in forensic investigations. To quantify this, we conducted a study to evaluate the influence of seven similarity algorithms on soil provenance, using as a sample set a soil spectral library consisting of 280 soil profiles from Anhui Province, China. This library includes three spatial scales of datasets: provincial (DS_p), county (DS_c) and field (DS_f). A set of ten samples covering a wide range of spectra variations were selected from the DS_f dataset as the "unknown" samples, with the remaining being used as the reference samples. This study aimed to: (1) evaluate how several commonly-used similarity algorithms, namely Euclidean distance (ED), Mahalanobis distance (MD), Spectral angle mapper (SAM), and Spectral information divergence (SID), as well as variants of several of these measured in standardized principal component space computed from the spectra (ED_PCA, MD_PCA and SAM_PCA), influence the identification of the matched similar samples; (2) determine the overlap in sample selection between different similarity algorithms; (3) propose best practices for similarity algorithms applied to soil forensic analysis using spectroscopy. The use of different similarity algorithms did influence the selection of most similar samples. The similarity algorithms calculated in PC space (ED_PCA, MD_PCA and SAM_PCA) performed slightly better than their counterparts calculated in spectral space. Due to the availability of a detailed spectral library, regardless of the different similarity algorithms used, the matched most similar samples were all located close to the unknowns, mostly within 3 km, with one exception. That is, the varied choices of different similarity algorithms hardly influenced the conclusion of soil provenance in this case. In general, MD_PCA, SAM and ED were the best similarity algorithms overall. However, since there was no single best algorithms for all cases, we recommend the joint use of MD_PCA, SAM and ED as an ensemble. Indications of possible sample provenance from these similarity measured can be useful evidence to complement evidence from other methods in a forensic investigation.

Sentinel-2 Recognition of Uncovered and Plastic Covered Agricultural Soil

Medium resolution satellite data, such as Sentinel-2 of the Copernicus programme, offer great new opportunities for the agricultural sector, and provide insights on soil surface characteristics and their management. Soil monitoring requires a high-quality dataset of uncovered and plastic covered agricultural soil. We developed a methodology to identify uncovered soil pixels in agricultural parcels during seedbed preparation and considered the impacts of clouds and shadows, vegetation cover, and artificial covers, such as those of greenhouses and plastic mulch films. We preserved the spatial and temporal integrity of parcels in the process and analysed spectral anomalies and their sources. The approach is based on freely available tools, namely Google Earth Engine and R Programming packages. We tested the methodology on the northern region of Belgium, which is characterised by small, fragmented parcels. We selected a period between mid-April to end-May, when active agricultural management practices leave the soil bare in preparation for the main cropping season. The spectral angle mapper was used to identify soil covered by non-plastic greenhouses or temporary soil covers, such as plastic mulch films. The effect of underlying soil on temporary covers was considered. The retrogressive plastic greenhouse index was used for detecting plastic greenhouses. The result was a high quality dataset of potential bare uncovered agricultural soil that allows further soil surface characterisation. This offered an improved understanding of the use of artificial covers, their spatial distribution, and their corresponding crops during the considered period. Artificial covers occurred most frequently in maize parcels. The approach resulted in precision values exceeding 0.9 for the detection of temporary covers and non-plastic greenhouses and a sensitivity value exceeding 0.95 for non-plastic and plastic greenhouses.