Estimation of Soil Properties Using a Combination of Spectral and Scalar Sensor Data

Local modeling approaches for estimating soil properties in selected Indian soils using diffuse reflectance data over visible to near-infrared region

<p>There is a high demand for information about soil conditions in forests stands as it is crucial to ensure sustainable management, to maintain ecosystem services, to preserve timber production and establish proper pest management. Nowadays, the main drivers for changes in soil conditions are element input, forest conversion, subsoil liming and changing climate. These drivers influence nutrients and water availability and are challenging current site mapping methods. However, for impact assessment high-resolution and up-to-date information is needed. As laboratory analysis is time consuming and expensive, alternative approaches are preferred.</p><p>The project DIGI-Humus uses methods of reflectance spectroscopy in the visual and near-infrared-region of the electromagnetic spectrum for indirect measurement and prediction of physical and chemical soil properties in forest stands. For this purpose, spectral data were collected under laboratory conditions to build a database of forest soils. We used retained samples from Saxony soil survey, measuring both Oh and Ah horizons. To ensure data quality, we developed our own protocol based on literature review and self-conducted test measurements. The data has been used to successfully calibrate regression models based on different forest types and soil horizons to predict the soil parameters C and N content, C/N ratio and pH-value.</p><p>To improve model performance and test its generalization capability, the created library has been extended with new samples from a field campaign conducted in 2019 at an additional local test site. Using this data, the impact of adding new information to the modelling process and the robustness of the models could be evaluated.</p><p>The results of this research will be used to assess forest sites regarding nutrients availability, as basis for the development of site specific management strategies and to enhance and improve current methods of periodic site mapping of forest stands.</p>

Impact of cover cropping and landscape positions on nitrous oxide emissions in northeastern US agroecosystems

Abstract. Combining data collected in-field from multiple soil sensors has the potential to improve the efficiency and accuracy of soil property estimates. Optical diffuse reflectance spectroscopy (DRS) has been used to estimate many important soil properties, such as soil carbon, water content, and texture. Other common soil sensors include penetrometers that measure soil strength and apparent electrical conductivity (ECa) sensors. Previous field research has related these sensor measurements to soil properties such as bulk density, water content, and texture. A commercial instrument that can simultaneously collect reflectance spectra, ECa, and soil strength data is now available. The objective of this research was to relate laboratory-measured soil properties, including bulk density (BD), total organic carbon (TOC), water content (WC), and texture fractions to sensor data from this instrument. At four field sites in mid-Missouri, profile sensor measurements were obtained to 0.9 m depth, followed by collection of soil cores at each site for laboratory measurements. Using only DRS data, BD, TOC, and WC were not well-estimated (R2 = 0.32, 0.67, and 0.40, respectively). Adding ECa and soil strength data provided only a slight improvement in WC estimation (R2 = 0.47) and little to no improvement in BD and TOC estimation. When data were analyzed separately by major land resource area (MLRA), fusion of data from all sensors improved soil texture fraction estimates. The largest improvement compared to reflectance alone was for MLRA 115B, where estimation errors for the various soil properties were reduced by approximately 14% to 26%. This study showed promise for in-field sensor measurement of some soil properties. Additional field data collection and model development are needed for those soil properties for which a combination of data from multiple sensors is required. Keywords: NIR spectroscopy, Precision agriculture, Reflectance spectra, Soil properties, Soil sensing.

BackgroundGrazing is widely regarded as a critical factor affecting the vegetation structure, productivity and nutritional value of natural grasslands. To protect and restore degraded grasslands, non-grazed exclosures are considered as a valuable tool. However, it is not clear whether long term non-grazed exclosures of grazers can improve the condition and nutritional value of vegetation and soil properties.MethodsWe have compared the impact of long-term non-grazed and continuous grazed management strategy on vegetation structure, nutritional values and soil properties of alpine meadow of the Qinghai-Tibet Plateau by field investigation (11–13 years) and indoor analysis during 2015–2017.ResultsOur results showed that long-term non-grazed exclosures clearly increased the aboveground biomass and coverage of plant functional types. Long-term non-grazed exclosures improved the development of all vegetation types, except NG (GG, grass species type; SG, sedge species type; LG, leguminous species type; FG, forbs species type and NG, noxious species type). Long-term non-grazed exclosures significantly improved all six measured soil properties (TN, total nitrogen; TP, total phosphorus; TK, total potassium; AN, available nitrogen; AP, available phosphorus and AK, available potassium) in 0–10 cm soil layer, considerable effect on the improvement of all measured soil properties, except TK in 10–20 cm soil layer and all measured soil properties, except TN and TK in 20–30 cm soil layer were observed. However, long-term non-grazed exclosures significantly decreased biodiversity indicators i.e., species richness, Shannon diversity index and Evenness index of vegetation. A substantial decrease in the density, biodiversity and nutritional values (CP (crude protein), IVTD (in vitro ture digestibility) and NDF (neutral detergent fiber)) of all vegetation types, except NG were recorded. While a downward trend in aboveground biomass and all measured soil properties except TP and TK were observed during 2015–2017 in alpine meadows due to long-term grazed treatment. The density, diversity and nutritional value (CP and IVTD) of long-term non-grazed alpine meadows showed a downward trend over time (2015–2017). By considering the biodiversity conservation and grassland livestock production, long-term non-grazed exclosures are not beneficial for the improvement of density, biodiversity and nutritional values of plant functional types. Thus, our study suggests that rotational non-grazed and grazed treatment would be a good management strategy to restore and improve the biodiversity and nutritional values of plant functional types in natural grassland ecosystems.

Three-dimensional digital soil mapping of multiple soil properties at a field-scale using regression kriging

Most existing methods of estimating the settlement of footings in cohesionless soils under working stresses are empirical and based on correlations with field measurements of settlement. The result is considerable scatter and a lack of a basis for long term improvements. In this paper we propose a method of analysis based on equivalent-linear elasticity that involves measurement of real soil properties and uses simple analytical methods. We also propose methods to determine and/or estimate the needed soil properties. We report here on load tests of two footings and one plate with diameters ranging from 0.25 m to 0.91 m, in silty sand, in which measured settlement is compared with settlement predicted using the theory and soil properties determined in the field from seismic tests. We also compare measured settlements with values calculated with Schmertmann's widely used method based on penetration testing.

Predicting water retention of biochar-amended soil from independent measurements of biochar and soil properties

The effects of secondary forest clearance and subsequent land-use on erosion losses and soil properties in the Blue Mountains of Jamaica

<strong class="journal-contentHeaderColor">Abstract.</strong> Information on soils' composition and physical, chemical and biological properties is paramount to elucidate agroecosystem functioning in space and over time. For this purpose, we developed a national Swiss soil spectral library (SSL; <span class="inline-formula"><i>n</i>=4374</span>) in the mid-infrared (mid-IR), calibrating 16 properties from legacy measurements on soils from the Swiss Biodiversity Monitoring program (BDM; <span class="inline-formula"><i>n</i>=3778</span>; 1094 sites) and the Swiss long-term Soil Monitoring Network (NABO; <span class="inline-formula"><i>n</i>=596</span>; 71 sites). General models were trained with the interpretable rule-based learner <code>CUBIST</code>, testing combinations of <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M4" display="inline" overflow="scroll" dspmath="mathml"><mrow><mo mathvariant="italic">{</mo><mn mathvariant="normal">5</mn><mo>,</mo><mn mathvariant="normal">10</mn><mo>,</mo><mn mathvariant="normal">20</mn><mo>,</mo><mn mathvariant="normal">50</mn><mo>,</mo></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="65pt" height="13pt" class="svg-formula" dspmath="mathimg" md5hash="88d0a21da09808e054f5c341d58c923a"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="soil-7-525-2021-ie00001.svg" width="65pt" height="13pt" src="soil-7-525-2021-ie00001.png"/></svg:svg></span></span> and <span class="inline-formula">100<i>}</i></span> ensembles of rules (committees) and <span class="inline-formula"><i>{</i>2</span>, 5, 7, and <span class="inline-formula">9<i>}</i></span> nearest neighbors used for local averaging with repeated 10-fold cross-validation grouped by location. To evaluate the information in spectra to facilitate long-term soil monitoring at a plot level, we conducted 71 model transfers for the NABO sites to induce locally relevant information from the SSL, using the data-driven sample selection method <code>RS-LOCAL</code>. In total, 10 soil properties were estimated with discrimination capacity suitable for screening (<span class="inline-formula"><i>R</i>²â‰¥0.72</span>; ratio of performance to interquartile distance (RPIQ) <span class="inline-formula">≥</span> 2.0), out of which total carbon (C), organic C (OC), total nitrogen (N), pH and clay showed accuracy eligible for accurate diagnostics (<span class="inline-formula"><i>R</i>²&gt;0.8</span>; RPIQ <span class="inline-formula">≥</span> 3.0). <code>CUBIST</code> and the spectra estimated total C accurately with the root mean square error (RMSE) <span class="inline-formula">=</span> 8.4 <span class="inline-formula">g kg^−1</span> and the RPIQ <span class="inline-formula">=</span> 4.3, while the measured range was 1–583 <span class="inline-formula">g kg^−1</span> and OC with RMSE <span class="inline-formula">=</span> 9.3 <span class="inline-formula">g kg^−1</span> and RPIQ <span class="inline-formula">=</span> 3.4 (measured range 0–583 <span class="inline-formula">g kg^−1</span>). Compared to the general statistical learning approach, the local transfer approach – using two respective training samples – on average reduced the RMSE of total C per site fourfold. We found that the selected SSL subsets were highly dissimilar compared to validation samples, in terms of both their spectral input space and the measured values. This suggests that data-driven selection with <code>RS-LOCAL</code> leverages chemical diversity in composition rather than similarity. Our results suggest that mid-IR soil estimates were sufficiently accurate to support many soil applications that require a large volume of input data, such as precision agriculture, soil C accounting and monitoring and digital soil mapping. This SSL can be updated continuously, for example, with samples from deeper profiles and organic soils, so that the measurement of key soil properties becomes even more accurate and efficient in the near future.

Measuring penetration resistance ( PR ) is a common technique for evaluating the effects of field management on soils. This study focuses on the effects of long‐term tillage on the spatial distribution of PR, comparing reduced and conventional tillage ( CT ) practices. The study site, located in Lower Saxony (Germany), has been subdivided into three plots, with one plot having been managed conventionally, whereas reduced tillage ( RT ) practices have been applied to the other two. In total, PR was measured at 63 randomly selected points. The PR data were stepwise interpolated using kriging with external drift. Core samples have been taken at 20 additional sites. The results show significant differences in PR between the different tillage practices. Within the conventionally managed plot, PR ranges to 2.3 MPa less in the topsoil than under RT. However, measured saturated hydraulic conductivity and amount of biopores at the depth of 30–35 cm are significantly greater under RT, indicating improved soil properties under RT. Comparisons between the headlands ( HL ) and the inner field point out the effects of intense field traffic in the HL, where maximum PR values of about 6 MPa have been measured. The spatial prediction of PR values show that long‐term effects of different tillage practices result in clearly structured patterns between CT and RT and the HL. Combining extensive PR measurements and point measurements of additional soil properties supports an adequate interpretation of PR data and can lead to fieldwide derivation of soil functions influenced by field management.

Natural rewilding of abandoned agricultural land and proforestation (the practice of leaving existing, mature forests to grow undisturbed and unmanaged) are increasingly promoted as low-cost nature-based solutions for biodiversity conservation and climate mitigation. Yet their outcomes remain debated across scientific, policy, and societal domains. In particular, Europe lacks harmonized, large-scale evidence on how rewilding influences soil biodiversity and its relationship to forest recovery and carbon sequestration. Existing studies are fragmented across regions, methods, and disciplines, leaving, especially, belowground responses insufficiently understood and poorly integrated into environmental monitoring frameworks.The WILDCARD project, an EU Horizon 2020 initiative, addresses these gaps through a coordinated, cross-European assessment of forest ecosystems undergoing natural succession and proforestation. Using high-throughput DNA metabarcoding of bacterial and fungal ribosomal markers, we characterize soil microbial communities along rewilding gradients spanning from managed croplands to old secondary and primeval forests. Microbial datasets are combined with measurements of forest structure, soil properties, and carbon accumulation trajectories to identify microbial indicators that reflect ecosystem recovery processes.Among others, we expect to observe belowground recovery patterns along the rewilding gradient, including increases in microbial diversity and network connectivity, which are associated with higher functional complexity and overall ecosystem resilience. Reductions in agroecosystem-associated pathogens and increases in symbiotic and saprotrophic microorganisms are anticipated to enhance nutrient cycling, support forest regeneration capacity, and influence carbon persistence. Exploring these patterns will help determine whether and how rewilding shapes belowground biodiversity, and what implications this may have for long-term ecosystem functioning and carbon stabilization.By identifying robust microbial indicators linked to forest rewilding and soil carbon dynamics, this work provides a foundation for developing soil health metrics suitable for large-scale monitoring. The resulting bioindicators will strengthen the scientific basis needed to evaluate the contribution of rewilding to EU climate and biodiversity goals and further support evidence-based decision-making in ecological restoration.

Core Ideas Soil and site properties predict rare plant distribution. Rare plant habitat can be predicted by random forests modeling of data. CaCO3 equivalent, silt, and dry color value predict SRM presence or absence. New rare plant locations identified by random forests modeling. Models help land managers locate and identify potential habitat. Conserving rare plants is important to biodiversity. Shrubby reed-mustard [Schoenocrambe suffrutescens (Rollins) S.L. Welsh and Chatterly] (SRM), a shrub endemic to the Uinta Basin, faces habitat loss due to energy development of fossil fuels. Little is known about the soil and site properties required for successful establishment, growth, and survival of SRM. Our goal was to create random forests models to identify potential SRM habitat and to verify habitat suitability in the field with easily measured properties. The soil properties (SP) model indicated that CaCO3 equivalent, silt, and dry color value predicted the presence or absence of SRM with 10% out-of-bag (OOB) error. Soil properties were correlated with Landsat 5 Thematic Mapper spectral data and a digital elevation model in a Soil–Spectral Correlation (SSC) model. Yellowness, 3/2 normalized difference ratio (NDR), and 3/1 NDR were most strongly correlated with CaCO3 equivalent and silt, and they predicted SRM habitat with 28% OOB error. Guided by SSC model output, 250 additional field presence or absence points were used to train the Spectral–Topographic (ST) model, which was validated by an independent set of SRM plant locations. The ST model using area solar radiation, 7/2 NDR, 5/2 NDR, and the normalized difference vegetation index gave an OOB error of 23%. The ST model can be used to identify potential habitat across a large area. Once identified, easily measured soil and site data from the SP model can verify SRM habitat suitability. These models can help land managers locate and check key soil properties at potential SRM habitat sites.

Mining can cause severe disturbances to the soil, which underpins the viability of terrestrial ecosystems. Post-mining rehabilitation relies on measuring soil properties that are critical soil health indicators. Soil visible–near-infrared (vis–NIR) spectroscopy is rapid, relatively accurate and cost-effective for estimating a range of soil properties. Recent advances in infrared detectors and microelectromechanical systems (MEMS) have produced miniaturised, relatively inexpensive spectrometers. Here, we evaluate the spectra from four miniaturised visible and NIR spectrometers, some combinations and a full-range vis–NIR spectrometer to model 29 soil physical, chemical and biological properties used to assess soil health at mine sites. We collected soils from reference undisturbed native vegetation and topsoil stockpiles from seven mines in Western Australia. We evaluated the repeatability of the spectrometers and the accuracy of the spectroscopic models built with seven statistical and machine learning algorithms. The spectra from the visible spectrometer could estimate soil texture (sand, silt, and clay) more accurately than the NIR spectrometers. However, the spectra from the NIR spectrometers produced better estimates of soil chemical and biological properties. By combining the miniaturised visible and NIR spectrometers, we improved the accuracy of their soil property estimates, which were similar to those from the full-range spectrometer. The miniaturised spectrometers and combinations predicted 24 of the 29 soil properties with moderate or greater accuracy (Lin’s concordance correlation, pc ≥ 0.65). The repeatability of the NIR spectrometers was similar to that of the full-range, portable spectrometer. Our results show that the miniaturised NIR spectrometers can produce accurate predictions of soil properties comparable to the (orders of magnitude) more expensive full-range portable system, particularly when combined with a visible range spectrometer. Thus, there is potential to develop rapid, accurate, cost-effective diagnostic capacity to support mine site rehabilitation based on miniaturised spectrometers and deliver significant positive economic and environmental outcomes.

Carex sempervirens tussocks induce spatial heterogeneity in litter decomposition, but not in soil properties, in a subalpine grassland in the Central Alps