A Spectral Transfer Function to Harmonize Existing Soil Spectral Libraries Generated by Different Protocols

Soil spectral libraries (SSLs) are important big-data archives (spectra associated with soil properties) that are analyzed via machine-learning algorithms to estimate soil attributes. Since different spectral measurement protocols are applied when constructing SSLs, it is necessary to examine harmonization techniques to merge the data. In recent years, several techniques for harmonization have been proposed, among which the internal soil standard (ISS) protocol is the most largely applied and has demonstrated its capacity to rectify systematic effects during spectral measurements. Here, we postulate that a spectral transfer function (TF) can be extracted between existing (old) SSLs if a subset of samples from two (or more) different SSLs are remeasured using the ISS protocol. A machine-learning TF strategy was developed, assembling random forest (RF) spectral-based models to predict the ISS spectral condition using soil samples from two existing SSLs. These SSLs had already been measured using different protocols without any ISS treatment the Brazilian (BSSL, generated in 2019) and the European (LUCAS, generated in 2009-2012) SSLs. To verify the TF's ability to improve the spectral assessment of soil attributes after harmonizing the different SSLs' protocols, RF spectral-based models for estimating organic carbon (OC) in soil were developed. The results showed high spectral similarities between the ISS and the ISS-TF spectral observations, indicating that post-ISS rectification is possible. Furthermore, after merging the SSLs with the TFs, the spectral-based assessment of OC was considerably improved, from R2 = 0.61, RMSE (g/kg) = 12.46 to R2 = 0.69, RMSE (g/kg) = 11.13. Given our results, this paper enhances the importance of soil spectroscopy by contributing to analyses in remote sensing, soil surveys, and digital soil mapping

An Integrative Information Aqueduct to Close the Gaps between Satellite Observation of Water Cycle and Local Sustainable Management of Water Resources

[EN] The past decades have seen rapid advancements in space-based monitoring of essential water cycle variables, providing products related to precipitation, evapotranspiration, and soil moisture, often at tens of kilometer scales. Whilst these data effectively characterize water cycle variability at regional to global scales, they are less suitable for sustainable management of local water resources, which needs detailed information to represent the spatial heterogeneity of soil and vegetation. The following questions are critical to effectively exploit information from remotely sensed and in situ Earth observations (EOs): How to downscale the global water cycle products to the local scale using multiple sources and scales of EO data? How to explore and apply the downscaled information at the management level for a better understanding of soil-water-vegetation-energy processes? How can such fine-scale information be used to improve the management of soil and water resources? An integrative information flow (i.e., iAqueduct theoretical framework) is developed to close the gaps between satellite water cycle products and local information necessary for sustainable management of water resources. The integrated iAqueduct framework aims to address the abovementioned scientific questions by combining medium-resolution (10 m-1 km) Copernicus satellite data with high-resolution (cm) unmanned aerial system (UAS) data, in situ observations, analytical- and physical-based models, as well as big-data analytics with machine learning algorithms. This paper provides a general overview of the iAqueduct theoretical framework and introduces some preliminary results.The authors would like to thank the European Commission and Netherlands Organisation for Scientific Research (NWO) for funding, in the frame of the collaborative international consortium (iAqueduct) financed under the 2018 Joint call of the Water Works 2017 ERA-NET Cofund. This ERA-NET is an integral part of the activities developed by the Water JPI (Project number: ENWWW.2018.5); the EC and the Swedish Research Council for Sustainable Development (FORMAS, under grant 2018-02787); Contributions of B. Szabo was supported by the Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences (grant no. BO/00088/18/4).Su, Z.; Zeng, Y.; Romano, N.; Manfreda, S.; Francés, F.; Ben Dor, E.; Szabó, B.... (2020). An Integrative Information Aqueduct to Close the Gaps between Satellite Observation of Water Cycle and Local Sustainable Management of Water Resources. Water. 12(5):1-36. https://doi.org/10.3390/w12051495S13612

The Brazilian Soil Spectral Service (BraSpecS): A User-Friendly System for Global Soil Spectra Communication

Although many Soil Spectral Libraries (SSLs) have been created globally, these libraries still have not been operationalized for end-users. To address this limitation, this study created an online Brazilian Soil Spectral Service (BraSpecS). The system was based on the Brazilian Soil Spectral Library (BSSL) with samples collected in the Visible–Near–Short-wave infrared (vis–NIR–SWIR) and Midinfrared (MIR) ranges. The interactive platform allows users to find spectra, act as custodians of the data, and estimate several soil properties and classification. The system was tested by 500 Brazilian and 65 international users. Users accessed the platform (besbbr.com.br), uploaded their spectra, and received soil organic carbon (SOC) and clay content prediction results via email. The BraSpecS prediction provided good results for Brazilian data, but performed variably for other countries. Prediction for countries outside of Brazil using local spectra (External Country Soil Spectral Libraries, ExCSSL) mostly showed greater performance than BraSpecS. Clay R2 ranged from 0.5 (BraSpecS) to 0.8 (ExCSSL) in vis–NIR–SWIR, but BraSpecS MIR models were more accurate in most situations. The development of external models based on the fusion of local samples with BSSL formed the Global Soil Spectral Library (GSSL). The GSSL models improved soil properties prediction for different countries. Nevertheless, the proposed system needs to be continually updated with new spectra so they can be applied broadly. Accordingly, the online system is dynamic, users can contribute their data and the models will adapt to local information. Our community-driven web platform allows users to predict soil attributes without learning soil spectral modeling, which will invite end-users to utilize this powerful technique

Spectral Assessment of Organic Matter with Different Composition Using Reflectance Spectroscopy

Soil surveys are critical for maintaining sustainable use of natural resources while minimizing harmful impacts to the ecosystem. A key soil attribute for many environmental factors, such as CO2 budget, soil fertility and sustainability, is soil organic matter (SOM), as well as its sequestration. Soil spectroscopy is a popular method to assess SOM content rapidly in both field and laboratory domains. However, SOM source composition differs from soil to soil, and the use of spectral-based models for quantifying SOM may present limited accuracy when applying a generic approach to SOM assessment. We therefore examined the extent to which the generic approach can assess SOM contents of different origin using spectral-based models. We created an artificial big dataset composed of pure dune sand as a SOM-free background, which was artificially mixed with increasing amounts of different organic matter (OM) sources obtained from commercial compost of different origins. Dune sand has high albedo and yields optimal conditions for SOM detection. This study combined two methods: partial least squares regression for the prediction of SOM content from reflectance values across the 400–2500 nm region and a soil spectral detection limit (SSDL) to judge the prediction accuracy. Spectral-based models to assess SOM content were evaluated with each OM source as well as with a merged dataset that contained all of the generated samples (generic approach). The latter was concluded to have limitations for assessing low amounts of SOM (<0.6%), even under controlled conditions. Moreover, some of the OM sources were more difficult to monitor than others; accordingly, caution is advised when different SOM sources are present in the examined population

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ICTAC Kinetics Committee recommendations for analysis of thermal polymerization kinetics

The present recommendations have been developed by the Kinetics Committee of the International Confederation for Thermal Analysis and Calorimetry (ICTAC). The recommendations provide guidance on kinetic analysis of thermal polymerization, which incorporates both linear and crosslinking polymerization (curing). The focus is on treating the kinetics as measured by differential scanning calorimetry (DSC). The recommendations discuss basic reaction mechanisms and emphasize the multi-step nature of the polymerization kinetics. An overview of mechanistic and phenomenological models is provided for polymerization controlled by reaction kinetics and diffusion. Three different approaches to evaluation of kinetic parameters (activation energy, preexponential factor, reaction model) for individual steps are considered. These approaches comprise model-fitting, isoconversional, and deconvolution analyses. Practical advices are offered for effective usage of each approach. Attention is paid to the typical problems and to the ways of addressing them. The recommendations are intended to assist with efficiently conducting kinetic analysis and interpreting its results.Peer ReviewedPostprint (author's final draft

Mapping soil properties for unmanned aerial system-based environmental monitoring.

Optimal management of water and land resources is based on process-based eco-hydrological models (Kutilek and Nielsen, 1994), which have been increasingly used to solve several scientific and practical problems, such as retrieving soil moisture status and water infiltration patterns, assessing vegetation stress and drought conditions, controlling the spray of pesticides, monitoring potential landslides, evaluating post-fire damages and related restoration practices. The reliability of numerical simulations in critical zone (CZ) processes depends on an accurate parameterization of the soil hydrological behavior that is traditionally assessed using direct measurement methods. Nevertheless, for studies devoted to relatively large spatial scales, direct methods are hampered by the time and costs required for field activities and laboratory analyses. To circumvent somehow these limitations, pedotransfer functions (PTFs) were proposed to estimate the soil hydraulic properties. Basically, a PTF exploits the knowledge of readily available or easily measurable basic information on soil physical and chemical properties to infer the soil water retention and hydraulic conductivity functions. In this chapter, the methods for mapping physical, chemical, and other key properties of the soil will be discussed jointly with a presentation of recently developed proxy tools for monitoring soil-vegetation characteristics through unmanned aerial systems (UASs). Multi- or hyper-spectral sensors installed on a UAS enable field-scale spectral measurements to be performed even at centimeter-scale thus allowing the prediction of soil properties at unprecedented grid resolution. Exploiting the high potential offered by UAS-based multi-spectral imaging, a new family of soil transfer functions, here called spectral transfer functions (STFs), is proposed to estimate the soil hydraulic properties from spectral measurements. The input and/or output data of the PTF/STF mandate the use of advanced interpolation techniques to reliably obtain the soil hydraulic behavior in a study area for modeling purposes. Spatial interpolation is an important task for running distributed hydrological models over relatively large areas. Therefore, a key component of this chapter is devoted to the issues of scale, spatial variability, and geostatistical mapping of soil characteristics

Exploitation of the SoilPRO® (SP) apparatus to measure soil surface reflectance in the field: Five case studies

The SoilPRO® (SP) is an assembly designed to acquire soil reflectance information in the field without disturbing the soil surface, and regardless of atmospheric and solar radiation conditions. This paper summarizes five case studies in which the SP assembly was used for different applications. The case studies consisted of: (1) generating surface spectral measurements under any atmospheric condition; (2) comparing the performance of the SP to the traditional bare fiber method for vicarious calibration of hyperspectral satellite sensors; (3) assessing water repellency of a soil surface governed by organic matter hydrophobicity; (4) spatial prediction of the rate of water infiltration into the soil profile as governed by the soil surface seal; and (5) using the SP apparatus to measure soil surface reflectance in South Shetland Island, Antartica under severe weather conditions. The case studies included calculation of spectral quality, prediction accuracy and measurement stability. The paper discusses each of the cases in detail and concludes that the SP (or similar assembly) is the best way to measure the reflectance of the original soil surface in the field. In the first case study, the spectrum collected by the SP under daily changing illumination was shown to be stable relative to the traditional measurement methods of contact probe or bare fiber. The second case study indicated that use of the SP for vicarious calibration is much more efficient (in terms of time and stability) than ground-truth practice over a large area, and in the third case study, the SP was able to assess a soil surface property governed by organic matter hydrophobicity better than the contact probe, which destroys the soil surface organic seal. A similar achievement was gained in the fourth case study, providing a better assessment of the water-infiltration rate into the soil. In the fifth case study, the SP demonstrated impressive high-quality acquisition of soil surface reflectance with a very low sun angle over the South Pole. Based on these case studies and the high quality of the data generated by the SP in the field, we suggest building, in parallel to the classical soil spectral libraries generated in the laboratory, field soil spectral libraries that will preserve the soil surface properties scanned in the field. We anticipate the development of more applications for the SP assembly based on the capabilities shown in this paper

Mapping water infiltration rate using ground and UAV hyperspectral data: A case study of Alento, Italy

Water infiltration rate (WIR) into the soil profile was investigated through a comprehensive study harnessing spectral information of the soil surface. As soil spectroscopy provides invaluable information on soil attributes, and as WIR is a soil surface-dependent property, field spectroscopy may model WIR better than traditional laboratory spectral measurements. This is because sampling for the latter disrupts the soil-surface status. A field soil spectral library (FSSL), consisting of 114 samples with different textures from six different sites over the Mediterranean basin, combined with traditional laboratory spectral measurements, was created. Next, partial least squares regression analysis was conducted on the spectral and WIR data in different soil texture groups, showing better performance of the field spectral observations compared to traditional laboratory spectroscopy. Moreover, several quantitative spectral properties were lost due to the sampling procedure, and separating the samples according to texture gave higher accuracies. Although the visible near-infrared-shortwave infrared (VNIR-SWIR) spectral region provided better accuracy, we resampled the spectral data to the resolution of a Cubert hyperspectral sensor (VNIR). This hyperspectral sensor was then assembled on an unmanned aerial vehicle (UAV) to apply one selected spectral-based model to the UAV data and map the WIR in a semi-vegetated area within the Alento catchment, Italy. Comprehensive spectral and WIR ground-truth measurements were carried out simultaneously with the UAV-Cubert sensor flight. The results were satisfactorily validated on the ground using field samples, followed by a spatial uncertainty analysis, concluding that the UAV with hyperspectral remote sensing can be used to map soil surface-related soil properties

An Integrative Information Aqueduct to Close the Gaps between Satellite Observation of Water Cycle and Local Sustainable Management of Water Resources

The past decades have seen rapid advancements in space-based monitoring of essentialwater cycle variables, providing products related to precipitation, evapotranspiration, and soilmoisture, often at tens of kilometer scales. Whilst these data effectively characterize water cyclevariability at regional to global scales, they are less suitable for sustainable management of localwater resources, which needs detailed information to represent the spatial heterogeneity of soil andvegetation. The following questions are critical to effectively exploit information from remotelysensed and in situ Earth observations (EOs): How to downscale the global water cycle products to thelocal scale using multiple sources and scales of EO data? How to explore and apply the downscaledinformation at the management level for a better understanding of soil-water-vegetation-energyprocesses? How can such fine-scale information be used to improve the management of soiland water resources? An integrative information flow (i.e., iAqueduct theoretical framework) isdeveloped to close the gaps between satellite water cycle products and local information necessary forsustainable management of water resources. The integrated iAqueduct framework aims to addressthe abovementioned scientific questions by combining medium-resolution (10 m–1 km) Copernicus satellite data with high-resolution (cm) unmanned aerial system (UAS) data, in situ observations,analytical- and physical-based models, as well as big-data analytics with machine learning algorithms.This paper provides a general overview of the iAqueduct theoretical framework and introduces somepreliminary results