Episodic basin-scale soil moisture anomalies associated with high relative humidity events in the McMurdo Dry Valleys, Antarctica

Outside of hydrologically wetted active layer soils and humidity-sensitive soil brines, low soil moisture is a limiting factor controlling biogeochemical processes in the McMurdo Dry Valleys. But anecdotal field observations suggest that episodic wetting and darkening of surface soils in the absence of snowmelt occurs during high humidity conditions. Here, I analyse long-term meteorological station data to determine whether soil-darkening episodes are present in the instrumental record and whether they are, in fact, correlated with relative humidity. A strong linear correlation is found between relative humidity and soil reflectance at the Lake Bonney long-term autonomous weather station. Soil reflectance is found to decrease annually by a median of 27.7% in response to high humidity conditions. This magnitude of darkening is consistent with soil moisture rising from typical background values of < 0.5 wt.% to 2–3 wt.%, suggesting that regional atmospheric processes may result in widespread soil moisture generation in otherwise dry surface soils. Temperature and relative humidity conditions under which darkening is observed occur for hundreds of hours per year, but are dominated by episodes occurring between midnight and 07h00 local time, suggesting that wetting events may be common, but are not widely observed during typical diel science operations.

Evaluating the Precision and Accuracy of Proximal Soil vis–NIR Sensors for Estimating Soil Organic Matter and Texture

Measuring soil texture and soil organic matter (SOM) is essential given the way they affect the availability of crop nutrients and water during the growing season. Among the different proximal soil sensing (PSS) technologies, diffuse reflectance spectroscopy (DRS) has been deployed to conduct rapid soil measurements in situ. This technique is indirect and, therefore, requires site- and data-specific calibration. The quality of soil spectra is affected by the level of soil preparation and can be accessed through the repeatability (precision) and predictability (accuracy) of unbiased measurements and their combinations. The aim of this research was twofold: First, to develop a novel method to improve data processing, focusing on the reproducibility of individual soil reflectance spectral elements of the visible and near-infrared (vis–NIR) kind, obtained using a commercial portable soil profiling tool, and their direct link with a selected set of soil attributes. Second, to assess both the precision and accuracy of the vis–NIR hyperspectral soil reflectance measurements and their derivatives, while predicting the percentages of sand, clay and SOM content, in situ as well as in laboratory conditions. Nineteen locations in three agricultural fields were identified to represent an extensive range of soils, varying from sand to clay loam. All measurements were repeated three times and a ratio spread over error (RSE) was used as the main indicator of the ability of each spectral parameter to distinguish among field locations with different soil attributes. Both simple linear regression (SLR) and partial least squares regression (PLSR) models were used to define the predictability of % SOM, % sand, and % clay. The results indicated that when using a SLR, the standard error of prediction (SEP) for sand was about 10–12%, with no significant difference between in situ and ex situ measurements. The percentage of clay, on the other hand, had 3–4% SEP and 1–2% measurement precision (MP), indicating both the reproducibility of the spectra and the ability of a SLR to accurately predict clay. The SEP for SOM was only a quarter lower than the standard deviation of laboratory measurements, indicating that SLR is not an appropriate model for this soil property for the given set of soils. In addition, the MPs of around 2–4% indicated relatively strong spectra reproducibility, which indicated the need for more expanded models. This was apparent since the SEP of PLSR was always 2–3 times smaller than that of SLR. However, the relatively small number of test locations limited the ability to develop widely applicable calibration models. The most important finding in this study is that the majority of vis–NIR spectral measurements were sufficiently reproducible to be considered for distinguishing among diverse soil samples, while certain parts of the spectra indicate the capability to achieve this at α = 0.05. Therefore, the innovative methodology of evaluating both the precision and accuracy of DRS measurements will help future developers evaluate the robustness and applicability of any PSS instrument.

Estimation of Soil Organic Carbon Contents in Croplands of Bavaria from SCMaP Soil Reflectance Composites

For food security issues or global climate change, there is a growing need for large-scale knowledge of soil organic carbon (SOC) contents in agricultural soils. To capture and quantify SOC contents at a field scale, Earth Observation (EO) can be a valuable data source for area-wide mapping. The extraction of exposed soils from EO data is challenging due to temporal or permanent vegetation cover, the influence of soil moisture or the condition of the soil surface. Compositing techniques of multitemporal satellite images provide an alternative to retrieve exposed soils and to produce a data source. The repeatable soil composites, containing averaged exposed soil areas over several years, are relatively independent from seasonal soil moisture and surface conditions and provide a new EO-based data source that can be used to estimate SOC contents over large geographical areas with a high spatial resolution. Here, we applied the Soil Composite Mapping Processor (SCMaP) to the Landsat archive between 1984 and 2014 of images covering Bavaria, Germany. Compared to existing SOC modeling approaches based on single scenes, the 30-year SCMaP soil reflectance composite (SRC) with a spatial resolution of 30 m is used. The SRC spectral information is correlated with point soil data using different machine learning algorithms to estimate the SOC contents in cropland topsoils of Bavaria. We developed a pre-processing technique to address the issue of combining point information with EO pixels for the purpose of modeling. We applied different modeling methods often used in EO soil studies to choose the best SOC prediction model. Based on the model accuracies and performances, the Random Forest (RF) showed the best capabilities to predict the SOC contents in Bavaria (R² = 0.67, RMSE = 1.24%, RPD = 1.77, CCC = 0.78). We further validated the model results with an independent dataset. The comparison between the measured and predicted SOC contents showed a mean difference of 0.11% SOC using the best RF model. The SCMaP SRC is a promising approach to predict the spatial SOC distribution over large geographical extents with a high spatial resolution (30 m).

Testing different remote sensing compositing periods for SOC content extraction in areas across Germany

&lt;p&gt;High spatial and temporal soil information is crucial to analyze soil developments and for monitoring long term changes to avoid soil degradation. A sufficient soil organic carbon (SOC) content is one of the key soil properties to achieve sustainable high productivity of soils, soil health and increased agroecosystem resiliency. For the usage of remote sensing approaches, naturally exposed soils in Germany occur rarely. Mainly agricultural regions can provide areas of exposed soils for short periods of time during a year. The Soil Composite Mapping Processor (SCMaP) is a fully automated approach to make use of per-pixel based bare-soil compositing to overcome the issue of limited soil exposure based on multispectral Landsat (TM 4, ETM 5, ETM+ 7 and OLI 8) imagery for individually determined time periods between 1984 and 2019.&lt;/p&gt;&lt;p&gt;Due to the high spatial and temporal resolution the SCMaP soil reflectance composites contain a considerable potential to derive detailed soil parameters as the SOC contents of exposed soils to add information to existing soil maps on field scale for areawide applications. Besides the soil reflectance composites several field soil samples provided by different federal authorities build the data base for the SOC modeling. Machine learning (ML) algorithms incl. Partial Least Squares and Random Forest regression with various inputs and set-ups are used and applied for several test areas in Germany. Furthermore, the capabilities of different compositing lengths (5-, 10- and 30-years) to derive spatial SOC contents are tested. The results and the validation of the different ML approaches and compositing lengths will be shown, providing insight into the benefits of this approach.&lt;/p&gt;

Development of a Spatially Upscaled Soil Spectral Library (SUSSL) of cropland signatures for EO sensors data simulation and calibration/validation of topsoil organic carbon prediction models

&lt;p&gt;Optical remote sensing and in particular hyperspectral or imaging spectroscopy remote sensing has been long proved to be an adequate method to predict topsoil organic carbon (Corg) content with good accuracy when the soils are well exposed and undisturbed. Several recent studies demonstrated further in science cases the potential of multispectral Copernicus Sentinel-2 data for bare soils Corg prediction, although challenges were reported related to the impact of disturbing factors. Disturbing factors that can affect the prediction and performances of soil surface properties from optical remote sensing are several and can be e.g. due to mixing in the field-of-view with partial vegetation cover depending on the landscape fragmentation. Most pixels at the remote sensing level are composites and in croplands, mixtures of soils with trees or green plants, or mixture with crop residues after harvest are likely. Another factor might be the presence of residual soil moisture or standing water after rain events. Soil reflectance decreases with increasing soil moisture and increasing soil roughness. Soil Surface roughness changes are observed due to variations in soil texture and to variable microtopography. Possible angular and solar illumination changes may affect the soil reflectance as well.&lt;/p&gt;&lt;p&gt;In the frame of the ESA WORLDSOILS Project (https://www.world-soils.com) aiming at developing a pre-operational Soil Monitoring System to provide yearly estimations of soil organic carbon at global scale based on space-based EO data, we are working on the development of a spatially upscaled soil spectral library (SUSSL). The SUSSL is based on a sub-selection of the European LUCAS soil database, and includes simulation of realistic scenarios of &amp;#8216;landscape-like&amp;#8217; cropland reflectance data with effect of mixture with green and dry vegetation, effect of varying soil moisture content, and effect of variable soil roughness. This database is further convoluted to the different spectral response functions of several EO sensors to simulate EO view of surface reflectances in croplands. In a next step, the SUSSL shall be used for the test and validation of different correction, disaggregation and unmixing techniques to assess the capabilities of the retrieval of undisturbed surface reflectance, to which soil prediction models can be applied with increased accuracy. In this talk, we will present the database developed, including methodological choices and parameter selections for the simulation of the different disturbing effects. Further, preliminary assessments will be shown on the uncertainties of the undisturbed vs. disturbed signal and impact on soil properties prediction.&lt;/p&gt;

Effect of Organic Matter Content on the Spectral Signature of Iron Oxides across the VIS–NIR Spectral Region in Artificial Mixtures: An Example from a Red Soil from Israel

The investigation of iron oxides in soil using spectral reflectance is very common. Their spectral signal is significant across the visible–near infrared (VIS–NIR) spectral range (400–1000 nm). However, this range overlaps with other soil chromophores, such as those for water and soil organic matter (SOM). This study aimed to investigate the effect of different SOM species on red soil from Israel, which is rich in hematite iron oxide, under air-dried conditions. We constructed datasets of artificially mixed soil and organic matter (OM) with different percentages of added compost from two sources (referred to as A2 and A5). Eighty subsamples of mixed soil–OM were prepared for each of the OM (compost) types. To investigate the effect of OM on the strong iron-oxide absorbance at 880 nm, we generated two indices: CRDC, the absorbance spectral depth change at 880 nm after continuous removal, and NRIR, the normalized red index ratio using 880 and 780 nm wavelengths. The different OM types influenced the soil reflectance differently. At low %SOM, up to 1.5%, the OM types behaved more similarly, but as the OM content increased, their effect on the iron-oxide signal was greater, enhancing the significant differences between the two OM sources. Moreover, as the SOM content increased, the iron-oxide signal decreased until it was completely masked out from the reflectance spectrum. The masking point was observed at different SOM contents: 4% for A5 and 8% for A2. A mechanism that explains the indirect chromophore activity of SOM in the visible region, which is related to the iron-oxide spectral features, was provided. We also compared the use of synthetic linear-mixing practices (soil–OM) to the authentic mixed samples. The synthetic mixture could not imitate the authentic soil reflectance status, especially across the overlapping spectral position of the iron oxides and OM, and hence may hinder real conditions.