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

Geoderma 438 (2023) 116636 Available online 11 August 2023 0016-7061/© 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Exploitation of the SoilPRO® (SP) apparatus to measure soil surface reflectance in the field: Five case studies Eyal Ben Dor a , * , Amihai Granot a , Rony Wallach b , Nicolas Francos a , Daniela Heller Pearlstein a , Bar Efrati a , Luboˇ s Borůvka c , Asa Gholizadeh c , Thomas Schmid d a Remote Sensing Laboratory, Geography Department, Porter School of the Environment and Earth Sciences, Faculty of Exact Sciences, Tel Aviv University, Tel Aviv 699780, Israel b Soil and Water Department, The Hebrew University of Jerusalem, Israel c Department of Soil Science and Soil Protection, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Kamycka 129, Suchdol, Prague 16500, Czech Republic d Department of Environment, Centro de Investigaciones Energ´ eticas, Medioambientes y Tecnol´ ogicas (CIEMAT), Avda. Complutense, 40, 28040 Madrid, Spain ARTICLE INFO Handling Editor: Jingyi Huang Keywords: Soil reflectance SoilPRO Field measurements Soil surface properties ABSTRACT 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. 1. Introduction Soil reflectance measurement across the visible–near infrared–- shortwave infrared (VIS–NIR–SWIR; 350–2500 nm) region is one of the most practical means for both proximal and remote sensing of soil attributes (Viscarra Rossel et al., 2016; Behera et al., 2022). It has become a common technique for predicting several soil properties in the laboratory—more rapidly than the traditional wet chemistry methods with reliable accuracy (Ben-Dor and Banin, 1995; Viscarra Rossel et al., 2006; Stenberg et al., 2010). Accordingly, it is widely used not only in the laboratory but also in the field. In general, soil spectral information is gathered into a soil spectral library (SSL) database consisting of both spectral and chemical information. Such libraries are mainly constructed in the laboratory with spectral measurements performed under * Corresponding author. E-mail address: [email protected] (E.B. Dor). Contents lists available at ScienceDirect Geoderma journal homepage: www.elsevier.com/locate/geoderma https://doi.org/10.1016/j.geoderma.2023.116636 Received 18 March 2023; Received in revised form 3 August 2023; Accepted 4 August 2023 Geoderma 438 (2023) 116636 2 controlled conditions (e.g. geometry, grain size, illumination, and moisture content). Soil reflectance measurements in the field are strongly affected by atmospheric attenuation, changes in the sun’s elevation, user stability and experience, spectral protocol configuration, surface variations, unstable geometry, and the measurement means (Milton et al., 2009). These lead to uncertainties in the data, and lower accuracy under outdoor conditions (Christy, 2008); Gomez et al., 2008; Wenjun et al., 2014; Rodionov et al., 2015). Minimizing these effects is therefore essential and requires frequent calibration, resulting in a high volume of measurements for a given target, together with extensive documentation of the environmental conditions during the spectral acquisition, resulting in a small number of samples per unit time. These effects therefore hinder accurate comparisons between spectra and, further, do not allow quantitative analysis of the acquired spectral information. Today, high volumes of field measurements are performed mostly with a bare fiber (BF) fore optic, which suffers from all of the abovelisted issues. Another method is to measure soil reflectance in the field using a contact probe (CP) device (Wenjun et al., 2014) a common assembly used in the laboratory that touches the soil surface with an area of about 12 cm 2 . However, this option suffers from poor representative area, poor contact with irregular surfaces, sensitivity to holding stability, and in some cases, it can harm the fragile soil surface seal due to probe pressure on the soil surface. There are other ways to extract soil spectral information in the field without harming the soil surface, such as deploying a goniometer or constructing a stable and fixed fore-optic geometry with artificial illumination (e.g. Wise and Mars, 2022). The last few years have seen the development of assemblies that can overcome some of the limitations inherent in spectral acquisition under field conditions (Milton, 2010; Milton et al., 2009). Some of these selfdeveloped tools make use of an internal illumination source, either inside the probe, as a modification of available devices (Kusumo, 2018), or in an isolated chamber, for surface and subsurface use (Rodionov et al., 2015), to ensure stable radiation intensity without any atmospheric attenuation. However, these tools are heavy and cannot be carried by an ordinary operator; they require a tractor tow, which may demolish the fragile surface seal, and are not suitable for field and ground-truth work. In general, a standard method for measuring soil surface reflectance in the field should be robust, rapid, representative, and as reproducible and as close to the measurements performed in the laboratory as possible (i.e., enabling extraction of the same spectrum over the same area with no change in its nature, thus allowing the exploitation of the many SSLs in the laboratory). Accordingly, alternative methods and protocols for measuring soil spectral information in the field are strongly required. The technique must represent the soil surface precisely as it is seen from a remote domain (i.e., a natural surface), cover a sizeable representative area, and overcome all the other aforementioned problems (i.e., user effect, geometry stability, surface characteristics, atmospheric attenuation (plus clouds) and changes in sun angle). In this context, Ben-Dor et al. (2017) developed a simple soil field probe assembly named “SoilPRO®” (SP) (U.S. Patent Office Serial No. 15407295). This device can be hooked up to any portable spectrometer furnished with a fiber-optic cable and has already demonstrated remarkable results in fieldwork scenarios. Ben-Dor et al. (2017) showed that the SP performs spectral measurements with laboratory quality, which are stable, even when taken by different non-skilled users. We thus exploited this assembly in different applications, where the soil surface controls field properties, and under different atmospheric conditions in the field. This paper reports on five case studies in which the soil’s thin surface must be preserved to accurately approximate the soil surface properties by spectroscopy. The SP was used by several independent users, and the results are reported herein. Our intention is mainly to open the way to other applications requiring high-quality soil reflectance products under extreme measurement conditions. 2. General materials and methods 2.1. Soil field probe The SP apparatus was used in all of the presented case studies. It was designed at Tel Aviv University and is patented under U.S. Patent Office Serial No. 15407295. It is composed of an aluminum cylinder (painted matte “Kodak” black inside) with a diameter of 24 cm and height of 25 cm, weighing 1.6 kg. The device is furnished with a fiber-optic holder that maintains a stable geometry for both illumination and viewing angle (Ben-Dor et al., 2017). The viewing angle is 45◦, enabling measurement of an area of about 1800 cm 2 with a BF of 25◦field of view (FOV). The assembly’s target dimensions were designed to align with the commonly used 25 cm ×25 cm (10 ×10 square inch) Spectralon (Labsphere®) white reference panel, for optimal performance of white reference measurements as relative reflectance for each measurement. On top of the cylinder, a stabilized tungsten halogen lamp uniformly illuminates the surface at a nadir angle. To use the SP illuminator lamp (Analytical Spectral Devices (ASD) ProLamp® and Ushio® bulb model JC14.5V-50 WC) in the field, we attached a power supply adapter (12 V DC to 15 V DC and 220 V AC to 15 V DC according to the bulb specifications). The adapter provides a stable input voltage to the bulb from any grid or portable power source during operation and indicates any power instability or faulty battery. For the SP prototype, we used a lightweight portable lithium-ion battery (11.1 V, 31.2 Ah, 1.98 kg), which could operate for about 5 h and, in contrast to other commonly used CP tools, it does not consume precious power from the field spectrometer’s battery. The fiber optic from the ASD FieldSpec® (model FSP 350-2500P) is connected via an adapter to a mount attached to the conical aluminum side addition of the chamber to position its fore optic at a 45◦angle for maximum target area. This configuration is designed to maintain minimal specular reflection and maximal Lambertian radiation collection from the target below, as well as to simulate the geometry of the ASD CP (ASD Inc., n.d.) apparatus and the illumination unit’s operating protocol as suggested by ASD Inc. (2012). Fig. 1 provides the SP scheme and all of the system’s accessories, and Fig. 2 illustrates the operating principle as demonstrated by a single operator. The operating scheme of the SP is similar to that for laboratory spectral data measurements. The spectrometer has to be warmed up for 60 min, the bulb for 15 min and the rim of the SP periphery has to be cleaned with a soft white paper. For configuration and white reference calibration, the assembly has to be deployed on a 10 square inch white reference (Spectralon) (see Fig. 2c). The spectral configuration consists of 30 readings for samples, white reference and black current. Optimization has to be established, just as in the laboratory, as the measurement scheme reflectance is relative to the white reference, and a stable and straight reflectance line of 100% has to be achieved for the white reference sample. The SP behaves as a real dark chamber as no SP signal occurs during a sunny day and high sun elevation condition while the bulb was shut off. Soil measurement is done by gently placing the SP surface rim on the soil surface to avoid any leakage of light from the inner SP volume. 2.2. Statistical evaluation To evaluate the spectral similarity in our case studies, we conducted statistical analyses of root mean square error (RMSE, Eq. (1), ratio of performance to interquartile distance (RPIQ, Eq. (2), and the average sum of deviations squared (ASDS, Eq. (3). RMSE =  ∑ n i=1 (Yi− Yi)2 n √ √ √ √ √(1) RPIQ =IQ/RMSE (2) E.B. Dor et al. Geoderma 438 (2023) 116636 3 ASDS =∑n i=1((Yi/ Yi) − 1)2 n(3) where ˆ Y is the predicted value, Y is the observed value of the number of data points, and IQ is the interquartile distance of the measured value. These statistics were used according to each of the case studies’ requirements. 3. Case studies Sections 3.1–3.5 present five case studies in which the SoilPro® (SP) was utilized. It is noteworthy that, in all instances, precise measurement of the uppermost layer of delicate soil, under undisturbed outdoor conditions such as surface and radiation, was crucial. Each case is accompanied by a thorough description of the problem addressed by the SP, a comprehensive description of the methodology employed to attain the solution, and a detailed analysis of the resulting outcomes and derived conclusions. 3.1. Case study 1: All conditions – 24/7 field spectral measurements To evaluate the reliability of the SP’s performance in measuring surface reflectance in the field while avoiding the aforementioned problems such as poor outdoor radiation, five soil samples from Israel Fig. 1. Left: Sketch of the SoilPRO device (profile view). Right: SoilPRO system setup configuration (top view). (1) main body, (2) illumination source, (3) illumination beam, (4) optic fiber, (5) fiber holder, (6) fiber FOV, (7) handle, (8) power adapter, (9) power cord, (10) portable battery, (11) ASD Field Spec Pro spectrometer and operating laptop. Fig. 2. Use of SoilPRO in the field. (a, b) Ease of carrying the portable spectrometer over different areas, (c) white reference calibration process, (d) surface measurement. Table 1 Soil samples selected for the SP exercise. Sample Israeli name Definition USDA Great Group soil taxonomy A Hamra Brownish red sandy soil Haploxeralfs B Rendzina Rendzina mountain soil Rendolls C Vertisol Brown alluvial soil Chromoxererts D Loess Loess raw soil Camborthids E Coastal beach sand Coastal beach sand Torripsamments E.B. Dor et al. Geoderma 438 (2023) 116636 4 representing five different USDA orders (see Table 1 and Fig. 3) were selected. All samples were collected from the upper 5 cm, brought to the laboratory, air-dried, and sieved to pass a 2 mm sieve. Each soil was placed on a shallow aluminum plate painted matte black with a diameter of 30 cm and thickness of 2 cm to completely fill the SP’s area diameter of 25 cm. The aim was to examine the stability of the SP apparatus relative to BF practices over several soil types and with changing sun elevation and atmospheric conditions. The reflectance of each soil was incrementally acquired on 23 May 2020 using a BF, outside the laboratory under a clear sky, and then using the SP for 8 h (08:30–16:30 h). At 12:30 h, clouds appeared temporarily during the measurements. Therefore, two measurements were acquired at that time, with and without clouds. The BF was mounted on a tripod facing nadir and covering the soil target with a 30% margin (area of 1000 cm 2 ). Fig. 4 shows the spectra throughout the day as measured with the SP and BF. Regarding the SP measurements, it can be noticed that a stable and constant reflectance was delivered throughout the day (Fig. 4), with no variance in albedo value or spectral shape for the spectra taken at different times. Only a minor variation in albedo was seen in sample (d) at 12:30 h, but the spectrum still satisfied our expectation of stability. In contrast, the BF measurements showed variations in albedo at each time point due to the instability of the atmospheric conditions and changes in sun angle. Marginal changes appeared during the clouds’ appearance at 12:30 h with the BF (Fig. 4). We assume that those changes were the result of bidirectional reflectance distribution function (BRDF) effects due to the changing solar elevation and stable nadir viewing angle, as well as the inconsistent illumination due to the passing clouds. In Fig. 4 (bare fiber), two extreme white reference values are shown: in the one that is lower than 100% reflectance, the white reference was measured when the clouds were passing over, and is relative to the white reference standard which was measured under a clear sky; the one that is higher than 100% was measured under a clear sky, and is relative to the white reference measured under a cloudy sky. The stable results of the SP measurements suggest that it can provide accurate and stable readings despite the uncertainties arising from atmosphere, illumination, measurement geometry, and user experience. This examination confirmed the ability of the SP assembly to acquire stable field measurements, while leaving the soil surface undisturbed (i. e., preserving it in its natural state under constant illumination and geometry). It can serve as a rapid measurement tool to develop a field SSL in parallel to a laboratory SSL, which will enable obtaining field information and reliable ground-truth measurements (Milton et al., 2009). Francos and Ben-Dor (2022) recently established a transfer function to estimate field reflectance from a laboratory SSL using mutual measurements with the field (SP) and laboratory (CP) SSL protocols. In addition, this device can close the gap, when soil spectral information is needed under extreme conditions such as during the night, avoiding complicated measurement schemes as conducted, for example, by Wise and Mars (2022). 3.2. Case study 2: Vicarious calibration of hyperspectral sensors from the ground Hyperspectral (HSR) sensors require periodic radiometric and spectral calibration. Aside from onboard calibration routines (e.g., partialaperture calibrators, standard lamps, and solar diffuser panels) (Thenkabail, 2016), vicarious calibration (VC) practices are the most useful method for examining and rectifying the sensor’s radiometric and spectral response, and checking its thematic performance (Xu et al., 2020). The VC method uses natural or artificial sites on the ground for the post-launch calibration of sensors and spectral measurements of these sites that precisely represent the reflectance of the area with high fidelity (Morain and Budge, 2004). Accordingly, VC is a significant part of many groups’ activity worldwide, and those who operate and use HSR data invest a great deal of effort in the calibration/validation (CAL/VAL) of the mission at hand, such as the IEEE P4001 (https://standards.ieee. org/ieee/4001/7314/) working group, the GEO working group, the CAL/VAL Working Groups of Surface Biology and Geology (SBG) (Kokaly and Turpie, 2019), and the RadCalNetWorking group (Bouvet et al., 2019). The most common way to carry out VC is to use natural and homogeneous ground targets on earth that are stable in space and time. The measurement is conducted during or close to the sensor’s overpass time, estimating top of the atmosphere (TOA) radiance at-sensor. The critical issue in VC is the measurement of the ground reflectance in a very precise, reliable, and representative way, very close to or during the overpass. This is also true for any other ground-truth mission and hence requires a carefully executed protocol and method. In general, to achieve representative reflectance on the ground for the mission, ground spectral measurements are well-planned, with several known protocols. In addition, the VC area should be stable with minimum changes over time, and it should allow for frequent and repeated ground measurements. Due to today’s availability of portable spectrometers, there are many protocols for acquiring field reflectance measurements, from just pointing the BF at a nadir-looking angle manually to using a goniometer; both rely on solar radiation and are strongly affected by the atmosphere’s attenuation and the sun’s elevation (e.g. Stoner et al. 1980, Peddle et al. 2001,Schopfer, 2008, Kodaira and Shibusawa, 2013, Labarre et al 2019). Another common practice is to use a CP furnished with artificial illumination while touching the surface for measurements. In each of these methods, samples representing the selected and stable area are needed where several limitations, such as sensitivity to the measurement geometry and even to the color of the clothes that the users are wearing, matter (SVC, 2019). The goniometer apparatus is a good method for a single measurement but cannot cover large areas, whereas the CP assembly, which can do so, is problematic as it has a very small area. In addition, it may destroy the surface due to the need to contact the fragile ground. To avoid some of these problems, Ray Kokaly (personal communication, 2022) developed a portable assembly that simultaneously measures the upwelling and downwelling radiation at a constant geometry, thereby minimizing some of the main uncertainties (recently, a new concept termed ASD Field Spec Dual has also been adopted to overcome these obstacles; htt Fig. 3. Five soil samples used for case study 1 (see Table 1). According to the USDA Great Group Soil Taxonomy, these soils are: (a) Haploxeralfs, (b) Rendolls, (c) Chromoxererts, (d) Camborthids, (e) Torripsamments. E.B. Dor et al. Geoderma 438 (2023) 116636 5 ps://www.malvernpanalytical.com/en/products/product-range/asd-ra nge/fieldspec-range/software/fieldspec-dual). In this case study, our aim was to minimize, using the SP apparatus, the effects of measurement geometry, atmospheric attenuation, and sun elevation changes during the short time of the sensor’s overpass. Accordingly, we examined the SP’s performance during a real calibration mission over a well-known VC site selected for HSR performance—Amiaz plain in southern Israel (31◦04 ′ 20.98 ″ N, 35◦22 ′ 14.83 ″ E). The area is in the Judean Desert (Fig. 5) and is a homogeneous brightsurface playa (5 km ×5 km) with high reflectance values (>0.4). Because vegetation is scarce and it is stable in space and time, it was selected for the radiometric evaluation and cross-calibration of PRISMA, DESIS, EnMAP and EMIT orbital sensors (Heller-Pearlshtien et al., 2021, 2023; Heller-Pearlshtien and Ben-Dor, 2022). During August 2021 and August 2022, and close to the overpass of EnMAP and EMIT sensors, we conducted VC measurements over the area. We used the EnMAP measurement protocol (Heller-Pearlshtien et al., 2023) that captures measurements of a 90 m ×90 m area and the SP measurement protocol for the VC mission. Fig. 5 provides the scheme of the EnMAP measurements from August 2022. An ASD BF with 25◦ fore optics was also used from a height of 1.5 m covering an area of about 1800 cm 2 . The measurements were conducted on a clear morning, and the users maintained a stable geometry of back to the sun while measuring the surface reflectance every 3 m. Each 90 m line captured 30 measurements, and another 48 points were taken randomly between the lines. After each line, a white reference measurement was taken to compensate for any changes in sun elevation or possible changes in the optical depth during the measurements. All measurements in the area took about 45 min. In total, the BF measurements consisted of 138 points (Fig. 5). After the BF session, we remeasured the area with the SP using the same 90 m ×90 m test area, though without any geometry limitations. During the measurements, cumulus and cirrus clouds appeared, but we continued measuring. In total, we measured 70 points with the SP, and it took approximately 20 min. No intercalibration with a white reference was conducted during the measurement session. The white reference at Fig. 4. Case study 1 spectra from bare fiber (top) vs. SoilPRO (bottom) of soils a–e (see Table 1) at different times and under a clear and cloudy sky at 12:30 h. White Reference (WR). E.B. Dor et al. Geoderma 438 (2023) 116636 6 the end of the session was measured and compared to the first white reference, demonstrating maintenance of stability with similar unit values for both. Fig. 6 shows the spectra of the BF under the EnMAP protocol and the SP measurements, along with the standard deviation (SD) calculated from all of the spectra. The SP provided more stable spectra with SD = 0.38 compared to BF, with SD =0.46. In addition, the SP provided information across the entire spectral range, including the well-defined spectral region of the atmospheric water vapor attenuation range around 1400 and 1900 nm. Fig. 7 shows a comparison of the measurements obtained using the BF and SP in August 2022 versus 2021 (1 year apart) at the exact same location in the Amiaz plain. The average spectra showed significant variation for the BF compared to the SP measurements. It should be noted that the two sets of data were acquired by different ASD spectrometers (FieldSpec3 in 2021 and FieldPro4 in 2022). The ASDS values between the average spectrum in each year for both SP and BF were calculated, (0.14 and 0.83, respectively). As a lower ASDS value suggests a closer similarity between two spectra, this indicates that the SP provided better results than the traditional BF measurement. Moreover, the SP enabled compensating not only for the atmospheric variation but also for different spectrometer models. It should be noted that internal calibration for cross-calibration between two or more sensors is an important approach that has not yet been consolidated; these results thus call for more studies on cross-calibration between both ground and orbital sensors. Fig. 8 shows photos from the BF field measurements compared to SP and white reference measurements. 3.3. Case study 3: Proximal sensing of soil water repellency in a field Water-repellent soils refer to soils that are not readily wetted (also termed hydrophobic). Soil hydrophobicity affects the soil’s physical and hydrological properties. It increases hysteresis of the water-retention curve (Ritsema et al., 1998; Bauters et al., 2000), generates unstable wetting fronts due to fingered flow (Hendrickx et al., 1993), reduces infiltration capacity relative to wettable soils (e.g., Wang et al., 2003; Doerr et al., 2006), and induces greater surface runoff and erosion (Burch et al., 1989; Benavides-Solorio and MacDonald, 2001). Soil water repellency (SWR) depends, among other factors, on water content and quality, and soil surface characteristics. The degree of SWR is commonly quantified by the water drop penetration time (WDPT) test (Doerr, 1998). This method consists of placing a drop of distilled water on the soil surface and recording the time taken for the drop to penetrate completely into the soil. The intensity of the repellency is usually determined by approximating the surface tension required to initiate infiltration instantly (Letey et al., 2000), a measure of how strongly a soil repels a water drop at the time of application (i.e., how much of the water drop “balls up” on the surface). Whereas both methods can be carried out in the laboratory and in the field, they have the disadvantage of being semi-objective, timeconsuming, and requiring expert users and sophisticated techniques (e. g., a well-aligned high-speed video camera). SWR can be strongly altered by variations in environmental conditions, such as humidity (Jex et al., 1985), temperature (Diehl and Schaumann, 2007), moisture content (T¨ aumer et al., 2005), organic matter content and species, and the thin soil layer’s structural characteristics; where the measurement must be applied on undisturbed samples under field conditions. Wallach et al. (2005) reported strong development of water repellency in sandy soil under orchard trees that were irrigated with secondary-treated wastewater for 30 years, resulting in the accumulation of hydrophobic organic compounds on the soil surface. Recent studies have shown that soil spectroscopy in the VIS–NIR–SWIR region (350–2500 nm) has the potential to predict SWR indices. Such studies have been performed in the laboratory with disturbed soil samples of different orders from New Zealand (Kim et al., 2014), and for homogeneous soils from Denmark (Knadel et al., 2016). To the best of our knowledge, assessing hydrophobicity indices, such as WDPT, by VIS–NIR–SWIR reflectance spectroscopy in the field under real conditions on an undisturbed soil surface has never been carried out. The aim of this case study was to use the SP to better measure the reflectance of the undisturbed soil surface, at laboratory quality, in order to extract more reliable field SWR values than is traditionally done. We therefore conducted the following exercise using the SP assembly to measure the undisturbed soil surface with a parallel measurement of WDPT. Two sets of samples were taken from a 50-year-old orchard plot that had been irrigated with secondary-treated effluent water for 30 years (Wallach et al., 2005): 146 samples were taken to the laboratory under “undisturbed” surface sampling (Persson and Bergstr¨ om, 1991) and subjected to spectral measurements using the ASD CP assembly in the laboratory and WDPT measurements according to a laboratory protocol (see later); 68 samples were spectrally measured in the field using the SP before the “undisturbed” soil-sampling procedure. These samples were also subjected to WDPT measurements following the same laboratory protocol. Fig. 5. Amiaz plain field test site, 90 m ×90 m. Three straight 90 m lines were measured with an additional 48 randomly measured points between these lines (sun elevation 70.54 ◦, sun azimuth 152.13◦). E.B. Dor et al. Geoderma 438 (2023) 116636 7 The laboratory protocol for the WDPT measurement consists of placing three 50 mL drops of distilled water on the surface of a soil sample, and determining the time elapsed to the drop’s complete absorption (Doerr, 1998). The time was measured using a high-speed camera that followed the water drop angle. The spectral measurement was performed for both sets of samples with the same ASD spectrometer, which was hooked up to the CP (in the laboratory set) and the SP (in the field set). The orchard tree area and the sampling locations of all soils for Fig. 7. Comparison of bare fiber and SoilPRO® reflectance measurements (average of all measurements) over the 90 m ×90 m test area in Amiaz plain in 2021 (solid line) vs. 2022 (dashed line). Fig. 6. Comparison of representative reflectance and variation over the Amiaz plain area using the bare fiber and the SoilPRO measurements for a 90 m ×90 m test area. E.B. Dor et al. Geoderma 438 (2023) 116636 8 WDPT measurements are shown in Fig. 9. Most of the sampling points were in shaded areas, without the direct solar radiation needed for ordinary field spectral measurements. Fig. 10 shows the spectra of some selected soil samples acquired in the field by the SP. The WDPT values for each of the soils are also given. The spectra are very stable, similar to laboratory quality. Especially noticeable is the concave slope across the VIS–NIR–SWIR region that suggests the presence of soil organic carbon (SOC) in both samples (#69 and #64) with the highest WDPT values (502 and 2215, respectively). This is in good agreement with Wallach et al.’s (2005) finding that in this area, the hydrophobic organic substances in the effluent water that have accumulated over the long irrigation period are the driving force Fig. 8. Field measurements using bare fiber (a) compared to SoilPRO (b), and white reference measurements using the SoilPRO (c). Fig. 9. The exact locations and the WDPT values (in second) that measured in the orchard area. Fig. 10. Spectra of soil samples from the orchard as taken by the SP. E.B. Dor et al. Geoderma 438 (2023) 116636 9 for the water repellency characteristics of the soil surface. The proximate spectral-based model was run on both data sets: 146 samples (calibration n =108, validation n =38) with the CP measurement in the laboratory and 68 samples (calibration n =51, validation n =17) with the SP measurement in the field. To quantitatively predict the WDPT values by spectroscopy, we ran a partial least squares regression (PLSR) procedure using several preprocessing stages on the reflectance values and their combinations: first derivative, Log (1/R), standard normal variate (SNV) (Dhanoa et al., 1989) and continuum removal (CR) using the PARACUDA-II engine (Gholizadeh et al., 2018) (Table 2). The SP data set (in-situ SP measurements with reflectance SNV preprocessing) gave the best validation results for the PLSR analyses, providing higher accuracy than the CP data set (with reflectance preprocessing) from the laboratory (R 2 =0.86 and 0.52, and RPIQ =4.43 and 2.16, respectively). Although the samples that were brought to the laboratory were kept “undisturbed”, using the CP for the measurement process may have destroyed the fragile soil seal and thus, could not mimic the real in-situ field condition as achieved by the SP measurement. The results demonstrated the potential of the SP assembly for measuring a thin undisturbed soil surface in the field by covering a large representative area with constant geometry and not contacting the soil seal. In this case study, the SP assembly enabled large-scale, precise and representative spectral measurements of the real undisturbed soil surface with no need to bring the soil to the laboratory. In addition, the SP makes it possible to measure the soil in the field in shaded areas under the tree plant canopy. The SP preserves the soil surface’s condition as it is in the field and therefore provides better proximal spectral modeling to estimate the SWR status. Easy operation of the SP, which does not require skilled personnel, and the ability to measure a large number of samples in a short time (100 samples’ spectra measured in about 60 min) and in shaded areas, place this assembly as a game-changer in assessing the hydrophobicity status of soils beneath tree canopies and close to the irrigation pivot of the drip irrigation practice. 3.4. Case study 4: Assessing the rate of water infiltration into soil profile This section is a summary of comprehensive research that has been published in the literature between 2021 and 2022. This work has adopted the SP to measure the soil surface condition and its effect on the water regime. In the previous sections, it was demonstrated that the SP gives a better representation of the upper (thin) soil surface layer (“seal”) condition in the field and enables better proximal sensing of soil surface-related properties (e.g., WIR). As the upper layer may be fragile and sensitive to small spectral changes in the field such as fire (Argentiero et al., 2021; Yang et al., 2021) or rain energy (Goldshleger et al., 2009; Li et al., 2020), we further examined the SP’s performance with another surface-related property—water-infiltration rate (WIR). WIR is a very important hydrological parameter that controls runoff, surface leaching, soil erosion, water-storage capacity in the soil profile, and water availability for crops (Franzluebbers, 2002). Moreover, WIR is involved in several soil processes and has been correlated with vegetation cover (Walker et al., 1981; Thurow et al., 1986, 1988; Van de Koppel et al., 1997, Van de Koppel et al., 2002). Accordingly, WIR may be a critical soil property in combating the desertification phenomenon in the current era of global warming, concomitant with high rain energy and increases in precipitation volume. WIR into the soil is strongly related to the soil’s surface properties, such as clay minerals, which are not necessarily driven by SOC, as demonstrated with the water repellency issue (SOC may vary from one source to the next and may change the WIR in opposite directions). The WIR is strongly dependent on the interface between the soil and the atmosphere and is mainly controlled by the thin seal that evolves between these two phases (also termed “physical crust” by Agassi et al., 1985, Agassi et al., 1994). Lado et al. (2004) stated that an increase in SOC limits soil seal formation and therefore may induce infiltration. On the other hand, in some cases (e.g., case study 3 in this paper and supported by Capriel, 1997), some species of SOC may cause hydrophobicity and thus decrease the WIR values. Ben-Hur and Letey (1989) found that clay dispersion at the soil surface determines WIR and affects seal formation. Stern et al. (1991) examined the relationship between clay minerals and WIR status and found that soils rich in smectite minerals are more vulnerable to sealing and erosion. They suggested that kaolinite and illite have opposite effects on soil sealing and the resulting erosion. Goldshleger et al. (2009) predicted WIR via spectroscopy and PLSR analyses in disturbed soils under a controlled rain simulator using varying raindrop energy. As the soil under the rain simulator did not mimic the real in-situ field condition (the soil surface was disturbed when it was brought to the laboratory), the rain simulator showed only trends, and not the actual field condition. As a result, the real field WIR must be examined with a different protocol. The aim of this case study was to minimize the destructive effect of the traditional CP measurement on the soil crust, and avoid indirect assessment of the WIR in the laboratory. Accordingly, we used the SP assembly to measure the reflectance values of the soil surface in the field while simultaneously measuring WIR with a mini disk infiltrometer (Decagon Devices, Inc). Several samples from different locations across Mediterranean countries that present variations in soil texture were selected: one place in Italy, one place in Israel and three in Greece (Fig. 11). After spectral and WIR measurements in the field, the soil samples were brought to the laboratory and the spectral measurements were performed on a disturbed mixed soil sample, using Ben Dor et al.’s (2015) protocol for laboratory ASD spectrometer measurements with the CP assembly. To generate the spectral-based models for the WIR prediction, the spectral data were subjected to several preprocessing techniques. The spectral readings in the field (using the SP) and the laboratory (using the CP) were compared to the WIR measured in the field. Table 3 provides the results of the validation stage for both spectral measurement practices after Savitzky–Golay and first-derivative preprocessing approaches were executed, and the PLSR models were generated. The mixed data set (generic) showed excellent results in the field domain using the SP (R 2 Val =0.70). Although the validation of the laboratory-based model also showed an acceptable performance (R 2 Val =0.57). These results Table 2 The best results of the PLSR model for the validation set using the CP and SP apparatus and the preprocessing method of the reflectance value. 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