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.

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Abstract. The new era of hyperspectral remote (HSR) sensors in orbit is approaching. Missions such as CHIME of the European Space Agency (ESA), EMIT/SBG of NASA, EnMAP of the German Aerospace Center (DLR), and SHALOM of the Israel Space Agency (ISA) will launch in the near future, while other HSR sensors are already in orbit, such as DESIS of DLR, PRISMA of the Italian Space Agency (ASI), and HISUI of the Japan Aerospace Exploration Agency (JAXA). Vicarious calibration (VC) of satellite sensors is vital to tracking a sensor's performance during its lifetime and is a routine procedure in any satellite mission. Accordingly, searching for ideal sites for CAL/VAL operation is an important task that should be part of the mission planning. This study demonstrates two areas in southern Israel that can be acquired in one overpass as VC sites: Amiaz Plain (AP) and Makhtesh Ramon (MR), which were evaluated for their fulfillment of all VC requirements for HSR sensors. AP (5 km2 of homogeneous bright target) was found suitable for radiometric calibration, and MR (200 km2) for spectral and thematic validations. We checked the applicability of these sites using a high-end airborne HRS sensor (AisaFENIX 1K sensor with 420 bands, 375–2500 nm spectral range, and 1.5-m spatial resolution) along with comprehensive field studies and ground measurements. Accordingly, we developed an operational VC protocol to use these sites for both radiometric and spectral quality inspection of HRS satellites. We demonstrated this capability on recent PRISMA and DESIS reflectance products. Here we provide these analyses and recommend how to use these areas to further examine DESIS data's performance. We call for collaborations with individuals and space agencies in using these VC sites, where we will provide ground-truth information and fulfill any other requirements for VC.

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.