Soil Reflectance Spectra Research Articles

A text-based, generative deep learning model for soil reflectance spectrum simulation in the solar range (400–2499 nm)

Soil spectral reflectance is a necessary input for land surface and radiative transfer models, and can be used to infer soil properties. Numerous soil reflectance inversion models have been developed based on mechanistic approaches, each with their own limitations. Mechanistic models based on radiative transfer theory are usually based on only a few input soil properties, whereas data-driven approaches are limited by high non-uniformity of available published datasets that severely limits the amount of data usable for model calibration. To address these limitations, a fully data-driven soil optics generative model (SOGM) for simulation of soil reflectance spectra from soil property inputs was developed based on the denoising diffusion probabilistic model (DDPM). The model was trained on an extensive dataset comprising nearly 180,000 soil spectra-property set pairs from 17 published datasets. The model generates soil reflectance spectra from text-based inputs describing soil properties and their values rather than only numerical values and labels in binary vector format, which means the model can handle variable formats for property reporting. Because the model is generative, it can simulate reasonable output spectra based on an incomplete set of available input properties, which becomes more reliable as the input property set becomes more complete. Two additional sub-models were also built to complement the SOGM: a spectral padding model that can fill in the gaps for spectra shorter than the target solar range (400 to 2499 nm), and a wet soil spectra model that can estimate the effects of water content on soil reflectance spectra given the dry spectrum predicted by the SOGM. It can also be easily integrated with other soil–plant radiation models used for remote sensing research such as PROSAIL and Helios 3D plant modeling software. The testing results of the SOGM on new datasets not included in model training demonstrated that the model can generate reasonable soil reflectance spectra based on available property inputs. Results also show soil clay/sand/silt fraction, organic carbon content, nitrogen content, and iron content tended to be important properties for spectra simulation. Inclusion of some trace minerals like nickel as model inputs decreased model performance because of their low concentrations and large propensity for ground-truth measurement error.

Predicting soil nutrients with PRISMA hyperspectral data at the field scale: the Handan (south of Hebei Province) test cases

ABSTRACT This research investigates the suitability of PRISMA and Sentinel-2 satellite imagery for retrieving topsoil properties such as Organic Matter (OM), Nitrogen (N), Phosphorus (P), Potassium (K), and pH in croplands using different Machine Learning (ML) algorithms and signal pre-treatments. Ninety-five soil samples were collected in Quzhou County, Northeast China. Satellite images captured soil reflectance data when bare soil was visible. For PRISMA data, a Linear Mixture Model (LMM) was used to separate soil and Photosynthetic Vegetation (PV) endmembers, excluding Non-Photosynthetic Vegetation (NPV) using Band Depth values at the 2100 nm absorption peak of cellulose. Sentinel-2 bare soil reflectance spectra were obtained using thresholds based on NDVI and NBR2 indices. Results showed PRISMA data provided slightly better accuracy in retrieving topsoil nutrients than Sentinel-2. While no optimal predictive algorithm was best, absorbance data proved more effective than reflectance. PRISMA results demonstrated potential for predicting soil nutrients in real scenarios.

Removing the moisture effect on predicting soil organic matter using vis-NIR spectroscopy with external parameter orthogonalization

External parameter orthogonalization (EPO) can remove the moisture effect on the in situ soil reflectance spectra. However, when applied to a sample space with high changeability in structure of mineral, EPO cannot fully remove the in situ effect on spectra due to the complex interactions between both soil moisture and mineral composition on the spectra. This study aims to minimize the effect of soil moisture on the accuracy of in situ visible and near-infrared (Vis-NIR) spectra on estimating of soil organic matter (SOM) content by using strategies of firstly classifying the samples according to absorbance length ∼ 2200 nm (AL), texture (TC) and land use (LC), and then implementing the EPO through each class in each strategy. The samples were split into the total EPO development, where a serial subset was selected for the EPO dataset, and validation dataset. For every subset selected from the total EPO development, an EPO corrected matrix, developed from the spectra from each class of each classification strategy, was implemented with the spectra from the validation dataset and local spectra library. The performances of the EPO algorithm were evaluated through partial least squares regression (PLSR). The results showed that the classifications of AL, LC and TC combined with EPO led to a greater reduction in the principal component space of in situ subsets and improved the similarity between in situ spectra and dry subset distribution. The combination of classification with EPO leads to a reduction of 11% - 25% prediction RMSE of SOM. Classification with EPO can minimize the moisture effect on in situ spectra from a sample space with high variability in mineral composition.

Estimation of Soil-Related Parameters Using Airborne-Based Hyperspectral Imagery and Ground Data in the Fenwei Plain, China

Hyperspectral remote sensing technology is an advanced and powerful tool that enables fine identification of the numerous soil reflectance spectrum characteristics. Heavy metal(loid)s (HMs) are the primary pollutants affecting the soil biodiversity and ecosystem services. Estimating HMs’ concentrations in soils using hyperspectral data is an effective method but is challenging due to the effects of varied soil properties and measurement-related errors inflicted by atmospheric effects. This study focused on typical mining areas in the Fenwei Plain (FWP), China. Soil-related data were collected by leveraging airborne- and ground-based integrated remote sensing observations. The concentrations of eight HMs, namely copper (Cu), lead (Pb), zinc (Zn), nickel (Ni), chromium (Cr), cadmium (Cd), arsenic (As), and mercury (Hg), were measured by laboratory analysis from 100 in situ soil samples. Soil reflectance spectra were processed based on resampling and envelope methods. The combination datasets of the concentrations and optimal soil reflectance spectra were used to build the soil-related parameter retrieval models using three machine learning (ML) methods, and the feasibility of applying the high-performance retrieval model to estimate the HM concentrations in mining areas was evaluated and explored. Spectral analysis results show that four hundred and twenty-eight bands of five wavelength ranges are of high quality and obviously demonstrate the spectral characteristics selected to build the soil-related parameter models. The evaluation results of eight combination data subsets and three methods show that the preprocessing of spectral data (ground- and airborne-based reflectance) and soil samples with the random forest (RF) method can obtain higher accuracy than support vector machine (SVM) and partial least squares (PLS) methods, denoted as the AER-ACS-RF and GER-GCS-RF models (the average RMSE values of eight HMs were 2.61 and 2.53 mg/kg, respectively). The highest R2 values were observed in Cd and As, with an equal value of 0.98, followed by that of Pb (R2 = 0.97). The relative prediction deviation (RPD) values of Cu and AS were greater than 1.9. Moreover, the airborne-based AER-ACS-RF model presents a good mapping effect about the concentrations (mg/kg) of eight HMs in mining areas, ranging from 21.65 to 31.25 (Cu), 16.38 to 30.45 (Pb), 62.02 to 109.48 (Zn), 23.33 to 32.47 (Ni), 49.81 to 66.56 (Cr), 0.09 to 0.23 (Cd), 7.31 to 12.24 (As), and 0.03 to 0.17 (Hg), respectively.

Visible and near-infrared spectral results of Chang’E-5 surficial and subsurface soils

Aims. Studies on high-resolution and high-precision laboratory reflectance spectra of the Moon have historically been restricted to the analysis of old Apollo samples (>3.0 Ga). In contrast, studies of young lunar soils have exclusively relied on the analysis of remote sensing spectra. In this study, we present the results of a laboratory spectral investigation of young lunar soils (~2.0 Ga) obtained by the Chang’E-5 (CE-5) mission. Methods. We analyzed surficial and subsurface soils collected through scooped and drilled sampling methods. The laboratory reflectance spectra of the CE-5 soils were compared with those of Apollo soils and orbital spectra. Two methods were employed for maturity inversion. The relationship between the UV-vis color and TiO2 content of young basalts was also investigated. Results. The CE-5 samples exhibit much fresher spectral features, including higher reflectance, deeper absorption depths, and a smaller visible and near-infrared continuum slope (VNCS), compared to pristine regolith. The subsurface soils sampled from a depth of approximately 10 cm exhibit a slightly fresher spectral feature compared to the surficial soils. Our comparison revealed a rapid rate of space weathering at the lunar surface compared to the vertical overturn. Compared to older iron-rich soils, the CE-5 soils have a larger reflectance but similar UV-vis ratios. The UV-vis ratio alone could not accurately predict the TiO2 content of all mare basalts. The CE-5 samples provide a new ground truth for estimating the TiO2 content of young lunar basalts, which have the largest uncertainty in TiO2 content, as estimated from spectral parameters. We find that the samples returned by the CE-5 mission represent disturbed soils and that they exhibit significantly fresher characteristics compared to pristine regolith, a fact that should be kept in mind when using samples as ground truth for remote sensing research.

Predicting the Surface Soil Texture of Cultivated Land via Hyperspectral Remote Sensing and Machine Learning: A Case Study in Jianghuai Hilly Area

Soil reflectance spectra and hyperspectral images have great potential to monitor and evaluate soil texture in large-scale scenarios. In hilly areas, sand, clay, and silt have similar spectral characteristics in visible, near-infrared, and short-wave infrared (VNIR-SWIR) reflection spectra. Soil texture spectra belong to mixed spectra despite some differences in particle size, mineral composition, and water content, making their distinction difficult. The accurate identification of the content within different particle sizes is difficult as it involves capturing spectral reflection features. Therefore, this study aimed to predict soil texture content through machine learning and unmixing the soil texture’s spectra while also comparing their respective modelling performances. Taking typical cultivated land in the Jianghuai hills as an example, the GaoFen-5 Advanced Hyperspectral Imaging (GF-5 AHSI) laboratory spectra of soil samples were used to predict sand, silt, and clay particle contents using partial least squares regression (PLSR) and convolutional neural networks (CNNs). The entire spectra of VNIR-SWIR regions were smoothed, and the dimensions were reduced via principal component analysis (PCA). The prediction models of sand, silt, and clay particle content were constructed, and inversion maps were generated using AHSI. The results showed that the PCA-CNN model achieved a higher prediction precision than the PCA-PLSR in both ASD and GF-5 data. Clay content exhibited the highest predictive performance with a coefficient of determination (R2) of 0.948 and 0.908 and a root mean square error (RMSE) of 26.51 g/kg and 31.24 g/kg, respectively, which represented a 39.0% and 79.8% increase in R2 and a 57% and 57.1% decrease in RMSE compared to that of the PCA-PLSR. This method indicates that the PCA-CNN model can effectively achieve nonlinear interactions between multiple spectral components and better model and fit spectral mixing processes; moreover, it provides an alternative method for investigating the spatial distribution of soil texture.

Development of the Ames Global Hyperspectral Synthetic Data Set: Surface Bidirectional Reflectance Distribution Function

AbstractThis study introduces the Ames Global Hyperspectral Synthetic Data set (AGHSD), in particular the surface bidirectional reflectance distribution function (BRDF) product, to support the NASA Surface Biology and Geology (SBG) mission development. The data set is generated based on the corresponding multispectral BRDF products from NASA's MODIS satellite sensor. Based on theories of radiative transfer in vegetation canopies, we derive a simple but robust relationship that indicates that the hyperspectral surface BRDF can be accurately approximated as a weighted sum of the soil surface reflectance, the leaf single albedo, and the canopy scattering coefficient, where the weights or coefficients are spectrally invariant and thus readily estimated from the multispectral MODIS products. We validate the algorithm with simulations by a Monte Carlo Ray Tracing model and find the results highly consistent with the theoretic derivation. Using reflectance spectra of soil and vegetation derived from existing spectral libraries, we apply the algorithm to generate the AGHSD BRDF product at 1 km and 8‐day resolutions for the year of 2019. The data set is biogeochemically and biogeophysically coherent and consistent, and serves the goal to support the SBG community in developing sciences and applications for the future global imaging spectroscopy mission.

Comparison of laboratory and in situ reflectance spectra of Chang’e-5 lunar soil

Context.Reflectance spectra provide essential information on the mineralogical composition of a planetary surface. However, the spectral characteristics of lunar soil are significantly influenced by its photometric properties, coupled with space weathering and particle size.Aims.China’s Chang’e-5 (CE5) mission returned lunar soil samples and obtained in situ spectra of the sampling site, enabling us to compare the laboratory and in situ analyses of the same sample.Methods.In this study, we measured the reflectance spectra of the bulk CE5 soil and two size fractions (<45 and 45–355 μm) at various phase angles (41.3° to 101.3°).Results.The photometric properties of the CE5 samples exhibit back scattering, whereas an in situ measurement appears as forward scattering, indicating that in situ photometric experiments are always necessary for spectral exploration on the Moon. In addition, the scattering properties of the <45-μm fraction are closer to the in situ spectral data, suggesting that the finer fraction could be more representative of pristine lunar soil. The maturity of CE5 soil is estimated to be submature to mature based on the spectral ratio between 750 nm and 950 nm.

Influence of arable land location incorporating its roughness on blue-sky albedo variation

This paper quantifies the albedo variation in global bare, arable land, as one of the components of the Earth's land surface, that occurs over relatively short periods of the year but over a large total area. The study focused on the impact of the location of the land's dominant soil units, incorporating their roughness in their diurnal albedo (α) variation on the shortest (SDOY) and longest (LDOY) days of the year, and on their mean diurnal albedo (αd) values throughout the year under clear-sky conditions. Specialized software was used to generate the aforementioned soil albedo values on the basis of soil reflectance spectra stored in a soil database. It was assumed that the air-dried soil surfaces being tested would present extremely different roughness states: very high, corresponding to those shaped by a plow (Pd), and very low, corresponding to a smoothing harrow (Hs). It was found that the farther the surfaces were from the equator, the higher the minimum α values would be on the SDOY, and the lower they would be on the LDOY. It was determined that the relative difference (AVαd) between the highest and lowest values of αd for the studied surfaces located within the latitude range of ±15° would be close to 0%. For latitudes within ±20°–±30°, the AVαd values would be 6 and 5% for surfaces shaped by Pd and Hs, respectively. For those at about 65°, AVαd values would even reach 63% and 54%, respectively. The α values of soil surfaces generated from many thousands of reflectance spectra collected in soil databases around the world, and taking into account their roughness, may be used in the future to model Earth's climate changes.

Hyperspectral Inversion of Soil Carbon and Nutrient Contents in the Yellow River Delta Wetland

Hyperspectral inversion techniques can facilitate soil quality monitoring and evaluation. In this study, the Yellow River Delta Wetland Nature Reserve was used as the study area. By measuring and analyzing soil samples under different vegetation types and collecting soil reflectance spectra, the relationships between vegetation types, soil depth, and the changes in soil total carbon (TC), total nitrogen (TN), and total phosphorus (TP) contents were assessed. The spectral data set was changed by spectral first derivative processing and division of the sample set according to vegetation type. The correlation between soil carbon, nitrogen, and phosphorus contents, and soil spectra was also analyzed, sensitive bands were selected, and the partial least-squares (PLS) method, support vector machine (SVM) method, and random forest (RF) model were used to establish the inversion model based on the characteristic bands. The optimal combination of spectral transformation, sample set partitioning, and inversion model was explored. The results showed significant differences (p < 0.05) in soil TC, TN, and TP contents under reed and saline alkali poncho vegetation, but not between soil element contents under different stratifications of the same plant species. The first derivative reflectance had higher correlation coefficients with soil TC, TN, and TP contents compared with the original reflectance, while the sensitive bands and quantities of the three elements differed. The division of the sample sets according to vegetation type and the first derivative treatment can improve the prediction accuracy of the model. The best combination of sample set plus FD plus RF for TC, TN, and TP in reed soil and sample set plus FD plus SVM for TC, TN, and TP in saline alkali pine soil provides technical support to further improve the prediction accuracy of TC, TN, and TP in wetland soil.

Reflectance study of ice and Mars soil simulant associations—II. CO[formula omitted] and H[formula omitted]O ice

We measure the visible and near-infrared reflectance of icy analogues of the Martian surface made of CO2 ice associated in different ways with H2O ice and the regolith simulant JSC Mars-1. Such experimental results obtained with well-controlled samples in the laboratory are precious to interpret quantitatively the imaging and spectral data collected by various Mars orbiters, landers and rovers. Producing and maintaining well-characterised icy samples while acquiring spectro-photometric measurements is however challenging and we discuss some of the difficulties encountered in preparing and measuring our samples. We present the results in the form of photometric and spectral criteria computed from the spectra and plotted as a function of the composition and physical properties of the samples. Consistent with previous studies, we find that when intimately mixed with other materials, including water ice, CO2 ice becomes rapidly undetectable due to its low absorptivity. As low as 5 wt% of fine-grained H2O ice is enough to mask entirely the signatures of CO2. Similarly, sublimation experiments performed with ternary mixtures of CO2 ice, H2O ice and JSC Mars-1 show that water, even when present as a minor component (3 wt%), determines the texture and evolution of the mixtures. We assess the ability of various combinations of spectral parameters to identify samples with H2O, CO2, JSC Mars-1, or various mixtures from their reflectance and orient our study to helping interpret ice and soil reflectance spectra from the Martian surface. From the laboratory spectra, we simulate the colour signal generated by the CaSSIS instrument to allow for direct comparisons with results from this instrument and provide to databases the necessary spectral data to perform the same operations with other instruments.

Accuracy and Reproducibility of Laboratory Diffuse Reflectance Measurements with Portable VNIR and MIR Spectrometers for Predictive Soil Organic Carbon Modeling.

Soil spectroscopy in the visible-to-near infrared (VNIR) and mid-infrared (MIR) is a cost-effective method to determine the soil organic carbon content (SOC) based on predictive spectral models calibrated to analytical-determined SOC reference data. The degree to which uncertainty in reference data and spectral measurements contributes to the estimated accuracy of VNIR and MIR predictions, however, is rarely addressed and remains unclear, in particular for current handheld MIR spectrometers. We thus evaluated the reproducibility of both the spectral reflectance measurements with portable VNIR and MIR spectrometers and the analytical dry combustion SOC reference method, with the aim to assess how varying spectral inputs and reference values impact the calibration and validation of predictive VNIR and MIR models. Soil reflectance spectra and SOC were measured in triplicate, the latter by different laboratories, for a set of 75 finely ground soil samples covering a wide range of parent materials and SOC contents. Predictive partial least-squares regression (PLSR) models were evaluated in a repeated, nested cross-validation approach with systematically varied spectral inputs and reference data, respectively. We found that SOC predictions from both VNIR and MIR spectra were equally highly reproducible on average and similar to the dry combustion method, but MIR spectra were more robust to calibration sample variation. The contributions of spectral variation (ΔRMSE < 0.4 g·kg−1) and reference SOC uncertainty (ΔRMSE < 0.3 g·kg−1) to spectral modeling errors were small compared to the difference between the VNIR and MIR spectral ranges (ΔRMSE ~1.4 g·kg−1 in favor of MIR). For reference SOC, uncertainty was limited to the case of biased reference data appearing in either the calibration or validation. Given better predictive accuracy, comparable spectral reproducibility and greater robustness against calibration sample selection, the portable MIR spectrometer was considered overall superior to the VNIR instrument for SOC analysis. Our results further indicate that random errors in SOC reference values are effectively compensated for during model calibration, while biased SOC calibration data propagates errors into model predictions. Reference data uncertainty is thus more likely to negatively impact the estimated validation accuracy in soil spectroscopy studies where archived data, e.g., from soil spectral libraries, are used for model building, but it should be negligible otherwise.

Estimation of soil copper content based on fractional-order derivative spectroscopy and spectral characteristic band selection

Hyperspectral remote sensing is a rapid and nondestructive method to estimate the soil copper content. However, before establishing the spectral estimation model, it is crucial to preprocess the hyperspectral data to eliminate noise and highlight the spectral response characteristics of copper. The two commonly used spectral preprocessing approaches, i.e., the first- and second-order derivatives, may not provide sufficient information on the copper in the soil spectra. Therefore, this study investigates the potential of using the fractional-order derivative (FOD) of the spectra (FOD spectra) for estimating the soil copper content. A total of 170 soil samples were collected, and the soil reflectance spectra were measured outdoors using an ASD FieldSpec3 portable spectrometer. The soil copper content was obtained by chemical analysis in the laboratory. A quantitative estimation model of the soil copper content was established by combining the FOD spectra with different orders and using the partial least squares (PLS) method. The results revealed that the accuracy and prediction ability of the models using different orders of the FOD spectra varied significantly. The model using the 0.8-order FOD spectra performed the best, and the coefficient of determination (R2) and the ratio of the performance to deviation (RPD) of the validation set were 0.6416 and 1.63, respectively. The performance of the model using three characteristic bands (2365.5 nm and 2375.5 nm of the 0.9-order derivatives and 864.5 nm of the 1.1-order derivatives) provided significantly better performance than utilizing all wavelength bands from 400 to 2400 nm. This model provided the optimum predictive ability (R2: 0.6552 vs. 0.6416, RPD: 1.65 vs. 1.63) and was straightforward, requiring only three bands. These results show that it is feasible and practical to establish an accurate and rapid estimation model of the soil copper content using FOD spectra.

MARMIT-2: An improved version of the MARMIT model to predict soil reflectance as a function of surface water content in the solar domain

This paper presents MARMIT-2, a radiative transfer model that predicts the spectral reflectance of soils in the solar domain (0.4–2.5 μm) as a function of their surface moisture. This is an improved version of MARMIT (multilayer radiative transfer model of soil reflectance) that represents a wet soil as a dry soil topped with a thin layer of liquid water. The changes brought in this article concern the mixing of the spectral reflectance of the dry and wet soil areas, the transmission of diffuse light in the water layer, and the inclusion of soil particles in the water layer. Wet soil reflectance is now expressed in terms of dry soil reflectance and three free parameters: the thickness of the water layer, the surface fraction of the wet soil, and a new parameter, the volume fraction of soil particles in the water layer. With more accurate physical modeling, MARMIT-2 simulates soil spectral reflectance with better accuracy than MARMIT. In particular, the fit of the soil reflectance spectra is much better for high water contents, both in the visible range (0.4–0.7 μm) and in the water absorption bands around 1.45 μm and 1.95 μm. The average root mean square error between measured and predicted reflectance obtained on a set of 225 soil samples is about 0.8% with MARMIT-2 versus 1.8% with MARMIT.

Estimating near-infrared reflectance of vegetation from hyperspectral data

Disentangling the individual contributions from vegetation and soil in measured canopy reflectance is a grand challenge to the remote sensing and ecophysiology communities. Since Solar Induced chlorophyll Fluorescence (SIF) is uniquely emitted from vegetation, it can be used to evaluate how well reflectance-based vegetation indices (VIs) can separate the vegetation and soil components. Due to the residual soil background contributions, Near-infrared (NIR) reflectance of vegetation (NIRv) and Difference Vegetation index (DVI) present offsets when compared to SIF (i.e., the value of NIRv or DVI is non-zero when SIF is zero). In this study, we proposed a simple framework for estimating the true NIR reflectance of vegetation from Hyperspectral measurements (NIRvH) with minimal soil impacts. NIRvH takes advantage of the spectral shape variations in the red-edge region to minimize the soil effects. We evaluated the capability of NIRvH, NIRv and DVI in isolating the true NIR reflectance of vegetation using the data from both the model-based simulations and Hyperspectral Plant imaging spectrometer (HyPlant) measurements. Benchmarked by simultaneously measured SIF, NIRvH has the smallest offset (0–0.037), as compared to an intermediate offset of 0.047–0.062 from NIRv, and the largest offset of 0.089–0.112 from DVI. The magnitude of the offset can vary with different soil reflectance spectra across spatio-temporal scales, which may lead to bias in the downstream NIRv-based photosynthesis estimates. NIRvH and SIF measurements from the same sensor platform avoided complications due to different geometry, footprint and time of observation across sensors when studying the radiative transfer of reflected photons and SIF. In addition, NIRvH was primarily determined by canopy structure rather than chlorophyll content and soil brightness. Our work showcases that NIRvH is promising for retrieving canopy structure parameters such as leaf area index and leaf inclination angle, and for estimating fluorescence yield with current and forthcoming hyperspectral satellite measurements.

Minimising the effect of moisture on soil property prediction accuracy using external parameter orthogonalization

Moisture is one of the most important factors affecting soil reflectance spectra. However, provisional and dynamic behavior of soil moisture (SM) in salty soils reduce the capability of field spectrometry in the estimation of soil properties. This study aims minimising the effect of SM on the accuracy of visible and near-infrared (VNIR) spectra estimation of clay, calcium carbonate (CaCO3), and organic carbon (OC) content in semi-arid soils by adopting External parameter orthogonalization (EPO). Spectral reflectance of disturbed soil samples was measured for seven moisture contents (i.e. air dried, 6%, 12%, 18%, 24%, 30%, and 36%) and five levels of electerical conductivity (EC) (i.e. < 1, 4, 8, 12 and 16 dS/m). The EPO algorithm was customized for four textural classes. The performances of EPO algorithm was evaluated through partial least squares–backpropagation neural network (PLS–BPNN). The optimum number of components for the EPO matrix was determined to be four. Results showed that geometric parameters (area, depth, and width) of diagnostic absorption features (AF) in 550 nm, 2200 nm, and 2340 nm were affected by SM, and the interaction between SM and reflectance was not completely equivalent through different texture classes. The EPO correction showed an improvement of prediction accuracy of clay (RPIQ improved from 1.82 to 2.93), CaCO3 (RPIQ improved from 1.96 to 2.73), and OC (RPIQ improved from 1.94 to 3.44). Also better results have been gained by the customization of EPO for different texture classes. The performances of the EPO algorithm for clay and OC modeling dropped by increasing EC. This suggests that further studies are required to develop a method for eliminating the effects of the external parameters caused by increased salt levels in the soil.

Monitoring the soil copper pollution degree based on the reflectance spectrum of an arid desert plant

Visible and near-infrared reflectance spectroscopy offers a rapid, inexpensive, and environmentally friendly method for monitoring copper pollution in the soil. However, the application of this approach in vegetation-covered areas is still a challenge due to interference from plants, making it difficult to acquire soil reflectance spectra. To address this problem, this study assesses whether the reflectance spectrum of a widely distributed arid desert plant (Seriphidium terrae-albae) can be used to rapidly evaluate copper pollution in the soil. A pot experiment was conducted for five months from April to September 2019. The reflectance spectra of the plants were measured in June, July, and August 2019 using an ASD Fieldspec3 spectrometer. Each month, the five vegetation indexes with the highest correlation with the evaluation value of the copper pollution degree were input into an extreme learning machine (ELM) to build a model to monitor the degree of copper pollution in the soil. The results showed that the model could quickly evaluate the degree of copper pollution, but the accuracy varied widely among the calculated vegetation indexes depending on the month when the spectral data were extracted. The model constructed by selecting ten vegetation indexes composed of plant spectra collected in June and July provides high recognition accuracy, reaching 89.02%. Only seven bands were needed due to the model's low complexity, which means that it has great potential to be applied to remote sensing images to establish a real-time monitoring system to detect copper pollution in the soil. This study proposed a simple and rapid method for monitoring copper pollution in soil using plant spectra, and this method could provide extremely valuable for soil protection and management in arid desert areas.

Water-absorption-trough dewatering machine for estimation of organic carbon in moist soil

Quantitative estimation of soil organic carbon (SOC) is essential for the study of the C cycle and global C storage. Soil spectroscopic technology provides a cost-effective and time-efficient method for SOC quantification and has been successfully used to determine SOC storage. However, the SOC estimation accuracy remains limited by other soil properties, particularly soil water. In this study, we proposed a new deep learning algorithm named the Water Absorption Trough Dewatering Machine (WATDM) to improve estimations of SOC from soil reflectance spectra and reduce the effect of soil water. Soil water and reflectance spectral data of soil samples were measured using spectrometry. Based on the soil water contents derived from the water absorption troughs around 1900 nm, the optimal WATDM model was obtained and treated as the final model of the WATDM method, which performed better than a multiple linear regression model based on moist soil samples. The findings of this study indicate that the WATDM method can improve the estimation accuracy of SOC content by reducing the effect of soil water and can be used as a valuable new methodology within the spectroscopic estimation of soil properties.

Near-UV and near-IR reflectance studies of lunar swirls: Implications for nanosize iron content and the nature of anomalous space weathering

We performed an analysis of spacecraft multispectral images for lunar swirls in order to gain an improved understanding of optical space weathering on the Moon and its causes. LROC WAC data provide information on the slope of the spectrum in the near-UV (NUV), as measured by the 321-nm/415-nm or 321-nm/360-nm reflectance ratios. Kaguya MI data were used to assess the near-infrared (NIR) continuum slope (1548-nm/749-nm reflectance ratio). Context for interpreting the spectral variations found in the remotely observed regions of the lunar surface is provided by laboratory reflectance spectra of lunar rocks and soils, as well as spectra for transparent silica gel analogs (Noble et al., 2007) containing different sizes and abundances of nanometer-sized iron (nsFe) particles. We gain additional insights into the spectral effects of sample maturity by considering the ferromagnetic resonance parameter (Is) values for mare and highland soils, as well as the number density of nsFe particles in the silica gels.We examined a set of three mare swirls (Reiner Gamma, Ingenii, and Mare Marginis) and three highland swirls (Airy, Descartes, and Gerasimovich). The NIR continuum slopes of both mare and highland swirls are shallower than those of the nearby normal mature regolith. Bright swirl surfaces have higher NIR slopes than normal fresh material of the same albedo. The NUV ratios within mare swirls are lower than in the mature background, but for highland swirls, the NUV ratios are approximately the same as the mature background. We do not see definitive evidence for "over-maturation" (excessive darkening and reddening beyond that found in the normal background surfaces) in dark lanes at the swirls we examined, although saturation of weathering effects at a high-iron location like Reiner Gamma could prevent over-maturation from appearing – even if enhanced solar-wind bombardment related to deflection by local magnetic fields is taking place.Evaluation of the NIR character of swirls and comparison with lab spectra of lunar soils and nsFe-bearing silica gel analogs leads to the conclusion that swirl materials contain abundances of nsFe that are lower than that of normal non-swirl background surfaces; the nsFe content of swirls corresponds to immature (though not pristine) or submature soils. However, the size distribution of nsFe in swirls is anomalous compared with normal lunar surfaces, with a deficiency in the smaller size range (< ~15 nm), as inferred from the NUV character of swirls. Because the flux of solar-wind ions reaching the surface in swirls is attenuated by shielding by crustal magnetic fields, we conclude that solar-wind exposure is the primary agent for production of small nsFe in normal lunar space weathering. Micrometeoroid bombardment, which is unimpeded by the presence of magnetic fields, is mainly responsible for production of larger nsFe in space weathering.

Hyperspectral inversion of soil heavy metals in Three-River Source Region based on random forest model

Hyperspectral remote sensing technology has considerable research value in monitoring and evaluating soil heavy metal pollution. In this study, the Three-River Source Region was taken as the study area. The occurrence relationship of six heavy metals in soil, such as Mn, Cu, Zn, Pb, Cr, Ni, with soil organic matter, clay minerals, and iron-manganese oxides, was studied through the determination and analysis of soil samples and the collection of soil reflectance spectrum. Spectral transformation was carried out by first derivative, second derivative, inverse-log, continuum removal and multiple scattering correction of the spectrum. The correlation between soil heavy metal content and soil spectrum was analyzed to select the characteristic band, and partial least squares (PLS) method, support vector machine (SVM) method and random forest (RF) model were used to build inversion model based on characteristic band. Then the best combination of spectral transformation and inversion model were explored. The results showed that Pb contents were the twice of the background in Qinghai province. The combination spectrum processing method can improve the correlation between spectrum and heavy metals. The location and quantity of characteristic bands of six heavy metals are different. The accuracy of RF was significantly better than that of SVM and PLS for all six heavy metal (i.e. pb: R2RF = 0.83, R2SVM = 0.62, R2PLS = 0.18), and the model effective of soil properties in non-polluted sites were reliable (i.e. clay: R2RF = 0.93, R2SVM = 0.87, R2PLS = 0.74). This study can provide technical support for the larger-scale monitoring of soil heavy metal content and heavy metal pollution assessment.