Soil Reflectance Research Articles

Effects of Sample Handling on Visible and Near-Infrared Soil Reflectance

Soil reflectance is a cumulative attribute determined by interactions between light (photons) and the physical, chemical, and biological properties of soil. Soil properties such as organic matter, moisture, mineral oxide contents, soil texture, and surface roughness all influence soil reflectance at unique wavelengths. Many standard sample preparation techniques are designed to alter soil properties, so as to homogenize samples to improve the consistency of reflectance data collected. This study aims to quantify the effects of one standard sample preparation activity, drying, and repeated wetting–drying cycles on visible–near-infrared soil reflectance, by collecting reflectance data from nine samples which were dried then wetted for a total of three times. This study demonstrates the major, permanent effects of a drying and wetting cycle on soil reflectance, and then presents a model which can be used to correct for these effects. These results have direct implications for remote sensing activities, soil libraries, and soil spectral libraries. These results show that without correction, data from soil spectral libraries, spectral data collected from stored samples, and spectral data transferred between studies, have limited utility for characterizing soils as they exist in the field. Soil spectral data collected in the lab requires correction before it may be used to predict soil properties in the field, as standard sample handling procedures change intrinsic soil properties and introduce systematic error into these data.

Exploiting Soil and Remote Sensing Data Archives for 3D Mapping of Multiple Soil Properties at the Swiss National Scale

Soils play a central role in ecosystem functioning, and thus, mapped soil property information is indispensable to supporting sustainable land management. Digital Soil Mapping (DSM) provides a framework to spatially estimate soil properties. However, broad-scale DSM remains challenging because of non-purposively sampled soil data, large data volumes for processing extensive soil covariates, and high model complexities due to spatially varying soil–landscape relationships. This study presents a three-dimensional DSM framework for Switzerland, targeting the soil properties of clay content (Clay), organic carbon content (SOC), pH value (pH), and potential cation exchange capacity (CECpot). The DSM approach is based on machine learning and a comprehensive exploitation of soil and remote sensing data archives. Quantile Regression Forest was applied to link the soil sample data from a national soil data base with covariates derived from a LiDAR-based elevation model, from climate raster data, and from multispectral raster time series based on satellite imagery. The covariate set comprises spatially multiscale terrain attributes, climate patterns and their temporal variation, temporarily multiscale land use features, and spectral bare soil signatures. Soil data and predictions were evaluated with respect to different landcovers and depth intervals. All reference soil data sets were found to be spatially clustered towards croplands, showing an increasing sample density from lower to upper depth intervals. According to the R2 value derived from independent data, the overall model accuracy amounts to 0.69 for Clay, 0.64 for SOC, 0.76 for pH, and 0.72 for CECpot. Reduced model accuracies were found to be accompanied by soil data sets showing limited sample sizes (e.g., CECpot), uneven statistical distributions (e.g., SOC), and low spatial sample densities (e.g., woodland subsoils). Multiscale terrain covariates were highly influential for all models; climate covariates were particularly important for the Clay model; multiscale land use covariates showed enhanced importance for modeling pH; and bare soil reflectance was a major driver in the SOC and CECpot models.

OptiSAIL: A system for the simultaneous retrieval of soil, leaf, and canopy parameters and its application to Sentinel-3 Synergy (OLCI+SLSTR) top-of-canopy reflectances

This paper describes the selected algorithm for the ESA climate change initiative vegetation parameters project. Multi- and hyper-spectral, multi-angular, or multi-sensor top-of-canopy reflectance data call for an efficient generic retrieval system which can improve the consistent retrieval of standard canopy parameters as albedo, Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (fAPAR) and their uncertainties, and exploit the information to retrieve additional parameters (e.g. leaf pigments). We present a retrieval system for canopy and sub-canopy parameters (OptiSAIL), which is based on a model comprising SAIL (canopy reflectance), PROSPECT-D (leaf properties), TARTES (snow properties), a soil model (soil reflectance anisotropy, moisture effect), and a cloud contamination model. The inversion is gradient based and uses codes created by Automatic Differentiation. The full per pixel covariance-matrix of the retrieved parameters is computed. For this demonstration, single observation data from the Sentinel-3 SY_2_SYN (synergy) product is used. The results are compared with the MODIS 4-day LAI/fAPAR product and PhenoCam site photography. OptiSAIL produces generally consistent and credible results, at least matching the quality of the technically quite different MODIS product. The system is computationally efficient with a rate of 150 pixel s−1 (7 ms per pixel) for a single thread on a current desktop CPU using observations on 26 bands. Not all of the model parameters are well determined in all situations. Significant correlations between the parameters are found, which can change sign and magnitude over time. OptiSAIL appears to meet the design goals and puts real-time processing with this kind of system into reach.

Improving the Estimation Accuracy of Soil Organic Matter Content Based on the Spectral Reflectance from Soils with Different Grain Sizes

Accurate and rapid estimation of soil organic matter (SOM) content is of great significance for advancing precision agriculture. Compared with traditional chemical methods, the hyperspectral estimation is superior in rapidly estimating SOM content. Soil grain size affects soil spectral reflectance, thereby affecting the accuracy of hyperspectral estimation. However, the appropriate soil grain size for the hyperspectral analysis is nearly unknown. This study propose a best hyperspectral estimation method for determining SOM content of farmland soil in the Ibinur Lake Irrigation Area (ILIA) of the northwest arid zones of China. The original spectral reflectance of the 20-mesh (0.85 mm) and 60-mesh (0.25 mm) sieved soil were obtained, and the feature wavebands were selected using five types of spectral transformations. Then, hyperspectral estimation models were constructed based on the partial least squares regression (PLSR), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) models. Results show that the SOM content had relatively higher correlation coefficient with spectral reflectance of the 0.85 mm sieved soil than that of the 0.25 mm sieved soil. The transformation of original spectral reflectance of soil effectively enhanced the spectral characteristics related to SOM content. Soil grain size obviously affected spectral reflectance and the accuracy of hyperspectral estimation models. The overall stability and estimation accuracy of RF model was significantly higher compared with the PLSR, SVM, and XGBoost. Finally, the RF model combined with the root mean first-order differentiation (RMSFD) of spectral reflectance of the 0.85 mm sieved soil (R2 = 0.82, RMSE = 2.37, RPD = 2.27) was identified as the best method for estimating SOM content of farmland soil in the ILIA.

Downscaling canopy photochemical reflectance index to leaf level by correcting for the soil effects

The photochemical reflectance index (PRI) is a promising remote sensing signal for monitoring vegetation physiology. Variations in leaf PRI are usually attributed to either the energy-dependent xanthophyll cycle or the carotenoid-chlorophyll ratio, both indicative of leaf physiology. However, canopy PRI is subject to soil, canopy structure, and incident and viewing angles, and thus has a weaker and more complicated relationship with vegetation photosynthetic activity or leaf pigment composition. Therefore, downscaling canopy PRI to the leaf level is essential for accurate remote sensing of vegetation physiology using PRI. An earlier investigation (P.Yang, RSE, 279, 113,133, 2022) illustrates that structural and angular variations in canopy PRI primarily result from varying degrees of soil interference. In this study, a soil correction method is proposed to mitigate the soil effects on top-of-canopy (TOC) reflectance at the PRI bands. The soil effects on TOC reflectance at 531 nm and 570 nm are respectively estimated as R531soil×R675/R675soil and R570soil×R675/R675soil, where R675 is TOC red reflectance, Rλsoil soil reflectance at wavelengthλ. R675/R675soil approximates the fraction of the observed sunlit soil, as leaves are nearly black at 675 nm due to strong absorption of chlorophyll, and R675 is mainly contributed from soil. To assess the effectiveness of the soil correction method, a wheat field dataset, a corn field dataset, and a simulated dataset, were utilized. The soil-adjusted and the original canopy PRI were compared with the leaf PRI for the real and synthetic scenarios that had various soil brightness, leaf chlorophyll content and canopy structure. Both the field and numerical experiments demonstrate that, for the canopies with low vegetation coverage and substantial soil contamination, the original canopy PRI was largely different from the leaf PRI, displaying substantial structural and angular dependence. In comparison, the soil-adjusted canopy PRI was more closely aligned with the PRI observed in the sunlit leaves in all three datasets. This study shows that accounting for the soil effects with TOC red reflectance allows downscaling canopy PRI to the leaf level. The soil-adjusted canopy PRI contributes to remote sensing of the xanthophyll cycle or the carotenoid-chlorophyll ratio from canopy PRI.

Monitoring Land Management Practices Using Vis–NIR Spectroscopy Provides Insights into Predicting Soil Organic Carbon and Limestone Levels in Agricultural Plots

This study aimed to establish sound relationships between soil properties of agricultural land in central Spain and their spectral attributes to contribute to deriving an indicator for sustainable farm management. Sixteen plots, managed under various conditions, eight with traditional tillage and eight with other alternative managements, were selected to gather soil samples representing three predominant soil orders (Leptosols, Cambisols, and Luvisols). Soil sampling was conducted from depths ranging from 0 to 30 cm (0–50; 5–10, 10–20, and 20–30 cm), ensuring a broad spectrum of sample variability across different times and locations. The reflectance of soil 64 soil samples was measured within the range of 400 to 900 nm, and the corresponding concentrations of soil organic carbon and majoritarian minerals, calcium carbonate, quartz, phyllosilicates, K-Feldspar, and plagioclase were determined for each sample. Partial least squares analysis was employed to construct prediction models using a calibration dataset comprising 66% of randomly selected samples. The remaining 33% of samples were utilized for model validation. The prediction models for the measured soil chemical properties yielded R2 values ranging from 0.14 to 0.79. Only SOC and limestone provided accurate prediction models. These findings hold promise for developing a soil health indicator tailored for site-specific crop management. However, the complex composition of soil organic carbon and calcium carbonate in certain soils underscores the importance of careful interpretation and validation of remote sensing data, as well as the need for advanced modeling approaches that can account for the interactions between multiple soil constituents.

High-resolution soil erosion mapping in croplands via Sentinel-2 bare soil imaging and a two-step classification approach

Erosion-induced lateral soil redistribution leads to spatially heterogenous soil composition, which can be captured through the distinctive spectral reflectance of soils under varying levels of erosion influence. This points to the potential of using remotely sensed soil spectra to detect severe erosion and deposition hotspots in exposed croplands and, importantly, further differentiate the intra-class spectral variability of moderate erosion that often occupies the largest proportion. Here, we aim to develop a two-step erosion classification and mapping approach based on multitemporal compositing of Sentinel-2 bare soil images of a typical agricultural region (11,500 km2) of northeast China. A random forest classifier was firstly trained against the ground-truth data derived from very high resolution (VHR) imagery in Google Earth, with an overall accuracy of 91 % that allowed for clear delineation of severe erosion and deposition areas based on their distinct topographic and spectral features particularly in the red and red-edge bands. In the second step, the remaining area of moderate erosion (60.30 %) was further differentiated using Iterative Self-Organizing cluster unsupervised classification to yield a five-class soil erosion map at 10 m spatial resolution. The accuracy of the predicted map was successfully validated by independent Caesium-137 (137Cs) and soil organic carbon observations at catchment and regional scales, as revealed by significant inter-class differences in soil redistribution rates estimated from 137Cs inventory. The severe erosion class had a soil loss rate of 5.5 mm yr−1, suggesting that previous assessments have underestimated erosion severity. The spatial accordance of crop growth with soil erosion intensity, particularly in localized settings, further highlighted the potential of bare soil imaging for mapping the spatiotemporal development of soil erosion and its response to targeted sustainable cropland management efforts.

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.

Estimation of soil moisture and organic matter content in saline alkali farmland by using CARS algorithm combined with covariates

Rapid acquisition of the data of soil moisture content (SMC) and soil organic matter (SOM) content is crucial for the improvement and utilization of saline alkali farmland soil. Based on field measurements of hyperspectral reflectance and soil properties of farmland soil in the Hetao Plain, we used a competitive adaptive reweighted sampling algorithm (CARS) to screen sensitive bands after transforming the original spectral reflectance (Ref) into a standard normal variable (SNV). Strategies Ⅰ, Ⅱ, and Ⅲ were used to model the input variables of Ref, Ref SNV, Ref-SNV+ soil covariate (SC), and digital elevation model (DEM). We constructed SMC and SOM estimation models based on random forest (RF) and light gradient boosting machine (LightGBM), and then verified and compared the accuracy of the models. The results showed that after CARS screening, the sensitive bands of SMC and SOM were compressed to below 3.3% of the entire band, which effectively optimized band selection and reduced redundant spectral information. Compared with the LightGBM model, the RF model had higher accuracy in SMC and SOM estimation, and the input variable strategy Ⅲ was better than Ⅱ and Ⅰ. The introduction of auxiliary variables effectively improved the estimation ability of the model. Based on comprehensive analysis, the coefficient of determination (Rp2), root mean square error (RMSE), and relative analysis error (RPD) of the SMC estimation model validation based on strategy Ⅲ-RF were 0.63, 3.16, and 2.01, respectively. The SOM estimation models based on strategy Ⅲ-RF had Rp2, RMSE, and RPD of 0.93, 1.15, and 3.52, respectively. The strategy Ⅲ-RF model was an effective method for estimating SMC and SOM. Our results could provide a new method for the rapid estimation of soil moisture and organic matter content in saline alkali farmland.

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.

Using local ensemble models and Landsat bare soil composites for large-scale soil organic carbon maps in cropland

National soil organic carbon (SOC) maps are essential to improve greenhouse gas accounting and support climate-smart agriculture. Large-scale SOC models based on wall-to-wall soil information from remote sensing remain a challenge due to the high diversity of natural soil conditions and the difficulty of accounting for the spatial location of the soil samples. In this study, we tested if the implementation of local ensemble models (LEM) can be used to improve the SOC predictions from Landsat-based soil reflectance composites (SRC) for Germany. For this, we divided the research area into 30 times 30 km tiles and calculated local generalized linear models (GLM) based on random, nearby observations. Based on the GLMs, local SOC maps were predicted and aggregated using a moving window approach. The local variable importance was analyzed to identify spatial dependencies in the correlation between the SRC and SOC. For the final SOC map, a Random Forest (RF) model was trained using the aggregated local SOC predictions, the SRC, and a full set of training samples from the agricultural soil inventory. The results show that the LEM was able to improve the accuracy (R2 = 0.68; RMSE = 5.6 g kg−1), compared to the maps based on a single, global model (R2 = 0.52; RMSE = 6.8 g kg−1). The local variable importance of the spectral bands showed clear spatial patterns throughout the research area. Differences can be explained by the local soil conditions, influencing the correlation between SOC and the spectral properties. Compared to the widely adopted integration of distance covariates such as geographical coordinates, the LEM was able the reduce the spatial autocorrelation to a greater extent and to improve the prediction accuracy, especially for underrepresented SOC values. The LEM presents a new method to integrate spatial information and increase the interpretability of DSM models.

Hyperspectral Imaging of Soil and Crop: A Review

The remote sensing is one of the precision technologies, can be used to monitor and assess the target area or object such as soil, crop, and water. Hyperspectral imaging (HSI), also known as imaging spectrometry or hyperspectral remote sensing, is a combined technique of spectroscopy and imaging system for sensing spectral information of an area or object. It involves capturing images of an object using multiple distinct optical bands that cover a wide range of the electromagnetic spectrum (350-2500 nm). The hyperspectral bands are continuous, narrow, and contagious and contain hundreds and thousands of numbers. Hyperspectral remote sensing is particularly valuable for gathering precise and up-to-date information necessary for agricultural planning and precision farming. HSI technology is the employment of hyperspectral sensors aids in analyzing soil physical (bulk density, texture, water content), chemical (pH, EC, SOC, and macro and micro nutrients), biological (SOM) properties and helps to categorize different crop varieties, identify pests and diseases, and assess crop yield and water stress in plants. The spectral reflectance of soil is affected by its properties such as mineral composition (Fe oxides), organic matter, soil moisture, and texture. For example, the spectral reflectance will be more if soil has less organic matter. The chemical bonds of soil molecules interact with the electromagnetic spectrum, and produce distinct pattern of reflectance. But the data collected from hyperspectral imaging are required big storage due to its large amount of data and finding the most appropriate hyperspectral image classification algorithm is a challenging task. So, these problems should be solved in future and national soil spectral library is needed for calibration of models which helps for efficient use of hyperspectral imaging technology.

Relationships between Soil Moisture and Visible–NIR Soil Reflectance: A Review Presenting New Analyses and Data to Fill the Gaps

The ability to precisely monitor soil moisture is highly valuable in industries including agriculture and civil engineering. As soil moisture is a spatially erratic and temporally dynamic variable, rapid, cost-effective, widely applicable, and practical techniques are required for monitoring soil moisture at all scales. If a consistent numerical relationship between soil moisture content and soil reflectance can be identified, then soil spectroscopic models may be used to efficiently predict soil moisture content from proximal soil reflectance and/or remotely sensed data. Previous studies have identified a general decrease in visible–NIR soil reflectance as soil moisture content increases, however, the strength, best wavelengths for modelling, and domain of the relationship remain unclear from the current literature. After reviewing the relevant literature and the molecular interactions between water and light in the visible–NIR (400–2500 nm) range, this review presents new analyses and interprets new 1 nm resolution soil reflectance data, collected at >20 moisture levels for ten soil samples. These data are compared to the results of other published studies, extending these as required for further interpretation. Analyses of this new high-resolution dataset demonstrate that linear models are sufficient to characterise the relationship between soil moisture and reflectance in many cases, but relationships are typically exponential. Equations generalising the relationship between soil MC and reflectance are presented for a number of wavelength ranges and combinations. Guidance for the adjustment of these equations to suit other soil types is also provided, to allow others to apply the solutions presented here and to predict soil moisture content in a much wider range of soils.

Causal relationship between seasonal variation in surface roughness and dust and sandstorm occurrence in arid and semi-arid Mongolia

In this study, the causal relationship between seasonal variations in surface conditions and DSS generation was investigated through the integrated use of the C-band backscatter coefficient (J 0) of the Sentinel 1 microwave satellite from the optical satellite sensor (Terra/MODIS). Because of the very high reflectance of the background soil in sparsely vegetated bare land, it has been difficult to rely solely on optical satellite data for accurate surface condition assessment in previous studies. Therefore, we used SAR and optical data to represent the spatial variability of backscatter coefficients for different surface conditions in the Gobi Desert and Steppe areas, and employed the C-band backscatter coefficient from the Sentinel-1 microwave satellite as a key parameter. The results show that in the Gobi Desert region, the correlation between the seasonal variation of backscatter coefficients and NDVI is highly significant, with a maximum correlation coefficient in fall (R=0.645, p<0.001) an inverse correlation in summer (R=-0.216, p<0.001), indicating little seasonal variation between NDVI and backscatter coefficients. In conclusion, surface roughness (σ0) is relatively low and stable from winter to the following spring and increases with plant growth in summer. In the Gobi Desert region, surface roughness changes little throughout the year, but in the steppe region, surface roughness increases with vegetation growth during the rainy season, which suppresses the generation of DSS.

Prediction of Open Woodland Transpiration Incorporating Sun-Induced Chlorophyll Fluorescence and Vegetation Structure

Transpiration (T) represents plant water use, while sun-induced chlorophyll fluorescence (SIF) emitted during photosynthesis, relates well to gross primary production. SIF can be influenced by vegetation structure, while uncertainties remain on how this might impact the relationship between SIF and T, especially for open and sparse woodlands. In this study, a method was developed to map T in riverine floodplain open woodland environments using satellite data coupled with a radiative transfer model (RTM). Specifically, we used FluorFLiES, a three-dimensional SIF RTM, to simulate the full spectrum of SIF for three open woodland sites with varying fractional vegetation cover. Five specific SIF bands were selected to quantify their correlation with field measured T derived from sap flow sensors. The coefficient of determination of the simulated far-red SIF and field measured T at a monthly scale was 0.93. However, when comparing red SIF from leaf scale to canopy scale to predict T, performance declined by 24%. In addition, varying soil reflectance and understory leaf area index had little effect on the correlation between SIF and T. The method developed can be applied regionally to predict tree water use using remotely sensed SIF datasets in areas of low data availability or accessibility.

Evaluation of the CRTM Land Emissivity Model over Grass and Sand Surfaces Using Ground-Based Measurements

Microwave surface emissivity is complex and variable, leading to increased difficulty in accurately retrieving atmospheric parameters and assimilating satellite microwave observations over land. The Community Radiative Transfer Model (CRTM) land emissivity model is a useful tool for providing microwave emissivity over complex surfaces. By combining the model with ground measurements from a mobile multi-surface observing system at the Xianghe site, China, the performance of the land emissivity model is evaluated over grass and sand surfaces. The simulated and measured emissivity agrees at both polarizations over the grassland surface but a more significant difference is observed at the horizontal polarization over the sand surface. To solve this problem, the Q/H module for soil reflectance roughness correction in the CRTM emissivity model was replaced with the Qp module for the sand surface. This results in a significant improvement in the horizontal polarization simulation, with the corresponding mean bias error (MBE) reducing from 0.08 in the Q/H module to less than 0.03. The adjustment demonstrates that the Qp module more effectively corrects the roughness effect on horizontally polarized emissivity for bare soil surfaces. For grassland, the CRTM emissivity model with the Q/H module demonstrates accurate simulations, showing its suitability for vegetated land surfaces.

UV Reflectance of Spacecraft Materials and Analog Soils: Implications for Bioburden Reductions on the Undersides of Mars Rovers

AbstractThe Mars Sample Return mission architecture will utilize three spacecraft to collect, cache, recover, launch, and return to Earth a diversity of regolith and rock samples. However, no comprehensive Mars Microbial Survival (MMS) model currently exists. As an initial effort in building an MMS model, we examined the UV reflectance of 15 spacecraft materials and seven Mars analog soils within the context of the Perseverance mission. Data were used to predict the times required to achieve one lethal dose (syn., Sterility Assurance Level [SAL]; def. as a bioburden reduction of −12 logs). Results suggest that a single SAL dosage of UVC was achieved on exposed surfaces on the upper deck of Perseverance within a few hours to a few sols post‐landing at Jezero Crater. The overall average for UVC reflectance from spacecraft materials was approximately 10%. The overall UVC reflectance from Mars analog soils was measured at 1.3%. The Adaptive Caching Assembly (ACA) on Perseverance is located on the forward edge of the underbelly of the spacecraft. Modeling of the accumulated UVC dosage for the ACA yielded a prediction of reaching one SAL for downward facing surfaces at 93 sols that receive “single‐bounce” UVC photons from the local terrain. The SAL increases to 930 sols if an additional “bounce” of the solar UV irradiation is required to reach a partially protected site in the ACA hardware. The current study is the first to report the UVC reflectance from a diversity of spacecraft materials.

Spectrometry for better soil fertility management in Abbay River Basin

AbstractThe aim of this study was to determine how soil bunds affected important soil characteristics based on the soil's reflectance in Aba Gerima, an Ethiopian midland of the Abbay River Basin. Soil samples were collected from 0 to 30 cm of the soil depth in 48 plots for soil texture, pH, organic carbon (OC), total nitrogen (TN), available phosphorus (av. P), and available potassium (av. K). Using a spectroradiometer, we evaluated the reflectance of soils that had been air‐dried, pulverized, and sieved. To locate and forecast the change in soil parameters, we employed regression modeling. We identified and predicted soil properties using the models, which were evaluated by the coefficient of determination (R2) and root mean square error. Soils from bunded plots had less reflection. Higher amounts of clay (61%), OC (2.30%), av. P (13.72 ppm), and av. K (116.55 mg/kg) were found in bunded plots. For calibration data, R2 was 0.93, 0.83, and 0.82 for clay, sand, and OC contents, respectively. Spectroradiometry could therefore complement conventional soil analysis. More study is needed to enhance spectroscopy's prediction performance.

Development of testing apparatus to measure carbon losses in soils

Soil is the largest terrestrial store of carbon in the world. It is estimated that soil contains approximately 2344 Gt of organic carbon globally [6]. Organic carbon is mainly sequestered in soils through plants [4], however, carbon can also be lost as a result of inappropriate land management, although other factors such as temperature and precipitation are also influential [7]. There have been efforts to monitor carbon losses from soil using soil reflectance [3], spatial and temporal data, [7], as well as more traditional method like sampling from controlled plots [1] although there is still a lack of understanding of the magnitude of carbon losses in soils and the mechanisms which drive organic carbon losses. This study will attempt to quantify organic carbon losses through dissolution and local erosion with the use of a newly developed testing apparatus. The schematic of the testing apparatus is shown in Figure 1.
 The testing apparatus will be stored in an outdoor location which ensure field conditions are captured and prevents any influence of laboratory atmospheric conditions. This project will develop a sampling strategy which will include regular testing to monitor overall changes on soil organic carbon using a number of plots with different plant species since plant species play a significant role through their root activity which determines the elevation of CO2 in soil [5]. The sampling strategy includes samples taken from each location shown in Section A-A in Figure 1. Soil samples are tested for bulk density, pH and total organic carbon through loss on ignition testing as well as other soil physical properties such as particle size distribution and permeability. Regular sampling and testing for soil physical properties will allow for correlations between a soil’s physical state and organic carbon losses to be determined. There is also some evidence that levels of organic carbon (and hence organic matter) can influence geotechnical properties of soils [2]. The sampling strategy also includes water samples taken from each run-off collection point as shown in Figure 1. Additional sampling will be undertaken following any periods of significant rainfall.
 The results from this study will be used in conjunction with field work in order to better understand the mechanisms which drive organic carbon loss in soil and hence provide recommendations on approaches to prevent carbon loss. Monitoring of chosen location over time to quantify carbon losses using a combination of sampling and testing and using Earth Observation data, similar to previous approaches taken in previous studies [8].