Soils provide a range of essential ecosystem services for sustaining life, including climate regulation. Advanced technologies support the protection and restoration of this natural resource. We developed the first fine-resolution spectral grid of bare soils by processing a spatiotemporal satellite data cube spanning the globe. Landsat imagery provided a 30 m composite soil image using the Geospatial Soil Sensing System (GEOS3), which calculates the median of pixels from the 40-year time series (1984-2022). The map of the Earth's bare soil covers nearly 90 % of the world's drylands. The modeling resulted in 10 spectral patterns of soils worldwide. Results indicate that plant residue and unknown soil patterns are the main factors that affect soil reflectance. Elevation and the shortwave infrared (SWIR2) band show the highest importance, with 78 and 80 %, respectively, suggesting that spectral and geospatial proxies provide inference on soils. We showcase that spectral groups are associated with environmental factors (climate, land use and land cover, geology, landforms, and soil). These outcomes represent an unprecedented information source capable of unveiling nuances on global soil conditions. Information derived from reflectance data supports the modeling of several soil properties with applications in soil-geological surveying, smart agriculture, soil tillage optimization, erosion monitoring, soil health, and climate change studies. Our comprehensive spectrally-based soil grid can address global needs by informing stakeholders and supporting policy, mitigation planning, soil management strategy, and soil, food, and climate security interventions.

Accurate spatial modeling of soil organic carbon (SOC) and interpretation of the results are crucial worldwide. In this study, we performed national-scale SOC mapping in Russia with a focus on model interpretability under Shapley values technique with its geovisual analysis. Also, we compared Random Forest (RF) models with a different set of covariates and basic hyperparameter tuning. Results revealed that neither the number of covariates nor the tuning significantly affected the model performance, with the best model achieving an RMSE = 70.03 g/kg and an R2 = 0.39. The generated map confirms that northern Russia holds immense SOC levels, primarily driven by the presence of organic soils and peatlands, but overestimated the lowest SOC levels in the south. Shapley value analysis revealed that both biotic and abiotic variables showed complex and non-linear relationships with SOC. Mean annual temperature and precipitation contributed positively to predictions in the north, with a sharply positive effect observed specifically within the 270-280 K (-3.15 to +6.85 °C) range and >500 mm, respectively. Furthermore, higher variability in land surface temperature and elevations below 250 m were identified as key factors promoting SOC accumulation, delineating the carbon-rich Siberian lowlands. The spatial pattern of Shapley values for the major covariates demonstrated a positive contribution to SOC prediction mainly in the northern regions. Notably, the boundary between the positive and negative contributions of some climatic variables coincided with the boundary between Dfb (warm-summer humid continental) and Dfc (subarctic) climatic zones. This study provides a critical baseline for Russia's soil carbon inventory and underscores the value of interpretable machine learning for unravelling the environmental drivers of major Earth carbon sink.

The objective of this study was to find the correlation between spectral reflectance of the arable soil surface and the main parameters of soil fertility in the visible spectral range based on proximal sensing data, to model relationship between soil fertility and spectral reflectance, and to determine whether the discovered correlations can be applied to satellite data. The majority of present methods for collecting information about the main soil agrochemical characteristics in the field are laborious and time-consuming. Moreover, as a rule, heterogeneity within the fields is not taken into account, which results in irrational fertilizer application, soil degradation, and environmental problems. Thus, it is crucial to develop methods that allow obtaining reliable information about soil parameters timely without applying extra efforts in the fields. We examined test field with gray forest soils (Phaeozems Albic). We have applied multiple regression modeling for soil properties prediction. The results demonstrated that each field requires its unique equation model. High correlation was shown for several soil properties. The best linear regression models have shown for exchangeable calcium R adj 2 = 0.76, for exchangeable magnesium R adj 2 = 0.79, for soil organic matter (SOM) content R adj 2 = 0.87, for total Nitrogen, R adj 2 = 0.89, for pH of salt extract, R adj 2 = 0.859. Using cross-validation, we evaluated the predictive capacity of the spectral indices. A number of spectral indices were proposed to predict the properties mentioned. The use of these indices allows to receive the relevant data (on soil properties) quickly and reduce the fertilizer costs and optimize crop growth.

Russia stands at the origins of world soil cartography. Given Russia's vast and diverse territories, the production of current soil maps is an important task in the context of global climate change and food demand. This article provides a review of the digital soil mapping (DSM) field in Russia by identifying trends and research gaps. We examined studies published in international journals in the Scopus and Web of Science databases between the 1990s and the year 2023. We identified 47 articles, an analysis of which, revealed that the growth of DSM began in the second decade of the century. Geographically, the studies were more frequently conducted in the European part of Russia, with the vast majority (79%) performed at the local level (<100,000 ha) and two studies at the national scale. The leading target variables were SOC\SOM and soil classes, whereas remote sensing data and terrain attributes were the most popular among the covariates. Among the DSM methods, linear approaches were predominant, whereas machine learning methods have been increasingly adopted recently. We also discuss the challenges of DSM in Russia and define the following desirable future directions: First, efforts should focus on rescuing existing data and creating new databases. Additionally, we propose a transition to more advanced DSM methods with their subsequent validation (e.g., uncertainty assessment) and the utilisation of diverse cartographic environmental materials accumulated during the USSR era that cover the entire country at different scales. Furthermore, we emphasise the importance of expanding research at the regional and national levels, as well as in the Arctic region and the Far East. This study provides a comprehensive overview of the current state and trends in DSM in Russia. It sheds light on its advancement, challenges, and potential areas for development, guiding future research and policy decisions in the fields of soil mapping and environmental studies.

Global estimates of the size, distribution, and vulnerability of soil inorganic carbon (SIC) remain largely unquantified. By compiling 223,593 field-based measurements and developing machine-learning models, we report that global soils store 2305 ± 636 (±1 SD) billion tonnes of carbon as SIC over the top 2-meter depth. Under future scenarios, soil acidification associated with nitrogen additions to terrestrial ecosystems will reduce global SIC (0.3 meters) up to 23 billion tonnes of carbon over the next 30 years, with India and China being the most affected. Our synthesis of present-day land-water carbon inventories and inland-water carbonate chemistry reveals that at least 1.13 ± 0.33 billion tonnes of inorganic carbon is lost to inland-waters through soils annually, resulting in large but overlooked impacts on atmospheric and hydrospheric carbon dynamics.

The Russian Federation needs to transition to carbon standards, which are established in many foreign countries, to regulate and control the negative consequences of anthropogenic human activity. Quoting of greenhouse gas emissions at the global level pushes for the development and implementation of technologies to reduce them. One of the ways to reduce emissions in agriculture is the creation of carbon farms. At present, there is no unified methodological and statistical basis for the creation of a carbon farm in the Russian Federation. When creating it, it is necessary to understand not only the general principles of agrolandscapes functioning, but also to take into account the factors that have an impact on the carbon absorption capacity of the land located on the territory of the farm. A new approach to reducing emissions by optimising the location of agricultural land with regard to its sequestration potential on the basis of spatial modelling has been proposed. The specificity of the approach is demonstrated on the example of the farm of the All-Russian Research Institute of Reclaimed Lands (Tver region, Kalininsky district, Emmauss settlement). After additional testing, the approach can be recommended for implementation in the practice of carbon-depleting agricultural land use.

Robust and detailed quantitative prediction of soil organic carbon (SOC) is of great significance to studying the carbon budget, soil management and decision-making. Spatial variations of SOC content were modelled using 863 soil profiles and a set of 22 environmental covariates representing relief, bioclimate variables and remote sensing data. The article provided the results of 3D modeling of SOC content in several soil layers (0-5, 5-15, 15-30, 30-60 and 60-100 cm) for the territory of the Russian Federation with 500 m spatial resolution. Machine learning framework was used, with random forest and spatial cross-validation techniques (150 km blocks) to handle the spatial autocorrelation of the training points. Compared with randomized cross-validation (R-2 0.66, Concordance Correlation Coefficient (CCC) 0.79, RMSE 0.99 g/kg), using spatial cross-validation to predict the SOC content yielded less accurate results - R-2 0.45, CCC 0.63, RMSE 1.41 g/kg. Regarding the importance of the variables, soil depth and temperature seasonality were major contributors to the SOC content prediction, followed by the EVI, 7 (MIR) MODIS band, and the topographic wetness index. The model was next evaluated with procedure so-called "area of applicability" (AOA) of prediction model - the areas for which we cannot estimate prediction quality. AOA spatial distribution showed that the feature space not represented by training data is located in the mountain provinces. The proposed framework can be used for SOC modeling with a limited soil profile number, and it is provides a reproducible approach for long-term SOC monitoring.

— Selenium is an important element for human health. Many studies have identified selenium deficiency in soil and water as an important factor in causing Keshan Disease (KD) in Northeast China. Previous studies have mainly focused on soil selenium content, staple food selenium content, and human selenium level, but there are few systematic studies on soil selenium’s existing forms and their migration from soil to crops and the human body. This paper focused on inferring the barrier factors in the migration of selenium from soil to crop and the human body and transformation of its compounds. It provides a reference basis for the etiological analysis, prevention, control, and elimination of KD. The study used 121 183 samples of topsoil (0−20 cm), 30 295 soil parent samples of selenium and other geochemical indices in northeast China, and crop seeds and human hair samples from the KD endemic area. The surface soil selenium was dominantly selenium-sufficient in Northeast China. However, the soil selenium levels were generally low. The average topsoil selenium in Northeast China was 0.20 mg/kg, significantly lower than the world’s average soil selenium content (0.4 mg/kg) and slightly lower than the Chinese average soil selenium content (0.24 mg/kg). Soil selenium mainly existed in strongly bounding by organic bound, with humic acid, and residue forms. The amount of selenium available to plants was sufficient in the selenium-sufficient and KD-endemic areas. However, the average selenium content of human hair was deficient, on average, with 0.16 mg/kg in KD endemic area. We assume that lower soil selenium content may be the basic factor influencing the biogeochemical deficiency of selenium in Northeast China. The sequestration of selenium by clay chemical constituents, such as iron and aluminum oxides and soil organic matter, especially in acidic soils, is another direct contributing factor to the low selenium content in biogeochemical food chain, which increases the risk of KD in the population.

The study reflects an understanding of individual factors regulating and controlling the content of organic carbon of soils, and shows a modern quantitative assessment of the content of organic carbon of soils in Russia, taking into account its huge variability. Paper presents the results of three-dimensional modeling of the organic carbon content of soils with 500 m spatial resolution at several standard depths (0–5, 5–15) to the territory of the Russian Federation using ensemble machine learning. Automated predictive mapping was based on 4 961 soil horizons from 863 soil profiles, and an extensive set of spatial information, including bioclimatic variables, a digital elevation model and its derivatives, and long-term averaged time series of MODIS data. The results of spatial cross-validation show lower (when compared with randomized) accuracy: the coefficient of determination is 0.46, CCC 0.63, RMSE 1.41 g/kg.

Soil color is a key indicator of soil properties and conditions, exerting influence on both agronomic and environmental variables. Conventional methods for soil color determination have come under scrutiny due to their limited accuracy and reliability. In response to these concerns, we developed an innovative system that leverages 35 years of satellite imagery in conjunction with in-situ soil spectral measurements. This approach enables the creation of a global soil color map with a fine spatial resolution of 30 m x 30 m. The system initially identifies bare earth areas worldwide using reflectance bands acquired from Landsat 4 through Landsat 8 between 1985 and 2020. Soil color was quantified using the CIE-XYZ coordinates, utilizing 8005 soil spectral measurements within the visible range (380-780 nm) as ground truth data. We established transfer functions to convert Landsat reflectance bands to standardized XYZ color coordinates. These transfer functions were subsequently applied to images of bare surfaces, covering approximately 38.5% of the Earth's surface. We validated the resulting global soil color map using statistical indices derived from an independent set of ground-truth spectral data, demonstrating a high degree of agreement. By creating the world's first global soil color map, we have set a baseline for future spatial and temporal monitoring of soil conditions, thus enhancing our understanding and management of our planet's vital soil resources.

This study identified and evaluated the association between metal content and UAV data to monitor pollution from roadways. A total of 18 mixed snow samples were collected at the end of winter, utilizing a 1 m long and 10 cm wide snow collection tube, from either side of the Caspian Highway (Moscow-Tambo-Astrakhan) in Moscow. Inductively coupled plasma optical emission spectrometry (ICP-OES) was used to examine the chemical composition of the samples, yielding 35 chemical elements (metals). UAV data and laboratory findings were calculated and examined. Regression estimates demonstrated the possibility of using remote sensing data to identify Al, Ba, Fe, K, and Na metals in snow cover near roadways due to dust dispersal. This discovery supports the argument that UAV sensing data can be utilized to monitor air pollution from roadways.

The present investigation examined the impact of highways on the global dispersion patterns of metallic elements present in dust and snow. A total of 18 mixed snow samples were collected from both sides of the Moscow-Tambo-Astrakhan Caspian Highway by the end of the winter season. The analysis of the samples indicated the presence of 35 distinct chemical elements, where Al, Ba, Ca, Fe, K, Mg, Na, and Zn were identified as the primary contaminants. The primary area of pollution on the windward side originating from the road spans a distance of 20-40 meters, while on the leeward side, it extends to 10 meters. The data presented suggests that the metals found in highways exhibited variability in terms of their solubility in water and concentration. Our findings demonstrate that the predominant wind directions affect the dispersion of pollutants. Furthermore, it was observed that the region with a higher concentration of metal on the side of the road facing the wind had a thickness that was 2-3 times less than that of the opposite side. It is advisable to conduct a subsequent inquiry within the ensuing five years to obtain dependable data regarding the extent of metal pollution.

Drought is considered one of the key barriers influencing wheat production, and various adaptation schemes are practiced globally to mitigate drought impacts. However, it is difficult to precisely assess the performances of drought mitigation measures, especially when multiple measures are implemented simultaneously. Here, a remote sensing-based agricultural drought-affected area change index (ADAC) was applied to assess the performance of drought mitigation schemes, which separates and avoids confusion between performances of drought mitigation and wheat yield improvement. The results revealed the historical performance changes and regional differences under drought mitigation measures in 12 major wheat-growing regions (WGRs) of the world. The drought mitigation efforts have steadily succeeded, with a reduction in the drought-affected area of approximately 14.5 % in the 1980s and 28.5 % in the last decade, relatively 55 % of drought-affected areas are alleviated in the 12 WGRs. However, there are significant regional differences ranging from 28 % to 79 % in the 12 WGRs. The drought mitigation measures implemented in the WGRs of China and India, followed by France and Ukraine, are more effective than other regions, while a few are still declining. By further evaluating the effects of short-term and long-term drought mitigation strategies taken in the WGRs, we found that irrigation is the main drought mitigation measure in dryland, while measures such as conservation tillage are of great value for yield stability for both dry and wet areas. The results of this study improve the understanding of the regional performance of drought mitigation schemes and will help stakeholders to select appropriate measures.

The potential of using spectroscopy for the quantification of soil attributes through its spectral signature is widely documented in the literature. However, a protocol to support formal soil classification systems combining spectral data has not been established. This research proposed a protocol for soil profile classification by combining spectral data from the near visible, shortwave infrared (Vis-NIR-SWIR) and mid infrared (MIR). For this purpose, we used 15 soil profiles located in the Pernambuco State, Brazil. A quantitative analysis between soil attributes and spectral curves was performed for the selection of bands with the best correlations (method 1). In addition, the recursive feature elimination (RFE) function was used for the selection of discriminant bands between soil profiles (method 2). The results of this research indicated that the combined use of spectra is efficient to successfully grouping Ferralsol, Gleysol, and Acrisol. The integrated use of sensors, pedometric techniques, and the expertise of soil scientists can lead to an advanced understanding of soil science.

Pre-harvest prediction of a crop yield may prevent a disastrous situation and help decision-makers to apply more reliable and accurate strategies regarding food security. Remote sensing has numerous returns in the area of crop monitoring and yield prediction which are closely related to differences in soil, climate, and any biophysical and biochemical changes. Different remote techniques could be used for crop monitoring and yield prediction including multi and hyper spectral data, radar and lidar imagery.This study reviews the potentialities, advantages and disadvantages of each technique and the applicability of these techniques under different agricultural conditions. It also shows the different methods in which these techniques could be used efficiently. In addition, the study expects future scenarios of remote sensing applications in vegetation monitoring and the ways to overcome any obstacles that may face this work.It was found that using satellite data with high spthermaatial resolution are still the most powerful method to be used for crop monitoring and to monitor crop parameters. Assessment of crop spectroscopic parameters through field or laboratory devices could be used to identify and quantify many crop biochemical and biophysical parameters. They could be also used as early indicators of plant infections; however, these techniques are not efficient for crop monitoring over large areas.

The purpose of this article is to analyze the changes in salinity of arable soils in the north of Crimea peninsula from 2013 to 2019 using Landsat 8 OLI satellite data in order to obtain additional and objective information about the modern state of soil secondary salinization processes. Comparison of the satellite data of 2013 and 2019 showed that in the area of the North-Crimean Canal there is both a slight expansion of the areas of saline soils and their reduction. The expansion of areas is observed mainly in the coastal zones of lakes and estuaries, and the reduction of areas in the fields, irrigation of which in the analyzed period did not stop. Both expansion and reduction of areas with saline soils did not exceed 10% of the study area. There is no evidence that the expansion of areas of saline soils during the analyzed period was due to the cessation of water supply to the North-Crimean canal.

Rice is an essential crop for national food security in Egypt. Increasing the population calls for regular increases in rice production. At the same time, cultivated rice crop areas should be decreased because of the gradual scarcity of irrigation water. This means more rice production should be gained from less rice area. This situation calls for the annual accurate system for rice monitoring and yield estimation. Therefore, it is necessary to apply a remotely sensed based system for rice cultivation assessment using satellite imagery parallel with field measurements of some biophysical parameters. Multi-temporal normalized difference vegetation index (NDVI) extracted from twelve sentinel-2 imagery cover the whole summer season with variance and maximum value assessed by ground control points (GCPs), were used to isolate uncultivated areas, then to isolate rice areas and other vegetation covers. object-based classification methods with kappa co-efficient 0.9261 and overall accuracy 94.92% was generated to discriminate rice crop area and other summer crops on the study area. Leaf area index (LAI) for the experiment the l site was calculated using the surface energy balance algorithm for Land (SEBAL) model and then tested versus measured (LAI). NDVI and LAI were used to generate an empirical ran rice yield prediction model. Then, this model was used to produce rice to yield a map. The study was carried out in an experimental site in Kafr Elsheikh governorate with a total area of 5040 Hectare. Produced cultivated land use map showed 95% overall accuracy. High similarity was observed between measured and calculated (LAI) with high accuracy of R2 = 0.94. of Rice, yield map showed expected to yield more to than a month before harvest. The generated yield map was tested using a correlation coefficient between actual yield and estimated yield with high accuracy R2 = 0.9. This method is applicable to estimate the acreage and productivity of rice in the northern Nile delta in adequate time before harvest.

The study of rootstocks group, providing the average vigor of growth for the grafted fruit plants, discovered their significant influence on the frost hardiness of the “graft-rootstock” system. The adaptivity of the plum varieties in the different graft and rootstock combinations was studied according to the “frost hardiness” criterion in the stages of the winter and spring development of the flower buds. The computer maps of the plum efficient allocation were created on the studied rootstocks, permitting to lay down the plantings with the lesser risks in the terms of climate fluctuation. It was established, that in the south of Russia the climatic conditions to cultivate Stanley variety on PK SK 1 rootstock, as compared with the rootstock of cherry plum seedling, mostly often used in industrial plantings of plum are mostly favorable. The conducted research contributes to isolation and allocation of the most adaptive graft and rootstock combinations based on their frost hardiness and, consequently, regularity of fruit-bearing and yielding capacity.

Due to short wavelengths, optical remote sensing data provides information about the properties of very thin soil surface layer. This is especially crucial for arable soils as their surface experiences intense impact of agricultural practices and natural conditions. In temperate zone atmospheric precipitation is one of the main natural factors affecting the surface state of arable soils. It causes the breakdown of soil surface aggregates and the redistribution of formed soil material resulting in surface sealing and the formation of soil crust. We studied the properties of soil crust and its impact on the detection of soil properties on arable soils of European part of Russia. Our research showed that the properties of soil surface crust (texture, mineralogical composition, organic matter content, content of microelements, spectral reflectance) differed from the properties of the rest of arable horizon. That discrepancy negatively impacted the performance and reproducibility of the models developed for the detection of arable soil properties and their monitoring on the basis of optical remote sensing data. We found that the performance of the models for the detection of soil fertility indicators based on Sentinel-2 data varied depending on the acquisition date. Optimal dates were different for different fertility indicators. Introduction of information on soil surface state (% of crust and shadows/cracks) at different acquisition dates as predictors in the models developed based on Sentinel-2 data allowed improving their performance and stability. Therefore, soil surface state is an important factor which should be considered when developing models for the detection and monitoring of arable soil properties based on optical remote sensing data or proximal sensing of soil surface. Usage of laboratory soil spectra libraries instead of field spectral data leads to less precise prediction models. The research was supported by the Ministry of science and higher education of Russia (agreement No 075-15-2020-909), and RUDN University Strategic Academic Leadership Program.