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Earth Observation in Support of Sustainable Soils Development

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (20 June 2022) | Viewed by 30018

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Special Issue Editors

Prof. Dr. Ernesto López-Baeza

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Guest Editor

1. Environmental Remote Sensing Group, Earth Physics & Thermodynamics Department, Faculty of Physics, University of Valencia, Valencia, Spain
2. Albavalor S.L.U., University of Valencia Science Park, Valencia, Spain
Interests: remote sensing; soil moisture; earth observation; validation; vegetation biophysical parameters; water resources management and sustainability
Special Issues, Collections and Topics in MDPI journals

Dr. Antonio Lidón Cerezuela

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Guest Editor

School of Agricultural Engineering and Environment, Institute for Water and Environmental Engineering, Universitat Politècnica de València, Camino de Vera, s/n, 46022 Valencia, Spain
Interests: water-soil-plant relationships; transport models of water and solutes in soil; soil carbon cycle; soil nitrogen dynamic

Dr. José Darrozes

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Guest Editor

Géosciences Environnement Toulouse (GET), UMR CNRS5563, CNRS/IRD/UPS, Observatoire Midi-Pyrénées (OMP), 14 Avenue Edouard Belin, 31400 Toulouse, France
Interests: earth observation; river morphology; near surface geophysics; soil moisture; GNSS-R; water cycle; soil contamination; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The 2030 Agenda for Sustainable Development proposes a shared program for the peace and prosperity of people and the planet, now and in the future. The Sustainable Development Goal (SDG) #15 on Life on Land aims to protect, restore and promote the sustainable use of terrestrial ecosystems, sustainably managing forests, combating desertification and stop and reverse land degradation and stop the loss of biodiversity. Moreover, SDG 15.3 specifies by 2030, combat desertification, restore degraded land and soil, including land affected by desertification, drought and floods, anthropogenic contaminations and strive to achieve a land degradation-neutral world.

Although the word “soil” is mentioned very little in the SDG text, many of these objectives will not be achieved without improving soil protection and conservation. The fact is that soil is not subject to rules and targets in the way that air and water are, it is increasingly under pressure (climate change, industrialisation and urbanisation, intensive agriculture and livestock farming, etc.) and land degradation (erosion, contamination, loss of organic matter, etc.) continues at an alarming pace, impeding the achievement of other environmental targets. In spite of this, the IPCC report on land degradation states that land is a critical resource that, despite being under threat, can also be part of the solution.

Earth Observation remote sensing techniques (VNIR, SWIR, TIR, microwaves) and their possible assimilation with in situ data may support the analysis and monitoring of soil types and properties (texture, bulk density, mineralogical composition, soil moisture, carbon and nitrogen cycles components, …). The temporal resolutions of remote sensing data, close to real time, allow a better understanding of soil dynamics and their physico-chemical changes. All these improvements can now particularly provide reliable soil indicators and indices leading to the development of sustainable applications in the fields of precision agriculture, risk management and ecosystem management.

Dr. Ernesto Lopez-Baeza
Dr. Antonio Lidón Cerezuela
Dr. José Darrozes
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Earth Observation
Ecosystem Services Assessment
Forest and Agricultural Models
GNSS-R
Land Degradation Neutrality
Near Surface Geophysics
Remote Sensing
Soil Carbon Cycle
Soil Contamination
Soil Indicators
Soil Moisture
Soils Monitoring
Soil Nitrogen Dynamics
Soil Properties
Sustainable Development Goals
Sustainable Land Management
Sustainable Soils
Transport Models of Water and Solutes in Soil
Water Cycle
Water-Soil-Plant Relationships

Published Papers (10 papers)

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Research

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23 pages, 5476 KiB

Open AccessArticle

Drought Risk Evaluation in Iran by Using Geospatial Technologies

by Abdolreza Ansari Amoli, Hossein Aghighi and Ernesto Lopez-Baeza

Remote Sens. 2022, 14(13), 3096; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14133096 - 27 Jun 2022

Cited by 3 | Viewed by 2609

Abstract

A drought risk map has been developed at the national scale by using remote-sensing satellite data over Iran by combining output layers resulting from three main components of a risk-evaluation procedure including Hazard Quantification (HQ), Vulnerability Assessment (VA) and Identification of Elements at Risk (IER) in a GIS environment. In this respect, Drought Severity (DS) was calculated by using the monthly Normalized Difference Vegetation Index (NDVI) (over 31 years from 1986–2016). Iran landcover classification and a slope map, population density maps, and irrigated farm percentages at the provincial scale were utilized within the drought risk evaluation (DRE) process. The final risk map reveals that the northwest of the country, with a climate similar to the central European weather conditions, is exposed to the maximum drought risk. In contrast, the areas with an arid climate, mainly located in the middle of Iran, exhibits minimum risk against drought. Based on the risk map, the southern part of the Caspian Sea shows very low drought risk due to the moderate and subtropical climate in this region. The outputs of this research will provide advice and warnings to help decision makers reduce drought risk consequences after prioritizing risk areas at the administrative scale. Full article

(This article belongs to the Special Issue Earth Observation in Support of Sustainable Soils Development)

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20 pages, 6831 KiB

Open AccessArticle

Mapping Climate Zones of Iran Using Hybrid Interpolation Methods

by Ebrahim Asadi Oskouei, Bahareh Delsouz Khaki, Saeedeh Kouzegaran, Mir Naser Navidi, Masoud Haghighatd, Naser Davatgar and Ernesto Lopez-Baeza

Remote Sens. 2022, 14(11), 2632; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14112632 - 31 May 2022

Cited by 10 | Viewed by 3612

Abstract

Climate plays a key role in ecosystem services. Understanding microclimate change can be a significant help in making the right decision for ecosystems and buffering the effects of global warming. Given the large distances between meteorological stations and the changes in the climate variables within short distances, such variations cannot be detected just by using observed meteorological data. This study aimed at determining the spatial structure of the mean annual temperature, the annual average precipitation, and the climate zoning of Iran using data from 3825 stations from 2002 to 2016.The multivariate regression demonstrated the dependence of these variables on longitude, latitude, and elevation. Regression-kriging indicated a decline in temperature from east to west and northwest in high-altitude areas, while most precipitation values were observed over the Caspian Sea coastline and the Zagros Mountains. Climatic zoning showed that using auxiliary variables was very effective in detecting 24 climatic classes and understating the climate diversity in Iran. Hot to very hot and arid to very arid climate classes occupy the largest part of Iran, including the southeastern and southern desert regions. According to the generated climatic map, the large climatic diversity of Iran needs accurate policymaking regarding cultivation patterns and biodiversity. Visual comparisons of climatic zones with four remotely sensed agricultural-related variables showed that using such carefully produced climatic maps would be beneficial in classifying, assessing, and interpreting the remote sensed agricultural-related variables. Full article

(This article belongs to the Special Issue Earth Observation in Support of Sustainable Soils Development)

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18 pages, 4492 KiB

Open AccessArticle

Modeling Influence of Soil Properties in Different Gradients of Soil Moisture: The Case of the Valencia Anchor Station Validation Site, Spain

by Ester Carbó, Pablo Juan, Carlos Añó, Somnath Chaudhuri, Carlos Diaz-Avalos and Ernesto López-Baeza

Remote Sens. 2021, 13(24), 5155; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13245155 - 19 Dec 2021

Cited by 1 | Viewed by 2710

Abstract

The prediction of spatial and temporal variation of soil water content brings numerous benefits in the studies of soil. However, it requires a considerable number of covariates to be included in the study, complicating the analysis. Integrated nested Laplace approximations (INLA) with stochastic partial differential equation (SPDE) methodology is a possible approach that allows the inclusion of covariates in an easy way. The current study has been conducted using INLA-SPDE to study soil moisture in the area of the Valencia Anchor Station (VAS), soil moisture validation site for the European Space Agency SMOS (Soil Moisture and Ocean Salinity). The data used were collected in a typical ecosystem of the semiarid Mediterranean conditions, subdivided into physio-hydrological units (SMOS units) which presents a certain degree of internal uniformity with respect to hydrological parameters and capture the spatial and temporal variation of soil moisture at the local fine scale. The paper advances the knowledge of the influence of hydrodynamic properties on VAS soil moisture (texture, porosity/bulk density and soil organic matter and land use). With the goal of understanding the factors that affect the variability of soil moisture in the SMOS pixel (50 km × 50 km), five states of soil moisture are proposed. We observed that the model with all covariates and spatial effect has the lowest DIC value. In addition, the correlation coefficient was close to 1 for the relationship between observed and predicted values. The methodology applied presents the possibility to analyze the significance of different covariates having spatial and temporal effects. This process is substantially faster and more effective than traditional kriging. The findings of this study demonstrate an advancement in that framework, demonstrating that it is faster than previous methodologies, provides significance of individual covariates, is reproducible, and is easy to compare with models. Full article

(This article belongs to the Special Issue Earth Observation in Support of Sustainable Soils Development)

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27 pages, 6056 KiB

Open AccessArticle

Soil Organic Carbon Content Prediction Using Soil-Reflected Spectra: A Comparison of Two Regression Methods

by Sharon Gomes Ribeiro, Adunias dos Santos Teixeira, Marcio Regys Rabelo de Oliveira, Mirian Cristina Gomes Costa, Isabel Cristina da Silva Araújo, Luis Clenio Jario Moreira and Fernando Bezerra Lopes

Remote Sens. 2021, 13(23), 4752; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13234752 - 24 Nov 2021

Cited by 12 | Viewed by 3585

Abstract

Quantifying the organic carbon content of soil over large areas is essential for characterising the soil and the effects of its management. However, analytical methods can be laborious and costly. Reflectance spectroscopy is a well-established and widespread method for estimating the chemical-element content of soils. The aim of this study was to estimate the soil organic carbon (SOC) content using hyperspectral remote sensing. The data were from soils from two localities in the semi-arid region of Brazil. The spectral reflectance factors of the collected soil samples were recorded at wavelengths ranging from 350–2500 nm. Pre-processing techniques were employed, including normalisation, Savitzky–Golay smoothing and first-order derivative analysis. The data (n = 65) were examined both jointly and by soil class, and subdivided into calibration and validation to independently assess the performance of the linear methods. Two multivariate models were calibrated using the SOC content estimated in the laboratory by principal component regression (PCR) and partial least squares regression (PLSR). The study showed significant success in predicting the SOC with transformed and untransformed data, yielding acceptable-to-excellent predictions (with the performance-to-deviation ratio ranging from 1.40–3.38). In general, the spectral reflectance factors of the soils decreased with the increasing levels of SOC. PLSR was considered more robust than PCR, whose wavelengths from 354 to 380 nm, 1685, 1718, 1757, 1840, 1876, 1880, 2018, 2037, 2042, and 2057 nm showed outstanding absorption characteristics between the predicted models. The results found here are of significant practical value for estimating SOC in Neosols and Cambisols in the semi-arid region of Brazil using VIS-NIR-SWIR spectroscopy. Full article

(This article belongs to the Special Issue Earth Observation in Support of Sustainable Soils Development)

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17 pages, 4451 KiB

Open AccessArticle

Quantifying Soil Moisture Impacts on Water Use Efficiency in Terrestrial Ecosystems of China

by Xingming Hao, Jingjing Zhang, Xue Fan, Haichao Hao and Yuanhang Li

Remote Sens. 2021, 13(21), 4257; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13214257 - 22 Oct 2021

Cited by 5 | Viewed by 2468

Abstract

Soil moisture (SM) significantly affects the exchange of land surface energy and the stability of terrestrial ecosystems. Although some conclusions have been drawn about the effects of SM on the ecosystem water use efficiency (WUE), the influence mechanism and the quantitative assessment framework of SM on WUE are still unclear. This study provides an analysis framework for the feedback relationship between SM and WUE based on the dependence of the evaporation fraction on SM and output datasets from remote sensing and the Global Land Data Assimilation System. The results show that the range of WUE of terrestrial ecosystems of China was 0.02–19.26 g C/kg H₂O in the growing season with an average value of 1.05 g C/kg H₂O. They also show a downward trend in 43.99% of the total area. In the evapotranspiration (ET) pathway, SM negatively affected WUE, and the sensitivity coefficient ranged from −18.49 to −0.04. In the net primary production (NPP) pathway, the sensitivity coefficient ranged from −68.66 to 43.19. Under the dual effects of the ET and NPP pathways, the influence of SM on WUE was negative in 84.62% of the area. Variation in SM led to significant WUE variability. Generally, the percentage change in WUE (ΔWUE) ranged from 0% to 190.86%, with an average value of 28.02%. The maximum ΔWUE ranged from 0% to 758.78%, with an average value of 109.29%. The WUE of forest ecosystems showed strong resistance to SM variation, whereas that of non-forest vegetation was more sensitive to SM variation. This analytical framework provides a new perspective on the feedback relationship between WUE and SM in terrestrial ecosystems. Full article

(This article belongs to the Special Issue Earth Observation in Support of Sustainable Soils Development)

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15 pages, 3516 KiB

Open AccessArticle

Prediction Potential of Remote Sensing-Related Variables in the Topsoil Organic Carbon Density of Liaohekou Coastal Wetlands, Northeast China

by Shuai Wang, Mingyi Zhou, Qianlai Zhuang and Liping Guo

Remote Sens. 2021, 13(20), 4106; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13204106 - 14 Oct 2021

Cited by 1 | Viewed by 2180

Abstract

Wetland ecosystems contain large amounts of soil organic carbon. Their natural environment is often both at the junction of land and water with good conditions for carbon sequestration. Therefore, the study of accurate prediction of soil organic carbon (SOC) density in coastal wetland ecosystems of flat terrain areas is the key to understanding their carbon cycling. This study used remote sensing data to study SOC density potentials of coastal wetland ecosystems in Northeast China. Eleven environmental variables including normalized difference vegetation index (NDVI), difference vegetation index (DVI), soil adjusted vegetation index (SAVI), renormalization difference vegetation index (RDVI), ratio vegetation index (RVI), topographic wetness index (TWI), elevation, slope aspect (SA), slope gradient (SG), mean annual temperature (MAT), and mean annual precipitation (MAP) were selected to predict SOC density. A total of 193 soil samples (0–30 cm) were divided into two parts, 70% of the sampling sites data were used to construct the boosted regression tree (BRT) model containing three different combinations of environmental variables, and the remaining 30% were used to test the predictive performance of the model. The results show that the full variable model is better than the other two models. Adding remote sensing-related variables significantly improved the model prediction. This study revealed that SAVI, NDVI and DVI were the main environmental factors affecting the spatial variation of topsoil SOC density of coastal wetlands in flat terrain areas. The mean (±SD) SOC density of full variable models was 18.78 (±1.95) kg m⁻², which gradually decreased from northeast to southwest. We suggest that remote sensing-related environmental variables should be selected as the main environmental variables when predicting topsoil SOC density of coastal wetland ecosystems in flat terrain areas. Accurate prediction of topsoil SOC density distribution will help to formulate soil management policies and enhance soil carbon sequestration. Full article

(This article belongs to the Special Issue Earth Observation in Support of Sustainable Soils Development)

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21 pages, 4989 KiB

Open AccessArticle

Data Fusion of XRF and Vis-NIR Using Outer Product Analysis, Granger–Ramanathan, and Least Squares for Prediction of Key Soil Attributes

by S. Hamed Javadi and Abdul M. Mouazen

Remote Sens. 2021, 13(11), 2023; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13112023 - 21 May 2021

Cited by 13 | Viewed by 2397

Abstract

Visible-near-infrared (vis-NIR) and X-ray fluorescence (XRF) are key technologies becoming pervasive in proximal soil sensing (PSS), whose fusion holds promising potential to improve the estimation accuracy of soil attributes. In this paper, we examine different data fusion methods for the prediction of key soil fertility attributes including pH, organic carbon (OC), magnesium (Mg), and calcium (Ca). To this end, the vis-NIR and XRF spectra of 267 soil samples were collected from nine fields in Belgium, from which the soil samples of six fields were used for calibration of the single-sensor and data fusion models while the validation was performed on the remaining three fields. The first fusion method was the outer product analysis (OPA), for which the outer product (OP) of the two spectra is computed, flattened, and then subjected to partial least squares (PLS) regression model. Two versions of OPA were evaluated: (i) OPA-FS in which the full spectra were used as input; and (ii) OPA-SS in which selected spectral ranges were used as input. In addition, we examined the potential of least squares (LS) and Granger–Ramanathan (GR) analyses for the fusion of the predictions provided by the single-sensor PLS models. Results demonstrate that the prediction performance of the single-sensor PLS models is improved by GR in addition to the LS fusion method for all soil attributes since it accounts for residuals. Resorting to LS, the largest improvements compared to the single-sensor models were obtained, respectively, for Mg (residual prediction deviation (RPD) = 4.08, coefficient of determination (R²) = 0.94, ratio of performance of inter-quantile (RPIQ) = 1.64, root mean square error (RMSE) = 4.57 mg/100 g), OC (RPD = 1.79, R² = 0.69, RPIQ = 2.82, RMSE = 0.16%), pH (RPD = 1.61, R² = 0.61, RPIQ = 3.06, RMSE = 0.29), and Ca (RPD = 3.33, R² = 0.91, RPIQ = 1, RMSE = 207.48 mg/100 g). OPA-FS and OPA-SS outperformed the individual, GR, and LS models for pH only, while OPA-FS was effective in improving the individual sensor models for Mg as well. The results of this study suggest LS as a robust fusion method in improving the prediction accuracy for all the studied soil attributes. Full article

(This article belongs to the Special Issue Earth Observation in Support of Sustainable Soils Development)

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20 pages, 4081 KiB

Open AccessArticle

Validation of the SMOS Level 1C Brightness Temperature and Level 2 Soil Moisture Data over the West and Southwest of Iran

by Mozhdeh Jamei, Mohammad Mousavi Baygi, Ebrahim Asadi Oskouei and Ernesto Lopez-Baeza

Remote Sens. 2020, 12(17), 2819; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12172819 - 31 Aug 2020

Cited by 8 | Viewed by 3054

Abstract

The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) mission with the MIRAS (Microwave Imaging Radiometer using Aperture Synthesis) L-band radiometer provides global soil moisture (SM) data. SM data and products from remote sensing are relatively new, but they are providing significant observations for weather forecasting, water resources management, agriculture, land surface, and climate models assessment, etc. However, the accuracy of satellite measurements is still subject to error from the retrieval algorithms and vegetation cover. Therefore, the validation of satellite measurements is crucial to understand the quality of retrieval products. The objectives of this study, precisely framed within this mission, are (i) validation of the SMOS Level 1C Brightness Temperature (TB_SMOS) products in comparison with simulated products from the L-MEB model (TB_L-MEB) and (ii) validation of the SMOS Level 2 SM (SM_SMOS) products against ground-based measurements at 10 significant Iranian agrometeorological stations. The validations were performed for the period of January 2012 to May 2015 over the Southwest and West of Iran. The results of the validation analysis showed an RMSE ranging between 9 to 13 K and a strong correlation (R = 0.61–0.84) between TB_SMOS and TB_L-MEB at all stations. The bias values (0.1 to 7.5 K) showed a slight overestimation for TB_SMOS at most of the stations. The results of SM_SMOS validation indicated a high agreement (RMSE = 0.046–0.079 m³ m⁻³ and R = 0.65–0.84) between the satellite SM and in situ measurements over all the stations. The findings of this research indicated that SM_SMOS shows high accuracy and agreement with in situ measurements which validate its potential. Due to the limitation of SM measurements in Iran, the SMOS products can be used in different scientific and practical applications at different Iranian study areas. Full article

(This article belongs to the Special Issue Earth Observation in Support of Sustainable Soils Development)

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Other

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12 pages, 4155 KiB

Open AccessTechnical Note

First Measurement of Soil Freeze/Thaw Cycles in the Tibetan Plateau Using CYGNSS GNSS-R Data

by Xuerui Wu, Zhounan Dong, Shuanggen Jin, Yang He, Yezhi Song, Wenxiao Ma and Lei Yang

Remote Sens. 2020, 12(15), 2361; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12152361 - 23 Jul 2020

Cited by 29 | Viewed by 2713

Abstract

The process of soil freezing and thawing refers to the alternating phase change of liquid water and solid water in the soil, accompanied by a large amount of latent heat exchange. It plays a vital role in the land water process and is an important indicator of climate change. The Tibetan Plateau in China is known as the “roof of the world”, and it is one of the most prominent physical characteristics is the freezing and thawing process of the soil. For the first time, this paper utilizes the spaceborne GNSS-R mission, i.e., CYGNSS (Cyclone Global Navigation Satellite System), to study the feasibility of monitoring the soil freeze-thaw (FT) cycles on the Tibetan Plateau. In the theoretical analysis part, model simulations show that there are abrupt changes in soil permittivities and surface reflectivities as the soil FT occurs. The CYGNSS reflectivities from January 2018 to January 2020 are compared with the SMAP FT state. The relationship between CYGNSS reflectivity and SMAP soil moisture within this time series is analyzed and compared. The results show that the effect of soil moisture on reflectivity is very small and can be ignored. The periodic oscillation change of CYGNSS reflectivity is almost the same as the changes in SMAP FT data. Freeze-thaw conversion is the main factor affecting CYGNSS reflectivity. The periodical change of CYGNSS reflectivity in the 2 years indicates that it is mainly caused by soil FT cycles. It is feasible to use CYGNSS to monitor the soil FT cycles in the Tibetan Plateau. This research expands the current application field of CYGNSS and opens a new chapter in the study of cryosphere using spaceborne GNSS-R with high spatial-temporal resolution. Full article

(This article belongs to the Special Issue Earth Observation in Support of Sustainable Soils Development)

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16 pages, 6020 KiB

Open AccessLetter

Models and Theoretical Analysis of SoOp Circular Polarization Bistatic Scattering for Random Rough Surface

by Xuerui Wu and Shuanggen Jin

Remote Sens. 2020, 12(9), 1506; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12091506 - 09 May 2020

Cited by 12 | Viewed by 2642

Abstract

Soil moisture is an important factor affecting the global climate and environment, which can be monitored by microwave remote sensing all day and under all weather conditions. However, existing monostatic radars and microwave radiometers have their own limitations in monitoring soil moisture with shallower depths. The emerging remote sensing of signal of opportunity (SoOp) provides a new method for soil moisture monitoring, but only an experimental perspective was proposed at present, and its mechanism is not clear. In this paper, based on the traditional surface scattering models, we employed the polarization synthesis method, the coordinate transformation, and the Mueller matrix, to develop bistatic radar circular polarization models that are suitable for SoOP remote sensing. Using these models as a tool, the bistatic scattering versus the observation frequency, soil moisture, scattering zenith angle, and scattering azimuth at five different circular polarizations (LR, HR, VR, + 45° R, and −45° R) are simulated and analyzed. The results show that the developed models can determine the optimal observation combination of polarizations and observation angle. The systematic analysis of the scattering characteristics of random rough surfaces provides an important guiding significance for the design of space-borne payloads, the analysis of experimental data, and the development of backward inversion algorithms for more effective SoOP remote sensing. Full article

(This article belongs to the Special Issue Earth Observation in Support of Sustainable Soils Development)

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