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Soil Moisture and Land Surface Processes: Observation, Modeling and Coupling Analysis

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A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Land Innovations – Data and Machine Learning".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 16869

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Special Issue Editors

Dr. Jianzhi Dong

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Guest Editor

School of Earth System Science, Tianjin University, Tianjin 300072, China
Interests: soil moisture; data assimilation; land surface modeling; uncertainty analysis; land-atmosphere coupling

Dr. Yonggen Zhang

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Guest Editor

School of Earth System Science, Tianjin University, Tianjin 300072, China
Interests: soil moisture; pedotransfer function; inverse modeling; pore scale simulation

Dr. Zhongwang Wei

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Guest Editor

Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 510275, China
Interests: evapotranspiration hydrology
Special Issues, Collections and Topics in MDPI journals

Dr. Sara Bonetti

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Guest Editor

Soil Physics and Land Management Group, Wageningen University, Droevendaalsesteeg 3, 6708 PB Wageningen, The Netherlands
Interests: soil-plant-atmosphere interactions; geomorphology; ecohydrology; vadose zone processes

Prof. Dr. Wei Shangguan

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Guest Editor

School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai 519082, China
Interests: land data development; machine learning; land surface modeling; soil
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Soil moisture is a central variable in land surface processes and has been a key research focus for the fields hydrology and climatology. In particular, the temporal variability of soil moisture determines the coupling strength of water, energy, and carbon fluxes between land and atmosphere. The fidelity of these coupling relationships determines the performance of both hydrological modeling and numerical weather forecasting over global dry–wet transitional zones. However, in most Land Surface and Earth System Models, such soil–moisture-centered coupling relationships are empirically defined and poorly constrained.

Recently, significant advances have been made in soil moisture observation. However, the traditional point-scale soil moisture observation techniques tend to contain substantial spatial representativeness errors for large-scale modeling analysis. On the other hand, remotely sensed soil moisture is vulnerable to a multitude of uncertainty sources, e.g., uncertainties in both retrieval algorithms and ancillary inputs. Uncertainties in soil moisture datasets complicate their utility in diagnosing land surface processes. Therefore, this Special Issue seeks to shed light on the following questions:

How to quantify the uncertainty of soil moisture observations at different scales?
How to constrain uncertainties in land surface modeling and/or vadose zone modeling?
How to derive optimal soil moisture estimates based on available information?
What are the quantitative links between large-scale soil moisture measurements and hydrology/climate/carbon fluxes?

This Special Issue focuses on the observation techniques of soil moisture and their effects on different land- and atmosphere-related variables.

The themes and article types of this Special Issue will include: (1) Uncertainty quantification of soil moisture observations, article and review; (2) soil moisture data scaling and merging, article and review; (3) observation techniques, article and review; (4) land surface modeling and parameterization, article; (5) land data assimilation, article and review; and (6) land-atmosphere interactions, article and review.

Dr. Jianzhi Dong
Dr. Yonggen Zhang
Dr. Zhongwang Wei
Dr. Sara Bonetti
Dr. Shangguan Wei
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. Land is an international peer-reviewed open access monthly 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 2600 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

soil moisture
remote sensing
land surface modeling
water–energy–carbon coupling
uncertainty analysis
data assimilation and merging

Published Papers (10 papers)

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Research

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24 pages, 5509 KiB

Open AccessFeature PaperArticle

Ecovoltaics: Maintaining Native Plants and Wash Connectivity inside a Mojave Desert Solar Facility Leads to Favorable Growing Conditions

by Tamara Wynne-Sison, Dale A. Devitt and Stanley D. Smith

Land 2023, 12(10), 1950; https://doi.org/10.3390/land12101950 - 21 Oct 2023

Viewed by 1772

Abstract

The installation of solar facilities is increasing rapidly in the Mojave Desert USA, with the largest facility in North America (3227 ha) currently being built 30 km north of Las Vegas, NV. At the state level, Nevada (USA) has developed an energy plan to diversify its energy portfolio by 2030 with green energy representing 50% of the energy produced. Although solar is considered a clean energy, it does require significant amounts of land and as such may have negative consequences at the habitat and ecosystem levels. A multi-year study was conducted to assess the impact a photovoltaic facility in the Mojave Desert had on the growth and physiological response of two native shrubs (Ambrosia dumosa and Larrea tridentata) growing inside and outside the facility. These species were selected because they were the dominant species at the site and are representative of desert scrub communities throughout the Mojave Desert. At the time of construction, native plants and washes were left intact inside the solar facility. The solar panel arrays were separated at either 8 m or 10 m. Plants were selected for monitoring on the basis of location: at the panel drip line, below the panels, or midway between panel rows. Abiotic factors, including PAR, reference evapotranspiration, precipitation, soil water in storage, and infiltration, were monitored bi-monthly. The growth and physiological status of the plants were assessed by monitoring leaf water potential, chlorophyll index, canopy temperatures, non-structural carbohydrates in the roots and stems, leaf tissue ion concentrations, stem elongation, and seed production. Plants at the bottom edges of the panels received more precipitation due to runoff from the panels, which led to increased soil moisture in the long spacing but not the short spacing. The lower soil water in storage in the short spacing was related to greater growth and higher soil water extraction. Although the area under the panels provided shade in the summer and warmer temperatures in the winter, the incoming PAR was reduced by as much as 85%, causing plants growing under the panels to be spindly with lower canopy volume (L. tridentata, p = 0.03) and seed yield (A. dumosa, p = 0.05). Ambrosia plants remained green in color year-round (not going into winter dormancy) inside the facility and had elevated levels of starch in their roots and stems compared with plants growing at the outside control sites (p < 0.001). Larrea growing outside the facility had lower xylem water potentials compared with those inside the facility (p < 0.001), lower chlorophyll index (p < 0.001, Ambrosia as well), and lower stem elongation (p < 0.001), supporting the conclusion that both Larrea and Ambrosia performed better inside the facility. Shifts in δ¹³ C suggested greater water-use efficiency at the locations with the least amount of soil water in storage. Our results support the installation of solar facilities that minimize the impact on native plants and wash connectivity (ecovoltaics), which should translate into a reduced negative impact at the habitat and ecosystem levels. Basedon our results, energy companies that embrace ecovoltaic systems that take an engineering and biological approach should provide acceptable environments for desert fauna. However, corridors (buffers) will need to be maintained between solar facilities, and fences will need to have openings that allow for the continuous flow of animals and resources. Full article

(This article belongs to the Special Issue Soil Moisture and Land Surface Processes: Observation, Modeling and Coupling Analysis)

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21 pages, 5461 KiB

Open AccessArticle

Scale-Dependent Field Partition Based on Water Retention Functional Data

by Annamaria Castrignanò, Ladan Heydari and Hossein Bayat

Land 2023, 12(5), 1106; https://doi.org/10.3390/land12051106 - 22 May 2023

Cited by 1 | Viewed by 844

Abstract

Functional data are being used increasingly in recent years and in many environmental sciences, such as hydrology applied to agriculture. This means that the output, instead of a scalar variable represented by a spatial map, is given by a function. Furthermore, in site-specific management, there is a need to delineate the field into management areas depending on the agricultural procedure and on the scale of application. In this paper, an approach based on multivariate geostatistics is illustrated that uses the parameters of Dexter’s water retention model and some soil properties to arrive at a multiscale delineation of an agricultural field in Iran. One hundred geo-referenced soil samples were taken and subjected to various measurements. The volumetric water contents at the different suctions were fitted to Dexter’s model. The estimated curve parameters plus the measurements of the soil variables were transformed into standardized Gaussian variables and the values transformed were subjected to geostatistical cokriging and factorial cokriging procedures. These results show that soil properties (organic carbon, bulk density, saturated hydraulic conductivity and tensile strength of soil aggregates) influence the parameters of Dexter’s model, although to different extents. The thematic maps of both soil properties and water retention curve parameters displayed a varying degree of spatial association that allowed the identification of homogeneous areas within the field. The first regionalized factors (F1) at the scales of 508 m and 3000 m made it possible to provide different delineations of the field into homogeneous areas as a function of scale, characterized by specific physical and hydraulic properties. F1 at a short and long distance could be interpreted as “porosity indicator” and “hydraulic indicator”, respectively. Such type of field delineation proves particularly useful in sustainable irrigation management. This paper emphasizes the importance of taking the spatial scale into account in precision agriculture. Full article

(This article belongs to the Special Issue Soil Moisture and Land Surface Processes: Observation, Modeling and Coupling Analysis)

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17 pages, 3343 KiB

Open AccessArticle

Soil Moisture and Water Transport through the Vadose Zone and into the Shallow Aquifer: Field Observations in Irrigated and Non-Irrigated Pasture Fields

by Daniel G. Gómez, Carlos G. Ochoa, Derek Godwin, Abigail A. Tomasek and María I. Zamora Re

Land 2022, 11(11), 2029; https://doi.org/10.3390/land11112029 - 13 Nov 2022

Cited by 1 | Viewed by 1261

Abstract

Reliable estimates of soil moisture and other field observations (e.g., precipitation, irrigation) are critical to quantify the seasonal variability of surface water and groundwater relationships. This is especially important in pasture-based agroecosystems that rely on surface water diversions and precipitation inputs for agricultural production. The objectives of this study were to (1) quantify soil water balance components in irrigated and non-irrigated pasture fields in western Oregon, USA and (2) evaluate soil moisture and shallow aquifer recharge relationships in irrigated vs. non-irrigated pasture fields. Four monitoring stations in each field were used to measure soil water content in the upper 0.8 m profile and shallow groundwater levels. A soil water balance (SWB) approach was used to determine deep percolation based on field measurements of several other hydrology variables (e.g., irrigation and soil moisture). The water table fluctuation method (WTFM) was used to estimate shallow aquifer response to irrigation and precipitation inputs. Results from this study add to the understanding of seasonal water transport through the vadose zone and into the shallow aquifer in agroecological systems with fine-textured soils in the Pacific Northwest region of the United States. Full article

(This article belongs to the Special Issue Soil Moisture and Land Surface Processes: Observation, Modeling and Coupling Analysis)

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17 pages, 3683 KiB

Open AccessArticle

Influence of Different Methods to Estimate the Soil Thermal Properties from Experimental Dataset

by Leugim Corteze Romio, Tamires Zimmer, Tiago Bremm, Lidiane Buligon, Dirceu Luis Herdies and Débora Regina Roberti

Land 2022, 11(11), 1960; https://doi.org/10.3390/land11111960 - 2 Nov 2022

Cited by 3 | Viewed by 1191

Abstract

Knowledge of soil thermal properties (diffusivity (k) and conductivity (λ)) is important to understand the soil–plant–atmosphere interaction related to the physical and biological processes associated with energy transfer and greenhouse gas exchanges. The incorporation of all the physical processes that occur in the energy transfer in the soil is a challenge in order to correctly estimate soil thermal properties. In this work, experimental measurements of soil temperature and soil heat flux obtained in a silty clay loam soil covered by native grassland located in the Brazilian Pampa biome were used to estimate soil thermal properties using different methods including the influence of the soil water content at different soil depths in heat transfer processes. The λ was estimated using the numerical solution of the Fourier equation by the Gradient and Modified Gradient methods. For the surface layer, the results for both models show large variability in daily values, but with similar values for the annual mean. For λ at different soil depths, both models showed an increase of approximately 50% in the λ value in the deeper layers compared to the surface layer, increasing with depth in this soil type. The k was estimated using analytical and numerical methods. The analytical methods showed a higher variability and overestimated the values of the numerical models from 15% to 35%. The numerical models included a term related to the soil water content. However, the results showed a decrease in the mean value of k by only 2%. The relationship between thermal properties and soil water content was verified using different empirical models. The best results for thermal conductivity were obtained using water content in the surface layer (R² > 0.5). The cubic model presented the best results for estimating the thermal diffusivity (R² = 0.70). The analyses carried out provide knowledge for when estimating soil thermal properties using different methods and an experimental dataset of soil temperature, heat flux and water content, at different soil depths, for a representative soil type of the Brazilian Pampa biome. Full article

(This article belongs to the Special Issue Soil Moisture and Land Surface Processes: Observation, Modeling and Coupling Analysis)

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18 pages, 1405 KiB

Open AccessArticle

An Evaluation and Promotion Strategy of Green Land Use Benefits in China: A Case Study of the Beijing–Tianjin–Hebei Region

by Wenying Peng, Yue Sun, Yingchen Li and Xiaojuan Yuchi

Land 2022, 11(8), 1158; https://doi.org/10.3390/land11081158 - 26 Jul 2022

Cited by 1 | Viewed by 1290

Abstract

Green development is the inevitable choice for global sustainable development, and China has chosen green development as its national strategy. Land use changes will affect a soil’s organic matter by changing the land’s productivity, soil quality and fertility. It is of great significance for ensuring soil fertility, improving the environment and promoting the carbon cycle that the concept of green development is implemented in the process of land use activity. Establishing an indicator system and evaluation method for a green land use benefit evaluation suitable for green development is helpful for strengthening the responsibility and consciousness of such land use, and to provide theoretical guidance and decision-making references for promoting such developments and evaluations. In this study, based on a connotation analysis of green land use, the entropy weight method and BP (Back Propagation) neural network model method were used to construct an evaluation index system for green land use benefits, including four criterion layers and eighteen evaluation indexes, and the entropy-BP neural network evaluation method was proposed to reveal the problems in green land use benefits in the Beijing–Tianjin–Hebei region. The results showed that the green land use benefit level in the region was low, while the spatial pattern was high in the north, low in the middle and high in the south. Langfang, Beijing and Handan were the lowest centers of green land ecological benefit, while Beijing and Tianjin were the lowest centers of green land economic benefit. The green governance benefit and green space benefit were in a relative spatial equilibrium. The cultivated land area, forestry products, sewage centralized treatment degree and built-up area ratio were the most important influences on the green ecological benefit, green economic benefit, green governance benefit and green space benefit, respectively. The entropy-BP neural network evaluation system and method have certain applications in the design of relevant assessment reward-and-punishment systems. Accelerating the optimization of the Beijing–Tianjin–Hebei territorial space’s development and utilization pattern, and constructing a green benefit sharing mechanism of land use, are important strategies to improve the benefits of green land use. Full article

(This article belongs to the Special Issue Soil Moisture and Land Surface Processes: Observation, Modeling and Coupling Analysis)

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25 pages, 34171 KiB

Open AccessArticle

Coupling a New Version of the Common Land Model (CoLM) to the Global/Regional Assimilation and Prediction System (GRAPES): Implementation, Experiment, and Preliminary Evaluation

by Zhenyi Yuan and Nan Wei

Land 2022, 11(6), 770; https://doi.org/10.3390/land11060770 - 24 May 2022

Cited by 3 | Viewed by 1490

Abstract

Land surface processes can significantly influence weather and climate. The Common Land Model version 2005 (CoLM2005) has been coupled to the Global Forecast System of the Global/Regional Assimilation and Prediction System (GRAPES_GFS), which is independently developed by the China Meteorological Administration. Since a new version of CoLM has been developed (CoLM2014) with updated soil basic data and parts of hydrological processes, we coupled CoLM2014 with GRAPES_GFS to investigate whether the land surface model can help to improve the prediction skill of the weather forecast model. The forecast results were evaluated against global validation datasets at different forecasting lengths and over various regions. The results demonstrate that GRAPES_GFS coupled with CoLM2005 and CoLM2014 can both well reproduce the spatial patterns and magnitude of atmospheric variables, and the effective predictable lengths of time are up to 3 days on the global scale and even up to 6 days on regional scales. Moreover, the GRAPES_GFS coupled with CoLM2014 outperforms the original one in predicting atmospheric variables. In addition, GRAPES_GFS coupled with both versions of CoLM reproduce acceptably accurate spatial distribution and magnitude of land variables. GRAPES_GFS coupled with CoLM2014 significantly improves the forecast of land surface state variables compared to the one coupled with CoLM2005, and the improvement signal is more notable than that in atmospheric variables. Overall, this study shows that CoLM is suitable for coupling with GRAPES_GFS, and the improvement of the land surface model in a weather forecast model can significantly improve the prediction skill of both atmospheric and land variables. Full article

(This article belongs to the Special Issue Soil Moisture and Land Surface Processes: Observation, Modeling and Coupling Analysis)

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15 pages, 1909 KiB

Open AccessArticle

Functional Evaluation of Digital Soil Hydraulic Property Maps through Comparison of Simulated and Remotely Sensed Maize Canopy Cover

by Mulenga Kalumba, Stefaan Dondeyne, Eline Vanuytrecht, Edwin Nyirenda and Jos Van Orshoven

Land 2022, 11(5), 618; https://doi.org/10.3390/land11050618 - 22 Apr 2022

Viewed by 1665

Abstract

Soil maps can usefully serve in data scarce regions, for example for yield (gap) assessments using a crop simulation model. The soil property estimates’ contribution to inaccuracy and uncertainty can be functionally evaluated by comparing model results using the estimates as input against independent observations. We conducted a functional evaluation of digital maps of soil hydraulic properties of the Zambezi River Basin using a crop growth model AquaCrop. AquaCrop was run, alimented with local meteorological data, and with soil hydraulic properties derived from the digital maps of digital soil mapping (DSM) techniques, as opposed to estimations from the widely used Saxton and Rawls pedotransfer functions. The two simulated time series of canopy cover (CC) (AquaCrop-CC-DSM and AquaCrop-CC-Saxton), which were compared against canopy cover data derived from the remotely sensed Leaf Area Index (LAI) from the MODIS archive (MODIS-CC). A pairwise comparison of the time series resulted in a root mean squared error (RMSE) of 0.07 and a co-efficient of determination (R²) of 0.93 for AquaCrop-CC-DSM versus MODIS-CC, and an RMSE of 0.08 and R² of 0.88 for AquaCrop-CC-Saxton versus MODIS-CC. In dry years, the AquaCrop-CC-DSM deviated less from the MODIS-CC than the AquaCrop-CC-Saxton (p < 0.001), although this difference was not significant in wet years. The functional evaluation showed that soil hydraulic property estimates based on digital soil mapping outperformed those based on Saxton and Rawls when used for simulating crop growth in dry years in the Zambezi River Basin. This study also shows the value of conducting a functional evaluation of estimated (static) soil hydraulic properties in terms of dynamic model output. Full article

(This article belongs to the Special Issue Soil Moisture and Land Surface Processes: Observation, Modeling and Coupling Analysis)

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22 pages, 11787 KiB

Open AccessArticle

Machine Learning Techniques for Estimating Hydraulic Properties of the Topsoil across the Zambezi River Basin

by Mulenga Kalumba, Edwin Nyirenda, Imasiku Nyambe, Stefaan Dondeyne and Jos Van Orshoven

Land 2022, 11(4), 591; https://doi.org/10.3390/land11040591 - 18 Apr 2022

Cited by 4 | Viewed by 2104

Abstract

It is critical to produce more crop per drop in an environment where water availability is decreasing and competition for water is increasing. In order to build such agricultural production systems, well parameterized crop growth models are essential. While in most crop growth modeling research, focus is on gathering model inputs such as climate data, less emphasis is paid to collecting the critical soil hydraulic properties (SHPs) data needed to operate crop growth models. Collection of SHPs data for the Zambezi River Basin (ZRB) is extremely labor-intensive and expensive, thus alternate technologies such as digital soil mapping (DSM) must be explored. We evaluated five types of DSM models to establish the best spatially explicit estimates of the soil water content at pF0.0 (saturation), pF2.0 (field capacity), and pF4.2 (wilting point), and of the saturated hydraulic conductivity (Ksat) across the ZRB by using estimates of locally calibrated pedotransfer functions of 1481 locations for training and testing the DSM models, as well as a reference dataset of measurements from 174 locations for validating the DSM models. We produced coverages of environmental covariates from various source datasets, including climate variables, soil and land use maps, parent materials and lithologic units, derivatives of a digital elevation model (DEM), and Landsat imagery with a spatial resolution of 90 m. The five types of models included multiple linear regression and four machine learning techniques: artificial neural network, gradient boosted regression trees, random forest, and support vector machine. Where the residuals of the initial DSM models were spatially autocorrelated, the models were extended/complemented with residual kriging (RK). Spatial autocorrelation in the model residuals was observed for all five models of each of the three water contents, but not for Ksat. On average for the water content, the R² ranged from 0.40 to 0.80 in training and test datasets before adding kriged model residuals and ranged from 0.80 to 0.95 after adding model residuals. Overall, the best prediction method consisted of random forest as the deterministic model, complemented with RK, whereby soil texture followed by climate and topographic elevation variables were the most important covariates. The resulting maps are a ready-to-use resource for hydrologists and crop modelers to aliment and calibrate their hydrological and crop growth models. Full article

(This article belongs to the Special Issue Soil Moisture and Land Surface Processes: Observation, Modeling and Coupling Analysis)

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16 pages, 3509 KiB

Open AccessArticle

Assessment of Agricultural Drought Based on Reanalysis Soil Moisture in Southern China

by Wei Shangguan, Ruqing Zhang, Lu Li, Shulei Zhang, Ye Zhang, Feini Huang, Jianduo Li and Wei Liu

Land 2022, 11(4), 502; https://doi.org/10.3390/land11040502 - 31 Mar 2022

Cited by 10 | Viewed by 1951

Abstract

Accurate assessment of agricultural drought is useful for ecosystem services. This is a successive work of our previous study that assessed agricultural drought using the soil water deficit index (SWDI) based on ERA5-Land in the four southern provinces of China from 2017 to 2019. Firstly, in addition to ERA5-Land, the suitability of CLDAS (China Land Data Assimilation System) soil moisture for drought assessment was investigated. Then, the study was extended with more comprehensive analysis and a much longer period (1981–2020). Based on three climate zones, in situ soil moisture was used for evaluation of both reanalysis datasets and agricultural drought. It was found that ERA5-Land_SWDI and CLDAS_SWDI have a good correlation with the in situ SWDI. ERA5-Land and CLDAS demonstrate some differences in representing agricultural drought but have a similar performance evaluated by in situ soil moisture. Droughts from 2001 to 2010 were more serious than in the other three decades, and droughts have become longer and severer in some areas in the last 40 years. There was a good correlation between agricultural drought and meteorological drought. Our work offers important insights for agricultural drought risk management in the four southern provinces of China. Full article

(This article belongs to the Special Issue Soil Moisture and Land Surface Processes: Observation, Modeling and Coupling Analysis)

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12 pages, 3079 KiB

Open AccessTechnical Note

Soil Moisture Change Detection with Sentinel-1 SAR Image for Slow Onsetting Disasters: An Investigative Study Using Index Based Method

by Arnob Bormudoi, Masahiko Nagai, Vaibhav Katiyar, Dorj Ichikawa and Tsuyoshi Eguchi

Land 2023, 12(2), 506; https://doi.org/10.3390/land12020506 - 17 Feb 2023

Viewed by 1832

Abstract

Understanding physical processes in nature, including the occurrence of slow-onset natural disasters such as droughts and landslides, requires knowledge of the change in soil moisture between two points in time. The study was conducted on a relatively bare soil, and the change in soil moisture was examined with an index called Normalized radar Backscatter soil Moisture Index (NBMI) using Sentinel-1 satellite data. Along with soil moisture measured with a probe on the ground, a study of correlation with satellite imagery was conducted using a Multiple Linear Regression (MLR) model. Furthermore, the Dubois model was used to predict soil moisture. Results have shown that NBMI on a logarithmic scale provides a good representation of soil moisture change with R²~86%. The MLR model showed a positive correlation of soil moisture with the co-polarized backscatter coefficient, but an opposite correlation with the surface roughness and angle of incidence. The results of the Dubois model showed poor correlation of 44.37% and higher RMSE error of 17.1, demonstrating the need for detailed and accurate measurement of surface roughness as a prerequisite for simulating the model. Of the three approaches, index-based measurement has been shown to be the most rapid for understanding soil moisture change and has the potential to be used for understanding some mechanisms of natural disasters under similar soil conditions. Full article

(This article belongs to the Special Issue Soil Moisture and Land Surface Processes: Observation, Modeling and Coupling Analysis)

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