Soil moisture content retrieval over meadows from Sentinel-1 and Sentinel-2 data using physically based scattering models

Benninga, Harm-Jan F.; Velde, R. van der; Su, Zhongbo

doi:10.1016/j.rse.2022.113191

Cited by 12 publications

(8 citation statements)

References 84 publications

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“…In the literature, several studies have outlined many limitations in the S1 C-band SM estimations, including but not limited to, the failure of estimating soil moisture when the vegetation of certain crops is highly developed [18,25,26], the sensitivity of the C-band radar signal only to the first cm of the soil [27] and the need to have low incidence angles for optimizing the estimation of soil moisture [28], as well as the difficulty of correctly estimating moisture content for soils with very high or very low roughness [9,29].…”

Section: Methodsmentioning

confidence: 99%

Retrieving Soil Moisture from Sentinel-1: Limitations over Certain Crops and Sensitivity to the First Soil Thin Layer

Bazzi,

Baghdadi,

Nino

et al. 2023

Water

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This paper presents a comparison between the Sentinel-1 (S1)/Sentinel-2 (S2)-derived soil moisture products at plot scale (S2MP) and in situ soil moisture measurements at a 10 cm depth for several winter and summer crops. Specifically, the paper discusses the consistency between the in situ soil moisture measurements, usually performed at a 10 cm soil depth, and the variable S1 C-band penetration depth in soil due to soil humidity conditions, vegetation development and S1 acquisition configuration. The aim is to provide end users with the strength and limitations of S1-derived soil moisture, mainly the S2MP soil moisture product, for their further applications. Both the estimated and measured soil moisture (SM) were evaluated over three testing fields in a Mediterranean climatic context, with crop cycles including wheat, tomato, cover crops and soybeans. The main results showed that the comparison between the S2MP-estimated SM based on S1 backscattering (at ~5 cm depth) with a 10 cm in situ SM is not always relevant during the crop cycle. In dry conditions, the S1 SM significantly underestimated the 10 cm SM measurements with an underestimation that could reach around 20 vol.% in some extremely dry conditions. This high underestimation was mainly due to the difference between the topsoil SM captured by the S1 sensor and the 10 cm in depth SM. Moderately wet conditions due to rainfall or irrigation showed less of a difference between the S1-estimated SM and the 10 cm in situ SM and varying between −10 and −5 vol.% due to the homogeneity of the SM at different soil depths. For extremely wet conditions, the S1 SM started to underestimate the SM values with an underestimation that can reach an order of −10 vol.%. A comparison of the S1-estimated SM as a function of the vegetation development showed that, for the studied crop types, the S1 SM estimates are only valid for low and moderate vegetation cover with a Normalized Difference Vegetation Index (NDVI) of less than 0.7. For dense vegetation cover (NDVI > 0.7), overestimations of the SM (average bias of about 4 vol.%) are mainly observed for developed tomato and soybean crops due to fruits’ emergence, whereas an extreme underestimation (average bias reaching −15.5 vol.%) is found for developed wheat cover due to the vertical structure of the wheat kernels. The results also suggest that the optimal SM estimations by S1 could be mainly obtained at low radar incidence angles (incidence angle less than 35°).

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Section: Methodsmentioning

confidence: 99%

Retrieving Soil Moisture from Sentinel-1: Limitations over Certain Crops and Sensitivity to the First Soil Thin Layer

Bazzi,

Baghdadi,

Nino

et al. 2023

Water

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“…Merging data from RS products generally provides better predictions than using a single RS product (e.g., using a time series to increase both the prediction accuracy and map coverage [352], using synergistic information to retrieve soil information [353] or fusing products to increase both spatial and temporal resolution (e.g., [354][355][356][357])).…”

Section: Relative Permanence Of Soil Properties and Revisit Time For ...mentioning

confidence: 99%

Remote Sensing Data for Digital Soil Mapping in French Research—A Review

et al. 2023

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Soils are at the crossroads of many existential issues that humanity is currently facing. Soils are a finite resource that is under threat, mainly due to human pressure. There is an urgent need to map and monitor them at field, regional, and global scales in order to improve their management and prevent their degradation. This remains a challenge due to the high and often complex spatial variability inherent to soils. Over the last four decades, major research efforts in the field of pedometrics have led to the development of methods allowing to capture the complex nature of soils. As a result, digital soil mapping (DSM) approaches have been developed for quantifying soils in space and time. DSM and monitoring have become operational thanks to the harmonization of soil databases, advances in spatial modeling and machine learning, and the increasing availability of spatiotemporal covariates, including the exponential increase in freely available remote sensing (RS) data. The latter boosted research in DSM, allowing the mapping of soils at high resolution and assessing the changes through time. We present a review of the main contributions and developments of French (inter)national research, which has a long history in both RS and DSM. Thanks to the French SPOT satellite constellation that started in the early 1980s, the French RS and soil research communities have pioneered DSM using remote sensing. This review describes the data, tools, and methods using RS imagery to support the spatial predictions of a wide range of soil properties and discusses their pros and cons. The review demonstrates that RS data are frequently used in soil mapping (i) by considering them as a substitute for analytical measurements, or (ii) by considering them as covariates related to the controlling factors of soil formation and evolution. It further highlights the great potential of RS imagery to improve DSM, and provides an overview of the main challenges and prospects related to digital soil mapping and future sensors. This opens up broad prospects for the use of RS for DSM and natural resource monitoring.

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“…From the crop parcel registry in 2015, we find that the agricultural land is covered 70.8 % by grass meadows, 22.4 % by maize, and the remaining 6.8 % is used for potatoes, cereals, and other crops. The grass-growing season is generally from March to November, during which the meadows are either grazed by cattle or cut four to six times per year (Benninga et al, 2022). Maize is planted in the months of April/May and harvested for silage in the period from September to November depending on the vehicle-bearing capacity of the land and growing conditions, in particular the dry matter content of the plants.…”

Section: Land Use Information Is Publicly Available From Statisticsmentioning

confidence: 99%

“…The large contrast between the relative electric permittivity (ε r ) of dry soil (3-5) and water (80) and its relative insensitivity to variations in salinity and soil texture have made electromagnetic field sensors operating at frequencies below 1 GHz the standard non-destructive measurement technique used for regional-scale soil moisture monitoring networks (e.g. Martínez-Fernández and Cebalos, 2005;Calvet et al, 2007;Bircher et al, 2012;Smith et al, 2012;Benninga et al, 2018;Bogena et al, 2018;Caldwell et al, 2019;Tetlock et al, 2019). Despite technological advances having facilitated a substantial increase in the worldwide monitoring infrastructure, in situ monitoring networks providing long-term soil moisture data records are still very scarce (GCOS, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Dente et al (2011) described the early development, and the first scientific use of the data was the validation of SMOS soil moisture products (Dente et al, 2012). Other studies performed with the datasets have focused on field-scale soil moisture retrieval Benninga et al, 2020Benninga et al, , 2022, upscaling of point measurements to coarse satellite footprints (Van der Velde et al, 2021), and agricultural and hydrological applications (Carranza et al, 2018(Carranza et al, , 2019Pezij et al, 2019;Buitink et al, 2020), and the Twente network has been used as one of the core international validation sites for the SMAP surface soil moisture products (Colliander et al, 2017;Chan et al, 2018;Chaubell et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Twelve years of profile soil moisture and temperature measurements in Twente, the Netherlands

Velde¹,

Benninga²,

Retsios³

et al. 2023

Earth Syst. Sci. Data

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Abstract. Spread across Twente and its neighbouring regions in the east of the Netherlands, a network of 20 profile soil moisture and temperature (5, 10, 20, 40, and 80 cm depths) monitoring stations was established in 2009. Field campaigns have been conducted covering the growing seasons of 2009, 2015, 2016, and 2017, during which soil sampling rings and handheld probes were used to measure the top 5 cm volumetric soil moisture content (VSM) of 28 fields near 12 monitoring stations. In this paper, we describe the design of the monitoring network and the field campaigns, adopted instrumentation, experimental setup, field sampling strategies, and the development of sensor calibration functions. Maintenance and quality control procedures and issues specific to the Twente network are discussed. Moreover, we provide an overview of open third-party datasets (i.e. land cover/use, soil information, elevation, groundwater, and meteorological observations) that can support the use and analysis of the Twente soil moisture and temperature datasets beyond the scope of this contribution. An indication for the spatial representativeness of the permanent monitoring stations is provided through comparisons of the 5 cm station measurements with the top 5 cm field-averaged VSM derived from the field campaign measurements. The results reveal in general reasonable agreements and root mean squared errors that are dominated by underestimations of the field-averaged VSM, which is particularly apparent for the grass fields and is strong after heavy rain. Further, we discuss the prospects the datasets offer to investigate (i) the reliability of soil moisture references that serve the development and validation of soil moisture products, and (ii) the water and energy exchanges across the groundwater–vadose-zone–atmosphere continuum within a lowland environment in a changing climate. The datasets discussed are publicly available at https://doi.org/10.17026/dans-znj-wyg5 (Van der Velde et al., 2022).

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Soil moisture content retrieval over meadows from Sentinel-1 and Sentinel-2 data using physically based scattering models

Cited by 12 publications

References 84 publications

Retrieving Soil Moisture from Sentinel-1: Limitations over Certain Crops and Sensitivity to the First Soil Thin Layer

Retrieving Soil Moisture from Sentinel-1: Limitations over Certain Crops and Sensitivity to the First Soil Thin Layer

Remote Sensing Data for Digital Soil Mapping in French Research—A Review

Twelve years of profile soil moisture and temperature measurements in Twente, the Netherlands

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