Estimating soil moisture using Sentinel-1 and Sentinel-2 sensors for dryland and palustrine wetland areas

Gangat, Ridhwannah; Deventer, Heidi van; Naidoo, Laven; Adam, Elhadi

doi:10.17159/sajs.2020/6535

Cited by 16 publications

(9 citation statements)

References 32 publications

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“…Measurements through the spectroradiometer were conducted on 26-27 th January, 28 th February-2 nd March, 24-25 th March, 30-31 st March, and 4-5 th April 2022. [21][22]. The bands used are bands with a spatial resolution of 10 m and 20 m; consequently, the band with a resolution of 60 m is not used.…”

Section: Field Data Collectionmentioning

confidence: 99%

Spectral pattern analysis of rice varieties with proximal sensing method and sentinel-2 imagery

Maarif

Barus

Iman

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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Rice growth monitoring through remote sensing devices, such as Sentinel-2 imagery has been widely used, yet the accuracy level is different due to variations in altitude and object reflectance. Proximal sensing provides site (ground-level) monitoring via high-resolution sensors. Spectroradiometer is a proximal sensing device that can continuously record the spectral pattern of an object. This research aims to examine the spectral pattern characteristic of Grendel sticky and Ciherang rice varieties based on proximal sensing and to investigate the relationship between the reflectance value of spectroradiometer measurements and Sentinel-2 image extraction. Based on the spectral pattern analysis, the Ciherang variety reached the maximum reflectance value for red-edge and NIR wavelength at 56 DAP (reproductive stage) and then decreased at 81 DAP (ripening stage). In comparison, the Grendel variety reached 81 DAP (reproductive stage) and dropped to 86 DAP (ripening stage). Based on linear regression, at 56, 81, 86, and 90, DAP indicated the reflectance values of spectroradiometer measurement are equivalent to the value of sentinel-2 image extraction with an average of R2 above 86%.

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Section: Field Data Collectionmentioning

confidence: 99%

Spectral pattern analysis of rice varieties with proximal sensing method and sentinel-2 imagery

Maarif

Barus

Iman

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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“…For different model-based approaches, retrieving SSM data requires a fusion of the data. The fusion of Sentinel-1 and Sentinel-2 satellite data showed successful applications and accurate results whether in global or field-based SMM estimations [165][166][167][168][169]. There are several different machine learning or empirical-based approaches for this purpose: the Water cloud model [149,168,170,171], change detection method [65,169,172], ANN [149], cost function [173], and support vector regression [174].…”

Section: Remote Sensing Of Soil Moisture In Droughtmentioning

confidence: 99%

Reviewing the Potential of Sentinel-2 in Assessing the Drought

et al. 2021

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This paper systematically reviews the potential of the Sentinel-2 (A and B) in assessing drought. Research findings, including the IPCC reports, highlighted the increasing trend in drought over the decades and the need for a better understanding and assessment of this phenomenon. Continuous monitoring of the Earth’s surface is an efficient method for predicting and identifying the early warnings of drought, which enables us to prepare and plan the mitigation procedures. Considering the spatial, temporal, and spectral characteristics, the freely available Sentinel-2 data products are a promising option in this area of research, compared to Landsat and MODIS. This paper evaluates the recent developments in this field induced by the launch of Sentinel-2, as well as the comparison with other existing data products. The objective of this paper is to evaluate the potential of Sentinel-2 in assessing drought through vegetation characteristics, soil moisture, evapotranspiration, surface water including wetland, and land use and land cover analysis. Furthermore, this review also addresses and compares various data fusion methods and downscaling methods applied to Sentinel-2 for retrieving the major bio-geophysical variables used in the analysis of drought. Additionally, the limitations of Sentinel-2 in its direct applicability to drought studies are also evaluated.

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“…Advances in satellite technology, data processing, and petrophysics ensure that these techniques will be increasingly important in the future. Many studies have used satellite data, such as the Sentinel and Landsat series, to observe and predict soil properties such as pH [21,22], cation exchange capacity [22], soil organic carbon [22,23], soil organic matter [21,24], clay content [21,22,24], salinity [25], and soil water content estimation [26][27][28][29][30][31][32]. Reference [24] showed that using Sentinel-2 satellite data to identify differences in soil properties can guide on-ground soil sampling and significantly reduce the time and cost of conventional sampling efforts.…”

Section: Introductionmentioning

confidence: 99%

“…Sentinel-2 satellites have been especially useful for soil characterization; data from these satellites are relatively high resolution, contain red-edge band data, and have a 10-day re-visit frequency for the single satellite and 5-day for two satellites under cloud-free conditions [33,34]. The main disadvantage of Sentinel-2 data for soil characterization is that the resolution is currently insufficient for some types of precision agriculture, as the spatial resolution ranges from 10 m to 60 m [31,35]. Additionally, the penetration depth for the Sentinel-2 is also relatively shallow (~5 cm in most soils) [26,31], and estimation of some soil properties is less effective if the vegetation is extensive [21][22][23][24]31].…”

Section: Introductionmentioning

confidence: 99%

“…The main disadvantage of Sentinel-2 data for soil characterization is that the resolution is currently insufficient for some types of precision agriculture, as the spatial resolution ranges from 10 m to 60 m [31,35]. Additionally, the penetration depth for the Sentinel-2 is also relatively shallow (~5 cm in most soils) [26,31], and estimation of some soil properties is less effective if the vegetation is extensive [21][22][23][24]31].…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Soil Water Content and Electrical Conductivity Using Random Forest Methods with UAV Multispectral and Ground-Coupled Geophysical Data

et al. 2022

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The volumetric water content (VWC) of soil is a critical parameter in agriculture, as VWC strongly influences crop yield, provides nutrients to plants, and maintains the microbes that are needed for the biological health of the soil. Measuring VWC is difficult, as it is spatially and temporally heterogeneous, and most agricultural producers use point measurements that cannot fully capture this parameter. Electrical conductivity (EC) is another soil parameter that is useful in agriculture, since it can be used to indicate soil salinity, soil texture, and plant nutrient availability. Soil EC is also very heterogeneous; measuring EC using conventional soil sampling techniques is very time consuming and often fails to capture the variability in EC at a site. In contrast to the point-based methods used to measure VWC and EC, multispectral data acquired with unmanned aerial vehicles (UAV) can cover large areas with high resolution. In agriculture, multispectral data are often used to calculate vegetation indices (VIs). In this research UAV-acquired VIs and raw multispectral data were used to predict soil VWC and EC. High-resolution geophysical methods were used to acquire more than 41,000 measurements of VWC and 8,000 measurements of EC in 18 traverses across a field that contained 56 experimental plots. The plots varied by crop type (corn, soybeans, and alfalfa) and drainage (no drainage, moderate drainage, high drainage). Machine learning was performed using the random forest method to predict VWC and EC using VIs and multispectral data. Prediction accuracy was determined for several scenarios that assumed different levels of knowledge about crop type or drainage. Results showed that multispectral data improved prediction of VWC and EC, and the best predictions occurred when both the crop type and degree of drainage were known, but drainage was a more important input than crop type. Predictions were most accurate in drier soil, which may be due to the lower overall variability of VWC and EC under these conditions. An analysis of which multispectral data were most important showed that NDRE, VARI, and blue band data improved predictions the most. The final conclusions of this study are that inexpensive UAV-based multispectral data can be used to improve estimation of heterogenous soil properties, such as VWC and EC in active agricultural fields. In this study, the best estimates of these properties were obtained when the agriculture parameters in a field were fairly homogeneous (one crop type and the same type of drainage throughout the field), although improvements were observed even when these conditions were not met. The multispectral data that were most useful for prediction were those that penetrated deeper into the soil canopy or were sensitive to bare soil.

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Estimating soil moisture using Sentinel-1 and Sentinel-2 sensors for dryland and palustrine wetland areas

Cited by 16 publications

References 32 publications

Spectral pattern analysis of rice varieties with proximal sensing method and sentinel-2 imagery

Spectral pattern analysis of rice varieties with proximal sensing method and sentinel-2 imagery

Reviewing the Potential of Sentinel-2 in Assessing the Drought

Prediction of Soil Water Content and Electrical Conductivity Using Random Forest Methods with UAV Multispectral and Ground-Coupled Geophysical Data

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