C-band radar data and in situ measurements for the monitoring of wheat crops in a semi-arid area (center of Morocco)

Abstract. A better understanding of the hydrological functioning of irrigated crops using remote sensing observations is of prime importance in the semi-arid areas where the water resources are limited. Radar observations, available at high resolution and revisit time since the launch of Sentinel-1 in 2014, have shown great potential for the monitoring of the water content of the upper soil and of the canopy. In this paper, a complete set of data for radar signal analysis is shared to the scientific community for the first time to our knowledge. The data set is composed of Sentinel-1 products and in situ measurements of soil and vegetation variables collected during three agricultural seasons over drip-irrigated winter wheat in the Haouz plain in Morocco. The in situ data gathers soil measurements (time series of half-hourly surface soil moisture, surface roughness and agricultural practices) and vegetation measurements collected every week/two weeks including above-ground fresh and dry biomasses, vegetation water content based on destructive measurements, cover fraction, leaf area index and plant height. Radar data are the backscattering coefficient and the interferometric coherence derived from Sentinel-1 GRDH (Ground Range Detected High resolution) and SLC (Single Look Complex) products, respectively. The normalized difference vegetation index derived from Sentinel-2 data based on Level-2A (surface reflectance and cloud mask) atmospheric effects-corrected products is also provided. This database, which is the first of its kind made available in open access, is described here comprehensively in order to help the scientific community to evaluate and to develop new or existing remote sensing algorithms for monitoring wheat canopy under semi-arid conditions. The data set is particularly relevant for the development of radar applications including surface soil moisture and vegetation parameters retrieval using either physically based or empirical approaches such as machine and deep learning algorithms. The database is archived in the DataSuds repository and is freely-accessible via the DOI: https://doi.org/10.23708/8D6WQC (Ouaadi et al., 2020a).

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C-band radar data and in situ measurements for the monitoring of wheat crops in a semi-arid area (center of Morocco)

Earth System Science Data ◽

10.5194/essd-13-3707-2021 ◽

2021 ◽

Vol 13 (7) ◽

pp. 3707-3731

Author(s):

Nadia Ouaadi ◽

Jamal Ezzahar ◽

Saïd Khabba ◽

Salah Er-Raki ◽

Adnane Chakir ◽

...

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

High Resolution ◽

Scientific Community ◽

Surface Soil ◽

Radar Data ◽

Surface Soil Moisture ◽

Data Set ◽

Semi Arid

Abstract. A better understanding of the hydrological functioning of irrigated crops using remote sensing observations is of prime importance in semi-arid areas where water resources are limited. Radar observations, available at high resolution and with a high revisit time since the launch of Sentinel-1 in 2014, have shown great potential for the monitoring of the water content of the upper soil and of the canopy. In this paper, a complete set of data for radar signal analysis is shared with the scientific community for the first time to our knowledge. The data set is composed of Sentinel-1 products and in situ measurements of soil and vegetation variables collected during three agricultural seasons over drip-irrigated winter wheat in the Haouz plain in Morocco. The in situ data gather soil measurements (time series of half-hourly surface soil moisture, surface roughness and agricultural practices) and vegetation measurements collected every week/2 weeks including aboveground fresh and dry biomasses, vegetation water content based on destructive measurements, the cover fraction, the leaf area index, and plant height. Radar data are the backscattering coefficient and the interferometric coherence derived from Sentinel-1 GRDH (Ground Range Detected High Resolution) and SLC (Single Look Complex) products, respectively. The normalized difference vegetation index derived from Sentinel-2 data based on Level-2A (surface reflectance and cloud mask) atmospheric-effects-corrected products is also provided. This database, which is the first of its kind made available open access, is described here comprehensively in order to help the scientific community to evaluate and to develop new or existing remote sensing algorithms for monitoring wheat canopy under semi-arid conditions. The data set is particularly relevant for the development of radar applications including surface soil moisture and vegetation variable retrieval using either physically based or empirical approaches such as machine and deep learning algorithms. The database is archived in the DataSuds repository and is freely accessible via the following DOI: https://doi.org/10.23708/8D6WQC (Ouaadi et al., 2020a).

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The Indian COSMOS Network (ICON): Validating L-Band Remote Sensing and Modelled Soil Moisture Data Products

Remote Sensing ◽

10.3390/rs13030537 ◽

2021 ◽

Vol 13 (3) ◽

pp. 537

Author(s):

Deepti B Upadhyaya ◽

Jonathan Evans ◽

Sekhar Muddu ◽

Sat Kumar Tomer ◽

Ahmad Al Bitar ◽

...

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Water Resource Management ◽

Surface Soil ◽

Root Zone ◽

Cosmic Ray ◽

Surface Soil Moisture ◽

Data Set ◽

Scale Difference

Availability of global satellite based Soil Moisture (SM) data has promoted the emergence of many applications in climate studies, agricultural water resource management and hydrology. In this context, validation of the global data set is of substance. Remote sensing measurements which are representative of an area covering 100 m2 to tens of km2 rarely match with in situ SM measurements at point scale due to scale difference. In this paper we present the new Indian Cosmic Ray Network (ICON) and compare it’s data with remotely sensed SM at different depths. ICON is the first network in India of the kind. It is operational since 2016 and consist of seven sites equipped with the COSMOS instrument. This instrument is based on the Cosmic Ray Neutron Probe (CRNP) technique which uses non-invasive neutron counts as a measure of soil moisture. It provides in situ measurements over an area with a radius of 150–250 m. This intermediate scale soil moisture is of interest for the validation of satellite SM. We compare the COSMOS derived soil moisture to surface soil moisture (SSM) and root zone soil moisture (RZSM) derived from SMOS, SMAP and GLDAS_Noah. The comparison with surface soil moisture products yield that the SMAP_L4_SSM showed best performance over all the sites with correlation (R) values ranging from 0.76 to 0.90. RZSM on the other hand from all products showed lesser performances. RZSM for GLDAS and SMAP_L4 products show that the results are better for the top layer R = 0.75 to 0.89 and 0.75 to 0.90 respectively than the deeper layers R = 0.26 to 0.92 and 0.6 to 0.8 respectively in all sites in India. The ICON network will be a useful tool for the calibration and validation activities for future SM missions like the NASA-ISRO Synthetic Aperture Radar (NISAR).

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Development of a deep learning-based method for estimating surface soil moisture at high spatial resolution from Sentinel-1 satellite data

10.5194/egusphere-egu21-9493 ◽

2021 ◽

Author(s):

Nicklas Simonsen ◽

Zheng Duan

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Deep Learning ◽

High Resolution ◽

Moisture Content ◽

Spatial Resolution ◽

Soil Moisture Content ◽

Surface Soil ◽

Surface Soil Moisture

Soil moisture content is an important hydrological and climatic variable with applications in a wide range of domains. The high spatial variability of soil moisture cannot be well captured from conventional point-based in-situ measurements. Remote sensing offers a feasible way to observe spatial pattern of soil moisture from regional to global scales. Microwave remote sensing has long been used to estimate Surface Soil Moisture Content (SSMC) at lower spatial resolutions (>1km), but few accurate options exist in the higher spatial resolution (<1km) domain. This study explores the capabilities of deep learning in the high-resolution domain of remotely sensed SSMC by using a Convolutional Neural Network (CNN) to estimate SSMC from Sentinel-1 acquired Synthetic Aperture Radar (SAR) imagery. The developed model incorporates additional SSMC predictors such as Normalized Difference Vegetation Index (NDVI), temperature, precipitation, and soil type to yield a more accurate estimation than traditional empirical formulas that focus solely on the conversion of backscatter signals to relative soil moisture. This also makes the developed model less sensitive to site-specific conditions and increases the model applicability outside the training domain. The model is developed and tested with in-situ soil moisture measurements in Denmark from a dense network maintained by HOBE (Danish Hydrological Observatory). The unique advantage of the developed model is its transferability across climate zones, which has been historically absent in many prior models. This would open up opportunities for high-resolution soil moisture mapping through remote sensing in areas with relatively few soil moisture gauges. A reliable high-resolution soil moisture platform at good temporal resolution would allow for more precise erosion modelling, flood forecasting, drought monitoring, and precision agriculture.

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From near-surface to root-zone soil moisture using an exponential filter: an assessment of the method based on in-situ observations and model simulations

Hydrology and Earth System Sciences ◽

10.5194/hess-12-1323-2008 ◽

2008 ◽

Vol 12 (6) ◽

pp. 1323-1337 ◽

Cited By ~ 261

Author(s):

C. Albergel ◽

C. Rüdiger ◽

T. Pellarin ◽

J.-C. Calvet ◽

N. Fritz ◽

...

Keyword(s):

Soil Moisture ◽

Soil Water ◽

Surface Soil ◽

Root Zone ◽

Synthetic Data ◽

Surface Soil Moisture ◽

Data Set ◽

Water Index ◽

In Situ Observations

Abstract. A long term data acquisition effort of profile soil moisture is under way in southwestern France at 13 automated weather stations. This ground network was developed in order to validate remote sensing and model soil moisture estimates. In this paper, both those in situ observations and a synthetic data set covering continental France are used to test a simple method to retrieve root zone soil moisture from a time series of surface soil moisture information. A recursive exponential filter equation using a time constant, T, is used to compute a soil water index. The Nash and Sutcliff coefficient is used as a criterion to optimise the T parameter for each ground station and for each model pixel of the synthetic data set. In general, the soil water indices derived from the surface soil moisture observations and simulations agree well with the reference root-zone soil moisture. Overall, the results show the potential of the exponential filter equation and of its recursive formulation to derive a soil water index from surface soil moisture estimates. This paper further investigates the correlation of the time scale parameter T with soil properties and climate conditions. While no significant relationship could be determined between T and the main soil properties (clay and sand fractions, bulk density and organic matter content), the modelled spatial variability and the observed inter-annual variability of T suggest that a weak climate effect may exist.

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Machine Learning to Estimate Surface Soil Moisture from Remote Sensing Data

Water ◽

10.3390/w12113223 ◽

2020 ◽

Vol 12 (11) ◽

pp. 3223

Author(s):

Hamed Adab ◽

Renato Morbidelli ◽

Carla Saltalippi ◽

Mahmoud Moradian ◽

Gholam Abbas Fallah Ghalhari

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Soil Moisture ◽

Surface Soil ◽

Remote Sensing Data ◽

Surface Soil Moisture ◽

Machine Learning Methods ◽

Land Use Types ◽

Semi Arid Region ◽

Semi Arid

Soil moisture is an integral quantity parameter in hydrology and agriculture practices. Satellite remote sensing has been widely applied to estimate surface soil moisture. However, it is still a challenge to retrieve surface soil moisture content (SMC) data in the heterogeneous catchment at high spatial resolution. Therefore, it is necessary to improve the retrieval of SMC from remote sensing data, which is important in the planning and efficient use of land resources. Many methods based on satellite-derived vegetation indices have already been developed to estimate SMC in various climatic and geographic conditions. Soil moisture retrievals were performed using statistical and machine learning methods as well as physical modeling techniques. In this study, an important experiment of soil moisture retrieval for investigating the capability of the machine learning methods was conducted in the early spring season in a semi-arid region of Iran. We applied random forest (RF), support vector machine (SVM), artificial neural network (ANN), and elastic net regression (EN) algorithms to soil moisture retrieval by optical and thermal sensors of Landsat 8 and knowledge of land-use types on previously untested conditions in a semi-arid region of Iran. The statistical comparisons show that RF method provided the highest Nash–Sutcliffe efficiency value (0.73) for soil moisture retrieval covered by the different land-use types. Combinations of surface reflectance and auxiliary geospatial data can provide more valuable information for SMC estimation, which shows promise for precision agriculture applications.

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Spatiotemporal monitoring of surface soil moisture using optical remote sensing data: a case study in a semi-arid area

Journal of Spatial Science ◽

10.1080/14498596.2018.1499559 ◽

2018 ◽

Vol 65 (3) ◽

pp. 481-499 ◽

Cited By ~ 4

Author(s):

Rida Khellouk ◽

Ahmed Barakat ◽

Abdelghani Boudhar ◽

Rachid Hadria ◽

Hayat Lionboui ◽

...

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Surface Soil ◽

Remote Sensing Data ◽

Arid Area ◽

Optical Remote Sensing ◽

Surface Soil Moisture ◽

Sensing Data ◽

Semi Arid

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New Downscaling Approach Using ESA CCI SM Products for Obtaining High Resolution Surface Soil Moisture

Remote Sensing ◽

10.3390/rs12071119 ◽

2020 ◽

Vol 12 (7) ◽

pp. 1119 ◽

Cited By ~ 2

Author(s):

Jovan Kovačević ◽

Željko Cvijetinović ◽

Nikola Stančić ◽

Nenad Brodić ◽

Dragan Mihajlović

Keyword(s):

Soil Moisture ◽

High Resolution ◽

Random Forest ◽

Spatial Resolution ◽

Surface Soil ◽

Initial Step ◽

Surface Soil Moisture ◽

In Situ Data ◽

Random Forest Models

ESA CCI SM products have provided remotely-sensed surface soil moisture (SSM) content with the best spatial and temporal coverage thus far, although its output spatial resolution of 25 km is too coarse for many regional and local applications. The downscaling methodology presented in this paper improves ESA CCI SM spatial resolution to 1 km using two-step approach. The first step is used as a data engineering tool and its output is used as an input for the Random forest model in the second step. In addition to improvements in terms of spatial resolution, the approach also considers the problem of data gaps. The filling of these gaps is the initial step of the procedure, which in the end produces a continuous product in both temporal and spatial domains. The methodology uses combined active and passive ESA CCI SM products in addition to in situ soil moisture observations and the set of auxiliary downscaling predictors. The research tested several variants of Random forest models to determine the best combination of ESA CCI SM products. The conclusion is that synergic use of all ESA CCI SM products together with the auxiliary datasets in the downscaling procedure provides better results than using just one type of ESA CCI SM product alone. The methodology was applied for obtaining SSM maps for the area of California, USA during 2016. The accuracy of tested models was validated using five-fold cross-validation against in situ data and the best variation of model achieved RMSE, R2 and MAE of 0.0518 m3/m3, 0.7312 and 0.0374 m3/m3, respectively. The methodology proved to be useful for generating high-resolution SSM products, although additional improvements are necessary.

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Modelling soil moisture at SMOS scale by use of a SVAT model over the Valencia Anchor Station

Hydrology and Earth System Sciences ◽

10.5194/hess-14-831-2010 ◽

2010 ◽

Vol 14 (5) ◽

pp. 831-846 ◽

Cited By ~ 30

Author(s):

S. Juglea ◽

Y. Kerr ◽

A. Mialon ◽

J.-P. Wigneron ◽

E. Lopez-Baeza ◽

...

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Surface Soil ◽

Temporal Distribution ◽

Sea Surface Salinity ◽

Surface Soil Moisture ◽

Surface Salinity ◽

Anchor Station ◽

Svat Model

Abstract. The main goal of the SMOS (Soil Moisture and Ocean Salinity) mission is to deliver global fields of surface soil moisture and sea surface salinity using L-band (1.4 GHz) radiometry. Within the context of the Science preparation for SMOS, the Valencia Anchor Station (VAS) experimental site, in Spain, was chosen to be one of the main test sites in Europe for Calibration/Validation (Cal/Val) activities. In this framework, the paper presents an approach consisting in accurately simulating a whole SMOS pixel by representing the spatial and temporal heterogeneity of the soil moisture fields over the wide VAS surface (50×50 km2). Ground and meteorological measurements over the area are used as the input of a Soil-Vegetation-Atmosphere-Transfer (SVAT) model, SURFEX (Externalized Surface) - module ISBA (Interactions between Soil-Biosphere-Atmosphere) to simulate the spatial and temporal distribution of surface soil moisture. The calibration as well as the validation of the ISBA model are performed using in situ soil moisture measurements. It is shown that a good consistency is reached when point comparisons between simulated and in situ soil moisture measurements are made. Actually, an important challenge in remote sensing approaches concerns product validation. In order to obtain an representative soil moisture mapping over the Valencia Anchor Station (50×50 km2 area), a spatialization method is applied. For verification, a comparison between the simulated spatialized soil moisture and remote sensing data from the Advanced Microwave Scanning Radiometer on Earth observing System (AMSR-E) and from the European Remote Sensing Satellites (ERS-SCAT) is performed. Despite the fact that AMSR-E surface soil moisture product is not reproducing accurately the absolute values, it provides trustworthy information on surface soil moisture temporal variability. However, during the vegetation growing season the signal is perturbed. By using the polarization ratio a better agreement is obtained. ERS-SCAT soil moisture products are also used to be compared with the simulated spatialized soil moisture. However, the lack of soil moisture data from the ERS-SCAT sensor over the area (45 observations for one year) prevented capturing the soil moisture variability.

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Modelling soil moisture at SMOS scale by use of a SVAT model over the Valencia Anchor Station

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-7-649-2010 ◽

2010 ◽

Vol 7 (1) ◽

pp. 649-686 ◽

Cited By ~ 2

Author(s):

S. Juglea ◽

Y. Kerr ◽

A. Mialon ◽

J.-P. Wigneron ◽

E. Lopez-Baeza ◽

...

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Surface Soil ◽

Remote Sensing Data ◽

Sea Surface Salinity ◽

Surface Soil Moisture ◽

Surface Salinity ◽

Anchor Station ◽

Svat Model

Abstract. The main goal of the SMOS (Soil Moisture and Ocean Salinity) mission is to deliver global fields of surface soil moisture and sea surface salinity using L-band (1.4 GHz) radiometry. Within the context of the preparation for this mission over land, the Valencia Anchor Station experimental site, in Spain, was chosen to be one of the main test sites in Europe for the SMOS Calibration/Validation (Cal/Val) activities. Ground and meteorological measurements over the area are used as the input of a Soil-Vegetation-Atmosphere-Transfer (SVAT) model, SURFEX (Externalized Surface)-module ISBA (Interactions between Soil-Biosphere-Atmosphere) so as to simulate the surface soil moisture. The calibration as well as the validation of the ISBA model was made using in situ soil moisture measurements. It is shown that a good consistency was reached when point comparisons between simulated and in situ soil moisture measurements were made. In order to obtain an accurate soil moisture mapping over the Valencia Anchor Station (50×50 km2 area), a spatialization method has been applied. To validate the approach, a comparison with remote sensing data from the Advanced Microwave Scanning Radiometer on Earth observing System (AMSR-E) and from the European Remote Sensing Satellites (ERS-Scat) was performed. Despite the fact that AMSR-E surface soil moisture product is not reproducing accurately the absolute values, it provides trustworthy information on surface soil moisture temporal variability. However, during the vegetation growing season the signal is perturbed. By using the polarization ratio a better agreement is obtained. ERS-Scat soil moisture products were also used to be compared with the simulated spatialized soil moisture. The seasonal variations were well reproduced. However, the lack of soil moisture data over the area (45 observations for one year) was a limit into completely understanding the soil moisture variability.

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Integrative soil moisture monitoring in Switzerland for a better preparedness for projected drying trends

10.5194/egusphere-egu21-9483 ◽

2021 ◽

Author(s):

Dominik Michel ◽

Martin Hirschi ◽

Sonia I. Seneviratne

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Real Time ◽

Surface Soil ◽

Microwave Remote Sensing ◽

Data Availability ◽

Climate Projections ◽

Surface Soil Moisture ◽

Soil Moisture Monitoring

Climate projections indicate an increasing risk of dry and hot episodes in Central Europe, including in Switzerland. However, models display a large spread in projections of changes in summer drying, highlighting the importance of related observations to evaluate climate models and constrain projections. Land hydrological variables play an essential role for these projections. This is particularly the case for soil moisture and land evaporation, which are directly affecting the development of droughts and heatwaves in both present and future.The recent 2020 spring as well as 2015 and 2018 summer droughts in Switzerland have highlighted the importance of monitoring and assessing changes of soil moisture and land evaporation, which are strongly related to drought impacts on agriculture, forestry, and ecosystems. The country was affected by major drought and heatwave conditions in 2015 and 2018. While the meteorological conditions started to recover at the end of the summer, the soil moisture conditions (and runoff) continued to be anomalously low for most of the fall. This illustrates the decoupling between meteorological drought and soil moisture drought conditions related to the intrinsic memory of the soil.The only Switzerland-wide soil moisture monitoring programme currently in place is the SwissSMEX (Swiss Soil Moisture Experiment) measurement network. It was initiated in 2008 and comprises 19 soil moisture measurement profiles at 17 different sites (grassland, forest and arable land). Since 2017, seven grassland SwissSMEX sites were complemented with land evaporation measurements from mini-lysimeters.First, a quality assessment and inter-comparison of the in-situ soil moisture and land evaporation observations at 12 grassland sites revealed substantial discrepancies between different sensor types in terms of absolute values and data availability. A standard procedure for processing and interpreting the SwissSMEX data is thus being established. Second, analyses have been carried out comparing the SwissSMEX measurements with gridded remote-sensing and reanalysis products that provide near real time soil moisture data. In particular, the European Space Agency (ESA) Climate Change Initiative (CCI) surface soil moisture product (ESA-CCI soil moisture) as well as the new ECMWF reanalysis ERA5 are considered. The seasonal evolution of the soil moisture anomalies (with respect to the long-term mean) show for 2020 two pronounced phases of dryness. These are consistently represented in SwissSMEX in-situ observations and ERA5. Also the other recent drought events of 2015 and 2018 show a similar temporal evolution in both datasets. The response of ESA-CCI surface soil moisture is less pronounced, more variable and also dependent on the measurement methodology, i.e., active or passive microwave remote sensing.These first analyses provide useful insights in order to provide near-real time monitoring, enhance process understanding at the national scale and a better preparedness for future droughts.

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