scholarly journals Evaluation of the AMSR2 L2 soil moisture product of JAXA on the Mongolian Plateau over seven years (2012–2018)

2019 ◽  
Vol 1 (11) ◽  
Author(s):  
Ichirow Kaihotsu ◽  
Jun Asanuma ◽  
Kentaro Aida ◽  
Dambaravjaa Oyunbaatar
Keyword(s):  
Soil Moisture ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Surface Soil ◽  
Surface Soil Moisture ◽  
Mongolian Plateau ◽  
Moisture Measurement ◽  
Rainfall Condition ◽  
Soil Moisture Measurement

Abstract This study evaluated the Advanced Microwave Scanning Radiometer 2 (AMSR2) L2 soil moisture product (ver. 3) using in situ hydrological observational data, acquired over 7 years (2012–2018), from a 50 × 50 km flat area of the Mongolian Plateau covered with bare soil, pasture and shrubs. Although AMSR2 slightly underestimated soil moisture content at 3-cm depth, satisfactory timing was observed in both the response patterns and the in situ soil moisture data, and the differences between these factors were not large. In terms of the relationship between AMSR2 soil moisture from descending orbits and in situ measured soil moisture at 3-cm depth, the values of the RMSE (m3/m3) and the bias (m3/m3) varied from 0.028 to 0.063 and from 0.011 to − 0.001 m3/m3, respectively. The values of the RMSE and bias depended on rainfall condition. The mean value of the RMSE for the 7-year period was 0.042 m3/m3, i.e., lower than the target accuracy 0.050 m3/m3. The validation results for descending orbits were found slightly better than for ascending orbits. Comparison of the Soil Moisture and Ocean Salinity (SMOS) soil moisture product with the AMSR2 L2 soil moisture product showed that AMSR2 could observe surface soil moisture with nearly same accuracy and stability. However, the bias of the AMSR2 soil moisture measurement was slightly negative and poorer than that of SMOS with deeper soil moisture measurement. It means that AMSR2 cannot effectively measure soil moisture at 3-cm depth. In situ soil temperature at 3-cm depth and surface vegetation (normalized difference vegetation index) did not influence the underestimation of AMSR2 soil moisture measurements. These results suggest that a possible cause of the underestimation of AMSR2 soil moisture measurements is the difference between the depth of the AMSR2 observations and in situ soil moisture measurements. Overall, this study proved the AMSR2 L2 soil moisture product has been useful for monitoring daily surface soil moisture over large grassland areas and it clearly demonstrated the high-performance capability of AMSR2 since 2012.

scholarly journals Penetration Analysis of SAR Signals in the C and L Bands for Wheat, Maize, and Grasslands

2018 ◽  
Vol 11 (1) ◽  
pp. 31 ◽  
Author(s):  
Mohammad El Hajj ◽  
Nicolas Baghdadi ◽  
Hassan Bazzi ◽  
Mehrez Zribi
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Surface Soil ◽  
Temporal Evolution ◽  
Canopy Cover ◽  
Radar Signal ◽  
Surface Soil Moisture ◽  
Sar Images ◽  
L Band

This paper assesses the potential of Synthetic Aperture Radar (SAR) in the C and L bands to penetrate into the canopy cover of wheat, maize and grasslands. For wheat and grasslands, the sensitivity of the C and L bands to in situ surface soil moisture (SSM) was first studied according to three levels of the Normalized Difference Vegetation Index (NDVI < 0.4, 0.4 < NDVI < 0.7, and NDVI > 0.7). Next, the temporal evolution of the SAR signal in the C and L bands was analyzed according to SSM and the NDVI. For wheat and grasslands, the results showed that the L-band in HH polarization penetrates the canopy even when the canopy is well-developed (NDVI > 0.7), whereas the penetration of the C-band into the canopy is limited for an NDVI < 0.7. For an NDVI less than 0.7, the sensitivity of the radar signal to SSM is approximately 0.27 dB/vol.% for the L-band in HH polarization and approximately 0.12 dB/vol.% for the C-band (in both VV and VH polarizations). For highly developed wheat and grassland cover (NDVI > 0.7), the sensitivity of the L-band in HH polarization to SSM is approximately 0.19 dB/vol.%, whereas as the C-band is insensitive to SSM. For maize, only the temporal evolution of the C-band according to SSM and the NDVI was studied because the swath of SAR images in the L-band did not cover the maize plots. The results showed that the C-band in VV polarization is able to penetrate the maize canopy even when the canopy is well developed (NDVI > 0.7) due to high-order scattering along the soil-vegetation pathway that contains a soil contribution. According to results obtained in this paper, the L-band would penetrate a well-developed maize cover since the penetration depth of the L-band is greater than that of the C-band.

Synergistic use of SMOS and Sentinel-3 for retrieving spatiotemporally estimates of surface soil moisture and evaporative fraction

2021 ◽  
Author(s):  
Maria Piles ◽  
Miriam Pablos Hernandez ◽  
Mercè Vall-llossera ◽  
Gerard Portal ◽  
Ionut Sandric ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Vegetation Index ◽  
Surface Soil ◽  
Added Value ◽  
The European Union ◽  
Surface Soil Moisture ◽  
Evaporative Fraction ◽  
In Situ Data

&lt;p&gt;Earth Observation (EO) makes it possible to obtain information on key parameters characterizing interactions among Earth&amp;#8217;s system components, such as evaporative fraction (EF) and surface soil moisture (SSM). Notably, techniques utilizing EO data of land surface temperature (Ts) and vegetation index (VI) have shown promise in this regard. The present study presents an implementation of a downscaling method that combined the soil moisture product from SMOS and the Fractional Vegetation Cover provided by Sentinel 3 ESA platform.&lt;/p&gt;&lt;p&gt;The applicability of the investigated technique is demonstrated for a period of two years (2017-2018) using in-situ data acquired from five CarboEurope sites and from all the sites available in the REMEDHUS soil moisture monitoring network, representing a variety of climatic, topographic and environmental conditions. Predicted parameters were compared against co-orbital ground measurements acquired from several European sites belonging to the CarboEurope ground observational network.&lt;/p&gt;&lt;p&gt;Results indicated a close agreement between all the inverted parameters and the corresponding in-situ data. SSM maps predicted from the &amp;#8220;triangle&amp;#8221;&amp;#160; SSM showed a small bias,&lt;sup&gt;&lt;/sup&gt;&amp;#160;but a large scatter. The results of this study provide strong supportive evidence of the potential value of the investigated herein methodology in accurately deriving estimates of key parameters characterising land surface interactions that can meet the needs of fine-scale hydrological applications. Moreover, the applicability of the presented approach demonstrates the added value of the synergy between ESA&amp;#8217;s operational products acquired from different satellite sensors, namely in this case SMOS &amp; Sentienl-3. As it is not tight to any particular sensor can also be implemented with technologically advanced EO sensors launched recently or planned to be launched.&lt;/p&gt;&lt;p&gt;In the present work Dr Petropoulos participation has received funding from the European Union&amp;#8217;s Horizon 2020 research and innovation programme ENViSIoN under the Marie Sk&amp;#322;odowska-Curie grant agreement No 752094.&lt;/p&gt;

scholarly journals Potential for the Detection of Irrigation Events on Maize Plots Using Sentinel-1 Soil Moisture Products

2020 ◽  
Vol 12 (10) ◽  
pp. 1621 ◽  
Author(s):  
Michel Le Page ◽  
Lionel Jarlan ◽  
Marcel M. El Hajj ◽  
Mehrez Zribi ◽  
Nicolas Baghdadi ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Vegetation Index ◽  
Surface Soil ◽  
Radar Data ◽  
Balance Model ◽  
Surface Soil Moisture ◽  
Upper Management ◽  
In Situ Data

Although the real timing and flow rates used for crop irrigation are controlled at the scale of individual plots by the irrigator, they are not generally known by the farm upper management. This information is nevertheless essential, not only to compute the water balance of irrigated plots and to schedule irrigation, but also for the management of water resources at regional scales. The aim of the present study was to detect irrigation timing using time series of surface soil moisture (SSM) derived from Sentinel-1 radar observations. The method consisted of assessing the direction of change of surface soil moisture (SSM) between observations and a water balance model, and to use thresholds to be calibrated. The performance of the approach was assessed on the F-score quantifying the accuracy of the irrigation event detections and ranging from 0 (none of the irrigation timing is correct) to 100 (perfect irrigation detection). The study focused on five irrigated and one rainfed plot of maize in South-West France, where the approach was tested using in situ measurements and surface soil moisture (SSM) maps derived from Sentinel-1 radar data. The use of in situ data showed that (1) irrigation timing was detected with a good accuracy (F-score in the range (80–83) for all plots) and (2) the optimal revisit time between two SSM observations was 2–4 days. The higher uncertainties of microwave SSM products, especially when the crop is well developed (normalized difference of vegetation index (NDVI) > 0.7), degraded the score (F-score = 69), but various possibilities of improvement were discussed. This paper opens perspectives for the irrigation detection at the plot scale over large areas and thus for the improvement of irrigation water management.

scholarly journals Calibration and Validation of the COSMOS Rover for Surface Soil Moisture Measurement

2014 ◽  
Vol 13 (4) ◽  
pp. vzj2013.08.0148 ◽  
Author(s):  
Jingnuo Dong ◽  
Tyson E. Ochsner ◽  
Marek Zreda ◽  
Michael H. Cosh ◽  
Chris B. Zou
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Surface Soil Moisture ◽  
Moisture Measurement ◽  
Calibration And Validation ◽  
Soil Moisture Measurement

scholarly journals 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

2008 ◽  
Vol 12 (6) ◽  
pp. 1323-1337 ◽  
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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