Root-zone plant available water estimation using the SMOS-derived soil water index

Abstract

Currently, there are several space missions capable of measuring surface soil moisture, owing to the relevance of this variable in meteorology, hydrology and agriculture. However, the Plant Available Water (PAW), which in some fields of application could be more important than the soil moisture itself, cannot be directly measured by remote sensing. Considering the root zone as the first 50 cm of the soil, in this study, the PAW at 25 cm and 50 cm and integrated between 0 and 50 cm of soil depth was estimated using the surface soil moisture provided by the Soil Moisture Ocean Salinity (SMOS) mission. For this purpose, the Soil Water Index (SWI) has been used as a proxy of the root-zone soil moisture, involving the selection of an optimal T (T opt ), which can be interpreted as a characteristic soil water travel time. In this research, several tests using the correlation coefficient (R), the Nash-Sutcliffe score (NS), several error estimators and bias as predictor metrics were applied to obtain the T opt , making a comprehensive study of the T parameter. After analyzing the results, some differences were found between the T opt obtained using R and NS as decision metrics, and that obtained using the errors and bias, but the SWI showed good results as an estimator of the root-zone soil moisture. This index showed good agreement, with an R between 0.60 and 0.88. The method was tested from January 2010 to December 2014, using the database of the Soil Moisture Measurements Stations Network of the University of Salamanca (REMEDHUS) in Spain. The PAW estimation showed good agreement with the in situ measurements, following closely the dry-downs and wetting-up events, with R ranging between 0.60 and 0.92, and error values lower than 0.05 m 3 m -3 . A slight underestimation was observed for both the PAW and root-zone soil moisture at the different depths; this could be explained by the underestimation pattern observed with the SMOS L2 soil moisture product, in line with previous studies. Nevertheless, the results obtained in this research showed an encouraging improvement of the PAW estimation. Despite the need for more research on this issue, the results of this study show that this methodology can be useful for applications of great interest in agriculture and hydrology.

Evaluation of Soil Moisture Variability in Poland from SMOS Satellite Observations

“…They are based on the synergy of passive microwaves with ancillary optical visible/infrared (VIS/IR) [43,44] or active data [45]. Another approach allows assessment of plant available water at the 0-50 cm depth from the SMOS surface soil moisture based on the Soil Water Index (SWI) as a proxy of the root-zone soil moisture [22]. The SWI is also used as a proxy of drought [23].…”

“…SMOS data supplements the CWB's information on soil moisture, which allows the indication of soil moisture conditions suitable for plant growth, and threats of droughts and floods. Comparisons of SMOS data against in-situ SM measurements showed good values of correlation (R > 0.7) [25,26] and relatively low RMSE (i.e., 0.04 m 3 m −3 ) [7,22,26].…”

“…To overcome this limitation, the surface SM products provided by remote sensing are gradually refined to increase their usefulness and applicability. An example can be assessment (integration) of plant available water at 0-50 cm depth from the SMOS surface soil moisture using the Soil Water Index (SWI) as a proxy of the root-zone soil moisture [22]. Moreover, SWI was found to be a suitable and cheap supplementary tool for drought monitoring using the SMOS soil moisture data [23].…”

Temporal and Spatial Comparison of Agricultural Drought Indices from Moderate Resolution Satellite Soil Moisture Data over Northwest Spain

“…As previously stated, the SMOS is only capable of retrieving the SM content of the soil top layer, which surely underestimates the root-zone SM. In the near future, the availability of root-zone SM estimates [92,93] is expected to improve the current estimation based on surface SM data for agricultural drought monitoring. Nevertheless, recent research has shown the capability of using satellite surface SM estimations for drought monitoring, e.g., using the surface SM observations from the Advanced Microwave Scanning Radiometer for Earth (AMSR-E) observation system and SMAP, after applying a cumulative distribution function (CDF) matching in situ surface SM data to remove the systematic bias and dynamic range differences [79,94].…”

Assessment of SMADI and SWDI agricultural drought indices using remotely sensed root zone soil moisture

“…This region is mainly characterized by a large plain surrounded by mountains to the North, South and East. The extension of the plain is approximately 65 000 km 2 and has a mean altitude of 800 m. Its climate is continental semi-arid Mediterranean with a mean annual temperature of 11.8 • C. The Castilla y León region suffers of scarcity of water, with an average annual precipitation of 450 mm (González-Zamora et al, 2015). Despite this, it is one of the largest agricultural areas of the European Union, chiefly dedicated to rainfed agriculture.…”