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

Abstract. The increasing frequency of drought events has expanded the research interest in drought monitoring. In this regard, remote sensing is a useful tool to globally mapping the agricultural drought. While this type of drought is directly linked to the availability of root zone soil moisture (RZSM) for plants growth, current satellite soil moisture observations only characterize the water content of the surface soil layer (0–5 cm). In this study, two soil moisture-based agricultural drought indices were obtained at a weekly rate from June 2010 to December 2016, using RZSM estimations at 1 km from the Soil Moisture and Ocean Salinity (SMOS) satellite, instead of surface soil moisture (SSM). The RZSM was estimated by applying the Soil Water Index (SWI) model to the SMOS SSM. The Soil Moisture Agricultural Drought Index (SMADI) and the Soil Water Deficit Index (SWDI) were assessed over the Castilla y León region (Spain) at 1 km spatial resolution. They were compared with the Atmospheric Water Deficit (AWD) and the Crop Moisture Index (CMI), both computed at different weather stations distributed over the study area. The level of agreement was analyzed through statistical correlation. Results showed that the use of RZSM does not influence the characterization of drought, both for SMADI and SWDI.

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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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A regionally explicit, global SWI calibration based on ISMN observations

10.5194/egusphere-egu21-496 ◽

2021 ◽

Author(s):

Manolis G. Grillakis

Keyword(s):

Soil Moisture ◽

Soil Water ◽

Large Scale ◽

Root Zone ◽

Soil Depth ◽

European Space Agency ◽

Soil Layer ◽

Added Value ◽

Water Index ◽

Soil Moisture Data

Remote sensing has proven to be an irreplaceable tool for monitoring soil moisture. The European Space Agency (ESA), through the Climate Change Initiative (CCI), has provided one of the most substantial contributions in the soil water monitoring, with almost 4 decades of global satellite derived and homogenized soil moisture data for the uppermost soil layer. Yet, due to the inherent limitations of many of the remote sensors, only a limited soil depth can be monitored. To enable the assessment of the deeper soil layer moisture from surface remotely sensed products, the Soil Water Index (SWI) has been established as a convolutive transformation of the surface soil moisture estimation, under the assumption of uniform hydraulic conductivity and the absence of transpiration. The SWI uses a single calibration parameter, the T-value, to modify its response over time.Here the Soil Water Index (SWI) is calibrated using ESA CCI soil moisture against in situ observations from the International Soil Moisture Network and then use Artificial Neural Networks (ANNs) to find the best physical soil, climate, and vegetation descriptors at a global scale to regionalize the calibration of the T-value. The calibration is then used to assess a root zone related soil moisture for the period 2001 &#8211; 2018.The results are compared against the European Centre for Medium-Range Weather Forecasts, ERA5 Land reanalysis soil moisture dataset, showing a good agreement, mainly over mid-latitudes. The results indicate that there is added value to the results of the machine learning calibration, comparing to the uniform T-value. This work contributes to the exploitation of ESA CCI soil moisture data, while the produced data can support large scale soil moisture related studies.

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Assessment of the SMAP-Derived Soil Water Deficit Index (SWDI-SMAP) as an Agricultural Drought Index in China

Remote Sensing ◽

10.3390/rs10081302 ◽

2018 ◽

Vol 10 (8) ◽

pp. 1302 ◽

Cited By ~ 10

Author(s):

Jueying Bai ◽

Qian Cui ◽

Deqing Chen ◽

Haiwei Yu ◽

Xudong Mao ◽

...

Keyword(s):

Soil Moisture ◽

Soil Water ◽

Water Deficit ◽

Microwave Remote Sensing ◽

Agricultural Drought ◽

Drought Monitoring ◽

In Situ Observation ◽

Soil Water Deficit ◽

The South ◽

The North

China is frequently subjected to local and regional drought disasters, and thus, drought monitoring is vital. Drought assessments based on available surface soil moisture (SM) can account for soil water deficit directly. Microwave remote sensing techniques enable the estimation of global SM with a high temporal resolution. At present, the evaluation of Soil Moisture Active Passive (SMAP) SM products is inadequate, and L-band microwave data have not been applied to agricultural drought monitoring throughout China. In this study, first, we provide a pivotal evaluation of the SMAP L3 radiometer-derived SM product using in situ observation data throughout China, to assist in subsequent drought assessment, and then the SMAP-Derived Soil Water Deficit Index (SWDI-SMAP) is compared with the atmospheric water deficit (AWD) and vegetation health index (VHI). It is found that the SMAP can obtain SM with relatively high accuracy and the SWDI-SMAP has a good overall performance on drought monitoring. Relatively good performance of SWDI-SMAP is shown, except in some mountain regions; the SWDI-SMAP generally performs better in the north than in the south for less dry bias, although better performance of SMAP SM based on the R is shown in the south than in the north; differences between the SWDI-SMAP and VHI are mainly shown in areas without vegetation or those containing drought-resistant plants. In summary, the SWDI-SMAP shows great application potential in drought monitoring.

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Satellite Soil Moisture for Agricultural Drought Monitoring: Assessment of SMAP-Derived Soil Water Deficit Index in Xiang River Basin, China

Remote Sensing ◽

10.3390/rs11030362 ◽

2019 ◽

Vol 11 (3) ◽

pp. 362 ◽

Cited By ~ 10

Author(s):

Qian Zhu ◽

Yulin Luo ◽

Yue-Ping Xu ◽

Ye Tian ◽

Tiantian Yang

Keyword(s):

Soil Moisture ◽

Soil Water ◽

Water Deficit ◽

River Basin ◽

Probability Of Detection ◽

Agricultural Drought ◽

Drought Monitoring ◽

Soil Water Deficit ◽

R Value ◽

Xiang River

Agricultural drought can have long-lasting and harmful impacts on both the ecosystem and economy. Therefore, it is important to monitor and predict agricultural drought accurately. Soil moisture is the key variable to define the agricultural drought index. However, in situ soil moisture observations are inaccessible in many areas of the world. Remote sensing techniques enrich the surface soil moisture observations at different tempo-spatial resolutions. In this study, the Level 2 L-band radiometer soil moisture dataset was used to estimate the Soil Water Deficit Index (SWDI). The Soil Moisture Active Passive (SMAP) dataset was evaluated with the soil moisture dataset obtained from the China Land Soil Moisture Data Assimilation System (CLSMDAS). The SMAP-derived SWDI (SMAP_SWDI) was compared with the atmospheric water deficit (AWD) calculated with precipitation and evapotranspiration from meteorological stations. Drought monitoring and comparison were accomplished at a weekly scale for the growing season (April to November) from 2015 to 2017. The results were as follows: (1) in terms of Pearson correlation coefficients (R-value) between SMAP and CLSMDAS, around 70% performed well and only 10% performed poorly at the grid scale, and the R-value was 0.62 for the whole basin; (2) severe droughts mainly occurred from mid-June to the end of September from 2015 to 2017; (3) severe droughts were detected in the southern and northeastern Xiang River Basin in mid-May of 2015, and in the northern basin in early August of 2016 and end of November 2017; (4) the values of percentage of drought weeks gradually decreased from 2015 to 2017, and increased from the northeast to the southwest of the basin in 2015 and 2016; and (5) the average value of R and probability of detection between SMAP_SWDI and AWD were 0.6 and 0.79, respectively. These results show SMAP has acceptable accuracy and good performance for drought monitoring in the Xiang River Basin.

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Development and Evaluation of an Agricultural Drought Index by Harnessing Soil Moisture and Weather Data

Water ◽

10.3390/w11071375 ◽

2019 ◽

Vol 11 (7) ◽

pp. 1375 ◽

Cited By ~ 5

Author(s):

Ali Ajaz ◽

Saleh Taghvaeian ◽

Kul Khand ◽

Prasanna H. Gowda ◽

Jerry E. Moorhead

Keyword(s):

Soil Moisture ◽

Crop Production ◽

Drought Index ◽

Root Zone ◽

Correlation Coefficients ◽

Meteorological Drought ◽

Agricultural Drought ◽

Weather Data ◽

Drought Indices ◽

Growth Stages

A new agricultural drought index was developed for monitoring drought impacts on agriculture in Oklahoma. This new index, called the Soil Moisture Evapotranspiration Index (SMEI), estimates the departure of aggregated root zone moisture from reference evapotranspiration. The SMEI was estimated at five locations across Oklahoma representing different climates. The results showed good agreement with existing soil moisture-based (SM) and meteorological drought indices. In addition, the SMEI had improved performance compared to other indices in capturing the effects of temporal and spatial variations in drought. The relationship with crop production is a key characteristic of any agricultural drought index. The correlations between winter wheat production and studied drought indices estimated during the growing period were investigated. The correlation coefficients were largest for SMEI (r > 0.9) during the critical crop growth stages when compared to other drought indices, and r decreased by moving from semi-arid to more humid regions across Oklahoma. Overall, the results suggest that the SMEI can be used effectively for monitoring the effects of drought on agriculture in Oklahoma.

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Error Propagation from Remotely Sensed Surface Soil Moisture Into Soil Water Index Using an Exponential Filter

10.29007/kvhb ◽

2018 ◽

Author(s):

Domenico De Santis ◽

Daniela Biondi

Keyword(s):

Soil Moisture ◽

Soil Water ◽

Error Propagation ◽

Surface Soil ◽

Root Zone ◽

In Situ Measurements ◽

Surface Soil Moisture ◽

Water Index ◽

Study Sites

In this study an error propagation (EP) scheme was introduced in parallel to exponential filter computation for soil water index (SWI) estimation. A preliminarily assessment of the computed uncertainties was carried out comparing satellite-derived SWI and reference root-zone in situ measurements. The EP scheme has shown skills in detecting potentially less reliable SWI values in the study sites, as well as a better understanding of the exponential filter shortcomings. The proposed approach shows a potential for SWI evaluation, providing simultaneous estimates of time-variant uncertainty.

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Corrigendum to “Satellite soil moisture for agricultural drought monitoring: Assessment of the SMOS derived Soil Water Deficit Index” [Remote Sens. Environ. 177 (2016) 277–286]

Remote Sensing of Environment ◽

10.1016/j.rse.2016.07.001 ◽

2016 ◽

Vol 183 ◽

pp. 368 ◽

Cited By ~ 1

Author(s):

J. Martínez-Fernández ◽

A. González-Zamora ◽

N. Sánchez ◽

A. Gumuzzio ◽

C.M. Herrero-Jiménez

Keyword(s):

Soil Moisture ◽

Soil Water ◽

Water Deficit ◽

Agricultural Drought ◽

Drought Monitoring ◽

Soil Water Deficit

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Evaluating the Utility of Drought Indices as Soil Moisture Proxies for Drought Monitoring and Land–Atmosphere Interactions

Journal of Hydrometeorology ◽

10.1175/jhm-d-20-0022.1 ◽

2020 ◽

Vol 21 (9) ◽

pp. 2157-2175

Author(s):

Shanshui Yuan ◽

Steven M. Quiring ◽

Chen Zhao

Keyword(s):

Soil Moisture ◽

Great Plains ◽

Soil Layer ◽

Standardized Precipitation Index ◽

Drought Indices ◽

Moisture Index ◽

Drought Conditions ◽

Crop Moisture Index ◽

The Standardized Precipitation Index ◽

The Relationship

AbstractThere are a variety of metrics that are used to monitor drought conditions, including soil moisture and drought indices. This study examines the relationship between in situ soil moisture, NLDAS-2 soil moisture, and four drought indices: the standardized precipitation index, the standardized precipitation evapotranspiration index, the crop moisture index, and the Palmer Z index. We evaluate how well drought indices and the modeled soil moisture represent the intensity, variability, and persistence of the observed soil moisture in the southern Great Plains. We also apply the drought indices to evaluate land–atmosphere interactions and compare the results with soil moisture. The results show that the SPI, SPEI, and Z index have higher correlations with 0–10-cm soil moisture, while the CMI is more strongly correlated with 0–100-cm soil moisture. All the drought indices tend to overestimate the area affected by moderate to extreme drought conditions. Significant drying trends from 2003 to 2017 are evident in SPEI, Z index, and CMI, and they agree with those in the observed soil moisture. The CMI captures the intra- and interannual variability of 0–100-cm soil moisture better than the other drought indices. The persistence of CMI is longer than that of 0–10-cm soil moisture and shorter than that of 0–100-cm soil moisture. Model-derived soil moisture does not outperform the CMI in the 0–100-cm soil layer. The Z index and CMI are better drought indices to use as a proxy for soil moisture when examining land–atmosphere interactions while the SPI is not recommended. Soil type and climate affect the relationship between drought indices and observed soil moisture.

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Global joint assimilation of GRACE and SMOS for improved estimation of root-zone soil moisture and vegetation response

Hydrology and Earth System Sciences ◽

10.5194/hess-23-1067-2019 ◽

2019 ◽

Vol 23 (2) ◽

pp. 1067-1081 ◽

Cited By ~ 9

Author(s):

Siyuan Tian ◽

Luigi J. Renzullo ◽

Albert I. J. M. van Dijk ◽

Paul Tregoning ◽

Jeffrey P. Walker

Keyword(s):

Soil Moisture ◽

Soil Water ◽

Surface Soil ◽

Root Zone ◽

Water Storage ◽

Microwave Remote Sensing ◽

Open Loop ◽

Total Water ◽

Surface Soil Moisture ◽

Vegetation Greenness

Abstract. The lack of direct measurement of root-zone soil moisture poses a challenge to the large-scale prediction of ecosystem response to variation in soil water. Microwave remote sensing capability is limited to measuring moisture content in the uppermost few centimetres of soil. The GRACE (Gravity Recovery and Climate Experiment) mission detected the variability in storage within the total water column. However, root-zone soil moisture cannot be separated from GRACE-observed total water storage anomalies without ancillary information on surface water and groundwater changes. In this study, GRACE total water storage anomalies and SMOS near-surface soil moisture observations were jointly assimilated into a hydrological model globally to better estimate the impact of changes in root-zone soil moisture on vegetation vigour. Overall, the accuracy of root-zone soil moisture estimates through the joint assimilation of surface soil moisture and total water storage retrievals showed improved consistency with ground-based soil moisture measurements and satellite-observed greenness when compared to open-loop estimates (i.e. without assimilation). For example, the correlation between modelled and in situ measurements of root-zone moisture increased by 0.1 (from 0.48 to 0.58) and 0.12 (from 0.53 to 0.65) on average for grasslands and croplands, respectively. Improved correlations were found between vegetation greenness and soil water storage on both seasonal variability and anomalies over water-limited regions. Joint assimilation results show a more severe deficit in soil water anomalies in eastern Australia, southern India and eastern Brazil over the period of 2010 to 2016 than the open-loop, consistent with the satellite-observed vegetation greenness anomalies. The assimilation of satellite-observed water content contributes to more accurate knowledge of soil water availability, providing new insights for monitoring hidden water stress and vegetation conditions.

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Groundwater dynamics retrievals in Africa using SMOS soil moisture measurements

10.5194/egusphere-egu2020-21581 ◽

2020 ◽

Author(s):

Thierry Pellarin ◽

Laurent Oxarango ◽

Jean-Martial Cohard ◽

Alban Depeyre ◽

Basile Hector ◽

...

Keyword(s):

Soil Moisture ◽

Groundwater Recharge ◽

Soil Water ◽

Surface Soil ◽

Root Zone ◽

Surface Soil Moisture ◽

Hydrodynamic Parameters ◽

Grace Satellite ◽

Moisture Dynamics

ESA&#8217;s SMOS mission is celebrating 10 years of measurements in 2020 and is still producing soil moisture data of interest for many applications. One of the successes of this mission is its unexpected applications of soil moisture, such as thin ice sheets over the ocean, above ground biomass and carbon stocks, crop yields or rainfall estimation. We believe that knowledge of soil moisture time series contains information that are closely related to the functioning of the hydrosphere (infiltration, evaporation, groundwater recharge) and the biosphere (vegetation development, crop yield, carbon storage). These two compartments are traditionally studied using models forced by precipitation rates and atmospheric variables. However, beyond the difficulty of measuring the precipitation rate accurately from space, a non-negligible portion of rain does not infiltrate the soil either because it is intercepted by vegetation or because of the surface runoff.In this study, we assume that SMOS retrieved soil moisture dynamics (0-5 cm) can inform us on much deeper soil horizons. Given that the water that reaches the root zone (0-200cm) and groundwater necessarily transits at some point through the surface, we can hypothesize that surface soil moisture dynamics intrinsically contains information on water dynamics in deeper layers.To test this idea, we used Richards' 1D model and forced the first layer of the model with 5-cm in-situ soil moisture measurements from the AMMA-CATCH observatory sites in West-Africa. A variation of soil moisture at the surface generates moisture variations in the deeper layers according to the hydrodynamic parameters of the model: soil conductivity at saturation (Ks), shape parameters of the retention curve (&#945; and m), soil porosity (&#952;sat). For highly permeable soils, water rapidly infiltrates the soil column and creates a groundwater table with its seasonal dynamics. For more impermeable soils, water remains close to the surface and there is no groundwater recharge. This approach satisfyingly compares with in-situ measurements concerning both root zone soil moisture profiles and water table dynamics.In a second step, the proposed methodology was applied to measurements derived from the SMOS satellite over the whole of Africa. To substitute in situ measurements, the GRACE satellite gravity data is used to compare with simulated soil water variations. This comparison allows to reject a lot of hydrodynamic parameters, and to select the best combination of the 4 parameters. Finally, the method makes it possible to produce maps of water table depths and their temporal dynamics at the scale of the African continent from information on surface soil moisture from SMOS (0-5cm) and soil water content from GRACE satellite.

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