# Exploiting Satellite-Based Surface Soil Moisture for Flood Forecasting in the Mediterranean Area: State Update Versus Rainfall Correction

^{*}

## Abstract

**:**

## 1. Introduction

- To what extent updating soil moisture states leads to better flood predictions than the correction of the rainfall?
- How much these improvements are affected by the underlying accuracy of the original rainfall product used for forcing the hydrological model?
- What is the impact of the basin size and the climate conditions on the results?

## 2. Material

#### 2.1. Study Area

^{2}for the Kolpa river basin in Slovenia to about 5000 km

^{2}for the Tevere River basin in central Italy, mean basin elevation ranges from 197 m a.s.l. (lowland basin) to 1362 m a.s.l. (mountainous basin). Given the different climatic and physiographic conditions that characterize the selected catchments, they can be considered a representative sample of the catchments located in the Mediterranean (Figure 1).

#### 2.2. Datasets

#### 2.2.1. Soil Moisture Observations

_{pas}—is driven by its independence with respect to ASCAT observations and its full availability during the period of analysis. Both the ASCAT and CCI

_{pas}products are spatially resampled over the catchment boundaries to provide watershed-scale average of soil moisture.

#### 2.2.2. Rainfall and Temperature Data

#### 2.2.3. Stream Flow Data

## 3. Methods

#### 3.1. MISDc

_{1}and W

_{2}. Water is extracted from the first layer by evapotranspiration which is calculated by a linear function between the potential evaporation (estimated via the Blaney and Criddle relation modified by [45,46] and the soil saturation. A non-linear relation proposed by [47] calculates percolation from the surface to the root zone layer. The rainfall excess is calculated by a power law relationship as a function of the first layer soil saturation while base flow is a non-linear function of the soil moisture content of the third layer [48].

#### 3.2. Soil Moisture Data Assimilation

#### 3.2.1. Pre-Processing of Soil Moisture Observations and Error Estimation

_{ASCAT}) and the model state of the first model layer (W

_{1}). Before the assimilation, the satellite soil moisture observations were bias corrected to the model climatology by the quantile mapping approach [52]. A 2nd order polynomial function was used for mapping SWI data to the model, thus obtaining SWI*

_{ASCAT}. The same processing steps applied to ASCAT were applied to the CCI

_{pas}to obtain SWI*

_{CCIpas}. This was done for obtaining the same climatology and dynamic range of W

_{1}and reduce the impact caused by the different vertical representativeness when used within the Triple Collocation (TC, [53]) analysis (see below).

_{ASCAT}, SWI*

_{CCIpas}and W

_{1}to calculate the SWI*

_{ASCAT}error variance ($\sigma {\ast}_{ASCAT}$). In practice, the three datasets are decomposed into the corresponding climatology anomalies time series by subtracting the long-term 31-day moving average from the raw time series. This guarantees the estimation of only random error sources and a more accurate observation error variance estimate [55]. The application of TC is only performed for the calculation of the scaled error variance of SWI*

_{ASCAT}which is then used within the SM-corr approach.

#### 3.2.2. The Ensemble Kalman Filter

**Y**(t

_{k}) the vector of system states at time step t

_{k},

**Y**(t) = [W

_{1}(t

_{k}), W

_{2}(t

_{k})]

^{T}obtained via a generic model and

**Z**

_{k}the observation vector at time t

_{k}, then, the optimal updating of

**Y**

_{k}, can be expressed as:

**H**

_{k}is the observation operator that maps the model states to the observations, v

_{k}is a synthetically generated error added to the observation

**Z**

_{k}and represents the uncertainties of the observation process that is assumed to be a mean-zero Gaussian random variable with variance

**R**

_{k}.

**G**

_{k}is the Kalman gain:

**H**= [1 0]

^{T}while the observation error covariance matrix,

**R**, reduces to ${\sigma}_{ASCAT}^{\ast 2}$ since only pre-processed soil moisture derived from ASCAT is assimilated. The single deterministic EnKF prediction (i.e., the “analysis”) is calculated by averaging model state predictions, ${\mathit{Y}}_{k}^{i-}$, and the consequent stream flow at each time step across the N members of the ensemble. In Equation (3), $\langle {\mathit{Y}}_{k}^{i-}\rangle $ denotes the mean of ${\mathit{Y}}_{k}^{i-}$. The covariance matrix of the forecast error was obtained by perturbing rainfall and temperature data along with the model soil moisture predictions (see next section).

_{k}#### 3.2.3. Filter Calibration

^{−3}. ${\sigma}_{P}$ was made variable between 10

^{−5}and 2 by assuming that the main error of the model is associated to the uncertainty in the precipitation forcing. The optimal value ${\sigma}_{P}$ was selected by picking the one that, by running the filter during the calibration period, minimizes the root mean square error (RMSE) between simulated (ensemble-averaged) and observed discharge time series and ensures that innovations (the second term in square brackets in Equation (1)) have zero mean and are serially uncorrelated [57]. Note that, given the model non-linearity, the satisfaction of the latter criterion was very difficult to obtain and thus was not always guaranteed. In addition, the model error calibration based on the minimization of the RMSE as done here, assumes that the error in stream flow observations is negligible. However, if significant errors are present in observed stream flow, this procedure may be sub-optimal and the filter inflated by these errors. Alternative procedures that guarantee a more optimal filter performance are also possible [56] but are beyond to scope of the paper and are not treated here. The ensemble size was set to 50 members, more numerous ensembles were also tested but did not provide significant changes therefore N = 50 was finally set to speed up the calculations.

#### 3.3. Rainfall Correction

#### 3.3.1. Pre-Processing of Rainfall Observations

_{SM2RAIN-ASC}for SM2RAIN-ASCAT, P

_{ERA}for ERA-Interim and P

_{3B42RT}for 3B42RT from here onward. To maintain a consistent notation, the original EOBS rainfall product will be also denoted as P

_{EOBS}in the following. As for the pre-processing of soil moisture the bias correction calibration parameters were determined during the calibration period (see Section 3.5) and then used in validation.

#### 3.3.2. Rainfall Integration

_{SM2RAIN-ASC}and the specific rainfall product (i.e., P

_{3B42RT}or P

_{ERA}) was carried out by a simple Newtonian nudging scheme [4]:

_{COR}is the corrected rainfall product (P

_{ERA+ SM2RAIN-ASC}or P

_{3B42RT+SM2RAIN-ASC}), P* is P

_{3B42RT}or P

_{ERA}. K is a static weighting parameter estimated during the calibration period by minimizing the RMSE between simulated stream flow time series and observations. K gives the relative weight of P

_{SM2RAIN-ASC}with respect to the satellite (reanalysis) rainfall product. K equal to 1 means that the error in satellite (reanalysis) rainfall is much lower than P

_{SM2RAIN-ASC}and no correction is performed while K equal to 0 means that P

_{SM2RAIN-ASC}error is much lower than satellite (reanalysis) rainfall, thus only P

_{SM2RAIN-ASC}is used. To maintain a similar methodology approach with the one used in Section 3.2.3, K was calibrated by minimizing the RMSE between simulated and observed discharge time series during the calibration period. The calibrated K for each basin were then used in validation. Being calibrated based on the RMSE between simulated and observed stream flow time series, the determination of K is subjected to the same limitations described in Section 3.2.3 (i.e., it can be inflated by errors contained in observed stream flow).

#### 3.4. Performance Metrics

_{t}of the observed, ${Q}_{obs}$, and simulated ${Q}_{sim}$, discharges vectors. The term $\epsilon $ in Equation (6), was arbitrarily chosen as a small fraction of the inter-annual mean discharge (e.g., $\langle {Q}_{obs}\rangle /40$) and was introduced to avoid problems with nil observed or simulated discharges.

_{sim}/μ

_{obs}), and variability (δ = CV

_{sim}/CV

_{obs}), between ${Q}_{sim}$ and ${Q}_{obs}$. KGE is defined as follows:

_{logQ}and KGE vary between −∞ and 1 with values equal to one denoting perfect agreement between stream flow observations and simulations.

#### 3.5. Method Implementation

_{EOBS}rainfall and temperature (see Figure 2a) through a standard gradient-based automatic optimization algorithm [62]. The calibrated parameters were then used within all the simulations involving P

_{3B42RT}and P

_{ERA}. As denoted in Section 3.2.1, Section 3.3.1 and Section 3.2.3, during calibration, we determined the (1) optimal rainfall bias correction parameters, (2) the parameter K related to the rainfall integration, (3) the data assimilation parameters (i.e., the characteristic time length T and the parameters associated to the bias correction of soil moisture), (4) the satellite soil moisture observation error (σ*

_{SWI}) and (5) the forecast model error. The calibrated integration/assimilation parameters were then used during the validation periods for obtaining discharge simulations for SM-corr and P-corr for a total of six different runs (Figure 2b). That is, the two off-line simulations obtained by forcing the model with P

_{ERA}(OLM) and P

_{3B42RT}(OLS), the two data assimilation experiments where ASCAT soil moisture observations were assimilated into MISDc forced with P

_{ERA}(DAM) and P

_{3B42RT}(DAS) and the two integration experiments where corrected rainfall P

_{SM2RAIN-ASC + ERA}(RCM) and P

_{SM2RAIN-ASC + 3B42RT}(RCS) were used to force MISDc. As a baseline for comparing the performance of satellite and reanalysis rainfall products, also the runs in which MISDc was forced with P

_{EOBS}were considered (OLG in Figure 2b).

## 4. Results and Discussion

#### 4.1. MISDc Model Calibration and Validation Forced with Ground-Based Data

_{EOBS}) provide a median KGE efficiency index equal to 0.692 (i.e., always above 0.6 except for Volturno, see Table 1). In this respect, MISDc performance can be considered relatively good and between the intermediate (0.75 > KGE > 0.5) and good (KGE > 0.75) level as identified in [63]. This ensures the reliability of the model for stream flow simulations. Based on Table 1 (and Table 3) it can be observed that cold and more humid catchments generally perform better than warm and drier ones.

#### 4.2. Satellite Soil Moisture Pre-Processing and Filter Calibration

_{1}and the SWI*

_{ASCAT}for all the analysed catchments for P

_{ERA}and P

_{3B42RT}. T is lower than 20 days for most of the catchments except Arga and Volturno where it reaches a value of about 60 days. These results are consistent with the range of values found in previous studies (e.g., [13,16,30,64]). There is not a specific pattern that is possible to identify for the study catchments because T variations are not only related to the specific catchment hydrology but also to the model and the satellite observation quality

_{ASCAT}obtained by considering the triplets among SWI*

_{ASCAT}, SWI

_{CCIpas}and the soil moisture simulated by MISDc model forced with P

_{ERA}(P

_{3B42RT}). The error variances found with the two triplets maintain a similar comparative relationship among basins showing smaller values for drier and warm catchments (Tevere, Arga, Mdouar) and larger values for more cold and humid (mountainous) catchments (Kolpa@Petrina, Gardon, Lim). The relatively better performance of ASCAT in semi-arid environments is consistent with the results of [64,65].

#### 4.3. Rainfall Correction Calibration

_{ERA}and P

_{3B42RT}with P

_{SM2RAIN-ASC}through Equation (4). It can be seen that K is significantly higher for RCM (mean K = 0.77) with respect to RCS (mean K = 0.42) suggesting a higher quality of P

_{ERA}with respect to P

_{3B42RT}. It can be also seen that lower K values (i.e., which means that P

_{SM2RAIN-ASC}is weighed more with respect to the counterpart product in Equation (4) provide a larger decrease in RMSE and this reduction is generally larger for RCS with respect to RCM.

#### 4.4. Rainfall Evaluation

_{EOBS}can be considered a good reference for evaluating the performance of P

_{3B42RT}and P

_{ERA}(and their associated integrated products) over the study catchments. Figure 5 shows the scatter plots of the correlations between P

_{3B42RT}, P

_{ERA}, P

_{ERA+SM2RAIN-ASC}

_{,}P

_{3B42RT+SM2RAIN-ASC}and P

_{EOBS}for each catchment in Table 1 during the validation period.

_{ERA}and P

_{3B42RT}. It can be seen that P

_{ERA}performs relatively better than P

_{3B42RT}for almost all catchments thus confirming the previous results in terms of K obtained in calibration. Similar results were also found by [44] who observed a higher quality of ERA-Interim in Europe with respect to 3B42RT. The only exception is the Mdouar catchment. Here the reanalysis product performs relatively worse than the satellite-based one. A possible reason of the lower performance is related to the type of precipitation that characterizes this area (stratiform vs. convective precipitation) as also found in [66,67,68].

_{SM2RAIN-ASC}and P

_{ERA}(P

_{3B42RT})—which is based on minimization of the RMSE between observed and simulated stream flow during the calibration period—indirectly leads to increased rainfall quality both during the calibration (not shown) and the validation periods (Figure 5b,c). In Figure 5b,c, the Lim catchment (#8), is the only catchment where the integration provides a significant deterioration of the correlation. A slight deterioration is also observed for Kolpa@Petrina (#1) and Kolpa@Metilka (#6) when integrating P

_{3B42RT}and P

_{SM2RAIN-ASC}. However, these deteriorations (including the one of Lim catchments) are significantly lower when the rainfall products are compared in terms of RMSE (not shown). Possible reasons of the deteriorations are related to ASCAT error that, in this catchment is relatively higher (see Figure 3) and to the SM2RAIN limitations when the soil is close to saturation (see below).

#### 4.5. Stream Flow Evaluation

_{EOBS}during the validation period (i.e., OLG run).

_{ERA}precipitation in this basin (as seen in Section 4.4). The topographic complexity of the study area along with the strong non-stationary performance of satellite-based rainfall products over time caused by the season and by the variable number of satellite microwave passes used for the retrieval of precipitation [42,69,70] are the main causes of the low scores obtained in the stream flow simulations with P

_{3B42RT}[71]. In practice, the satellite precipitation error has both (1) a direct effect on stream flow estimates by determining under(over) estimations due to the erroneous instantaneous precipitation and (2) an indirect effect on the state estimation that propagates in time for several days/months causing additional stream flow errors. The low scores of the stream flow estimates derived from satellite-based rainfall observations are in line with those found in many other studies in literature [30,37,71,72]. In the latter, it was who found that reanalysis-based rainfall products generally outperform satellite-based ones in hydrological modelling.

_{EOBS}) and those obtained with RCM. Here, KGE is equal 0.494 for OLS and 0.481 for OLG with Volturno and Tevere being the best among OLG, OLS, DAS, DAM. In a recent study by [72] it was found that gauge-based and reanalysis products generally outperform satellite based products for flood simulations [72]. We found that the correction of 3B42RT with SM2RAIN rainfall estimates performs better than simulations using gauge-based observations (i.e., EOBS). This encouraging result demonstrates the potentiality to improve operational stream flow forecasting by using remotely sensed surface soil moisture.

_{lnQ}indexes in Figure 6. For ANSE, both the SM-corr and P-corr schemes improve the model performance obtained in the off-line simulations with a clear advantage of the P-corr scheme. In median, the enhancements obtained for RCM and RCS are larger with respect to DAM and DAS and allow to obtain ANSE values close to the ones obtained in OLG (note that as for the KGE results presented in Table 3 some catchments—not shown—have ANSE values larger than the ones obtained in OLG). For low flow conditions, the performance of the model is generally lower and cannot be observed a clear advantage of one technique with respect to the other. However, in median, DAM and DAS provide a slightly better performance (note that for DAM, these scores are above those obtained in OLG). These results are consistent with those found by [11] where the rainfall correction scheme improved better high flows with respect to state correction. They are less consistent with the results of [30] who showed a higher positive impact on the stream flow prediction of the state correction with respect to the forcing correction scheme both for high and low flows (for the latter the improvements were higher though). The smaller increments obtained in the DAM and DAS cases are in line with those found in [28] and lower with respect to the ones in [11] where the state correction scheme implemented via EnKF benefited from the correction of the ensemble perturbation bias.

_{SM2RAIN-ASC}rainfall estimates). This assumption is supported by the detrimental effect of the integration on the precipitation for this catchment as plotted in Figure 5c.

## 5. Conclusions

- The gauge-based rainfall dataset (EOBS) performs satisfactorily well over the Mediterranean area with median ANSE and KGE values close to 0.5 (in validation) for the investigated catchments while P
_{ERA}and P_{3B42RT}provide poorer stream flow predictions. - The soil moisture correction produces an overall slight improvement in terms of median KGE and ANSE scores (4.25% and 1.5% for ERA-Interim and 9.6% and 7.6% for 3B42RT, respectively) whereas the rainfall correction provides a much larger impact with an increase in KGE and ANSE values equal to 14.81 and 7.3% for ERA and 71.8 and 100% for 3B42RT, respectively. In summary, the impact of the rainfall correction for flood simulation is much larger than the soil moisture correction and is consistently higher when the quality of the non-corrected rainfall forcing is poor. Conversely, for low flows, the soil moisture correction schemes provide slight better results but these improvements are limited.
- After the rainfall correction, the simulation run using the satellite-based product (i.e., 3B42RT) shows KGE scores larger than those obtained by using ground-based observations (EOBS). This is an encouraging result that demonstrates the potentiality to improve operational stream flow forecasting by using remotely sensed surface soil moisture.
- The climate, the specific catchment hydrology/model configuration/data assimilation set up and the pre-processing steps associated with the two schemes exert a remarkable effect on the results that complicates the answer to weather is preferable correcting rainfall or updating the model states.

## Acknowledgments

## Author Contributions

## Conflicts of Interest

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**Figure 2.**(

**a**) Flowchart illustrating the main implementation steps of the soil moisture data assimilation (SM-corr, left) and the rainfall correction (P-corr, right) methods. (

**b**) Simulation runs used in the study.

**Figure 3.**Characteristic time length T (

**a**), SWI*

_{ASCAT}error standard deviation (

**b**), mean Kalman Gain G (

**c**), and % improvement in RMSE between simulated and observed stream flow (

**d**) obtained during the calibration period within the SM-corr approach by using P

_{3B42RT}(DAS) and P

_{ERA}(DAM) for forcing MISDc model.

**Figure 4.**Values of the calibrated gain parameter K for P

_{ERA}and the P

_{3B42RT}obtained during the calibration period (

**a**) and % reduction in RMSE between observed and simulated stream flow during the calibration period (

**b**). K close to zero indicates that more weight is assigned to P

_{SM2RAIN-ASCAT}dataset according to Equation (4).

**Figure 5.**(

**a**) comparison of correlations R between P

_{ERA}and P

_{3B42RT}obtained with P

_{EOBS}for all study catchments during the validation period; (

**b**) same as panel (

**a**) but between P

_{ERA}and P

_{ERA+SM2RAIN-ASC}

_{;}(

**c**) same as panel (

**a**) but between P

_{3B42RT}and P

_{3B42RT+SM2RAIN-ASC}. The points where the lines cross refer to the medians while the line edges represent the 25th and the 75th percentiles.

**Figure 6.**Summary of the results in terms of ANSE (Nash–Sutcliffe efficiency for high-flow conditions) and NS

_{lnQ}(NS adapted for low flow conditions) for all the investigated basins during the validation period. Red box plots refer to the results obtained by forcing MISDc with P

_{EOBS}datasets; the number in the square boxes represent the median values. Results are shown for the off-line simulations (OLM, OLS), for the SM-corr scheme (DAM, DAS) and for the P-corr scheme (RCM, RCS).

**Figure 7.**Stream flow simulations in the calibration and validation period for Kolpa-Pet (panels

**a**,

**b**) and Tevere (panel

**c**,

**d**) catchments. For each catchment, the results for OLM, OLS, DAM, DAS, RCM and RCS are shown. In each panel, the upper plot shows the comparison in terms of stream flow while the bottom one shows the smoothed time series of the RMSE (by using a moving windows of 60 days for sake of visualization) between observed (Q

_{obs}) and the three simulated stream flow. The white background refers to the calibration period and the grey background to the validation.

**Table 1.**Main characteristics of the investigated catchments. Cfb: temperate warm summer, Dfb: Cold Warm summer; Csa: Temperate dry and hot summer; Csb: Temperate dry and warm summer according to the Köppen classication.

ID# | Basin | Station | Country | Area (km^{2}) | Mean Elev. (m) | Annual Rainfall (mm) | Daily Temp (°C) | Climate Type | Calibration Period | Validation Period |
---|---|---|---|---|---|---|---|---|---|---|

1 | Kolpa | Petrina | Slovenia | 460 | 629 | 1304 | 8 | Cfb | 2007–2009 | 2010–2012 |

2 | Arga | Arazuri | Spain | 810 | 559 | 609 | 13 | Cfb | 2007–2011 | 2012–2014 |

3 | Brenta | Berzizza | Italy | 1506 | 1362 | 701 | 10 | Dfb | 2010–2011 | 2012–2013 |

4 | Gardon | Russan | France | 1530 | 514 | 679 | 13 | Csb | 2008–2011 | 2012–2013 |

5 | Mdouar | Elmakhazine | Morocco | 1800 | 304 | 561 | 18 | Csa | 2007–2009 | 2010–2011 |

6 | Kolpa | Metlika | Slovenia | 2002 | 197 | 920 | 11 | Cfb | 2007–2010 | 2011–2012 |

7 | Volturno | Solopaca | Italy | 2580 | 611 | 455 | 15 | Csa | 2010–2011 | 2012–2013 |

8 | Lim | Prijepolje | Serbia | 3160 | 612 | 668 | 9 | Cfb | 2007–2008 | 2009–2010 |

9 | Tanaro | Asti | Italy | 3230 | 927 | 630 | 11 | Cfb | 2010–2011 | 2012–2013 |

10 | Tevere | M. Molino | Italy | 4820 | 435 | 710 | 14 | Csa | 2007–2011 | 2012–2015 |

Parameter | Description | Range of Variability | Unit |
---|---|---|---|

Wmax_{1} | Maximum water capacity of the first layer | 150 | mm |

Wmax_{2} | Maximum water capacity of the second layer | 300–4000 | mm |

m_{1} | Exponent of drainage for 1st layer | 2–10 | - |

m_{2} | Exponent of drainage for 2nd layer | 5–20 | - |

Ks_{1} | Hydraulic conductivity of the 1st layer | 0.1–20 | mm/day |

Ks_{2} | Hydraulic conductivity of the 2nd layer | 0.01–45 | mm/day |

γ | Coefficient lag-time relationship | 0.5–3.5 | - |

Kc | Parameter of potential evapotranspiration | 0.4–2 | - |

α | Exponent of the infiltration relationship | 1–15 | - |

C_{m} | Snow module parameter degree-day | 0.004–3 | °C/day |

**Table 3.**Kling-Gupta performance index obtained during calibration by forcing the model with P

_{EOBS}(CALIBRATION in dark grey) and during the validation period (VALIDATION in light grey and blue) for (1) MISDc model forced with P

_{EOBS}(OLG in white), (2) MISDc model forced with P

_{ERA}and P

_{3B42RT}(OLM, OLS), (3) the state correction scheme (DAM, DAS) and (4) the rainfall correction scheme (RCM, RCS). Numbers in bold refer to the best score obtained in validation among OLG, OLM, DAM, RCM, OLS, DAS and RCS.

BASIN | CALIBRATION | VALIDATION | ||||||
---|---|---|---|---|---|---|---|---|

OLG | OLM | DAM | RCM | OLS | DAS | RCS | ||

Kolpa@Petrina | 0.817 | 0.637 | 0.510 | 0.510 | 0.508 | 0.426 | 0.425 | 0.389 |

Arga | 0.770 | 0.536 | 0.419 | 0.373 | 0.438 | 0.135 | 0.143 | 0.530 |

Brenta | 0.701 | 0.414 | 0.379 | 0.398 | 0.366 | 0.328 | 0.313 | 0.321 |

Gardon | 0.665 | 0.736 | 0.716 | 0.716 | 0.689 | 0.537 | 0.536 | 0.480 |

Mdouar | 0.683 | 0.562 | −1.085 | −1.320 | −0.376 | 0.234 | 0.379 | 0.136 |

Kolpa@Metilka | 0.709 | 0.796 | 0.656 | 0.655 | 0.624 | 0.588 | 0.363 | 0.510 |

Volturno | 0.416 | 0.426 | 0.193 | 0.187 | 0.228 | 0.090 | 0.093 | 0.508 |

Lim | 0.680 | 0.420 | 0.526 | 0.524 | 0.714 | 0.279 | 0.165 | 0.617 |

Tanaro | 0.713 | 0.262 | 0.152 | 0.197 | 0.152 | 0.121 | 0.097 | 0.121 |

Tevere | 0.603 | 0.417 | 0.327 | 0.434 | 0.474 | 0.299 | 0.320 | 0.701 |

Median | 0.692 | 0.481 | 0.399 | 0.416 | 0.456 | 0.289 | 0.317 | 0.494 |

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**MDPI and ACS Style**

Massari, C.; Camici, S.; Ciabatta, L.; Brocca, L.
Exploiting Satellite-Based Surface Soil Moisture for Flood Forecasting in the Mediterranean Area: State Update Versus Rainfall Correction. *Remote Sens.* **2018**, *10*, 292.
https://doi.org/10.3390/rs10020292

**AMA Style**

Massari C, Camici S, Ciabatta L, Brocca L.
Exploiting Satellite-Based Surface Soil Moisture for Flood Forecasting in the Mediterranean Area: State Update Versus Rainfall Correction. *Remote Sensing*. 2018; 10(2):292.
https://doi.org/10.3390/rs10020292

**Chicago/Turabian Style**

Massari, Christian, Stefania Camici, Luca Ciabatta, and Luca Brocca.
2018. "Exploiting Satellite-Based Surface Soil Moisture for Flood Forecasting in the Mediterranean Area: State Update Versus Rainfall Correction" *Remote Sensing* 10, no. 2: 292.
https://doi.org/10.3390/rs10020292