SMOS retrieval over forests: Exploitation of optical depth and tests of soil moisture estimates

Vittucci, Cristina; Ferrazzoli, P.; Kerr, Yann H.; Richaume, Philippe; Guerriero, L.; Rahmoune, R.; Laurin, Gaia Vaglio

doi:10.1016/j.rse.2016.03.004

Cited by 71 publications

(51 citation statements)

References 46 publications

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“…This frequency is less affected by the vegetation cover [64]. Moreover, the soil moisture retrievals on forest have been improved with the new level 2 V620 algorithm [80], which was the version used in this study. These features of SMOS help to explain why the influence of vegetal cover/use on the coupling strength between the AWD and SWDIS in the benchmark sites was little (see Table 1).…”

Section: Discussionmentioning

confidence: 99%

Evaluation of the SMOS-Derived Soil Water Deficit Index as Agricultural Drought Index in Northeast of Brazil

Paredes‐Trejo

Barbosa

2017

Water

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Northeast Brazil (NEB) has recently experienced one of its worst droughts in the last decades, with large losses on rainfed agriculture. Soil moisture is the main variable to monitor agricultural drought. The remote sensing approach for drought monitoring has been enriched with the launch of the Soil Moisture and Ocean Salinity (SMOS) in November 2009 by European Space Agency (ESA). In this work, the Soil Water Deficit Index (SWDI) was calculated using the SMOS L2 soil moisture in the NEB. The SMOS-derived SWDI data (SWDIS) were evaluated against the atmospheric water deficit (AWD) calculated from in situ observations. Comparisons were made at seven-day and 0.25 • scales, over the time-span of June 2010 to December 2013. It was found that the SWDIS has a reasonably good overall performance in terms of the drought-weeks detection (skill = 0.986) and capture of the upper soil moisture temporal dynamic (r = 0.652), implying that the SWDIS could be used to track agricultural droughts. Furthermore, SWDIS shows poor performance at sites located in mountains regions affected by severe droughts (−0.10 ≤ r ≤ 0.10). It is also noted that the vegetal cover/use, climate regime, and soil texture have little influence on the AWD-SWDIS coupling.

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Section: Discussionmentioning

confidence: 99%

Evaluation of the SMOS-Derived Soil Water Deficit Index as Agricultural Drought Index in Northeast of Brazil

Paredes‐Trejo

Barbosa

2017

Water

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“…Although the L-band observations show better performance over more densely vegetated areas than AMSR-E, there is still a drop in skill towards denser vegetation, as seen in earlier studies on SMOS over forested regions (e.g., [69]). The results concerning the complementary nature of the different sensor are generally in line with previous studies by De Jeu et al [22], Al-Yaari et al [23] and Dorigo et al [24] but provides a better indication where L-band retrievals could add value in multi-sensor soil moisture data records containing both active and higher frequency passive soil moisture retrievals.…”

Section: R Value Techniquementioning

confidence: 57%

The Effect of Three Different Data Fusion Approaches on the Quality of Soil Moisture Retrievals from Multiple Passive Microwave Sensors

Schalie

Jeu²,

Rodríguez-Fernández³

et al. 2018

Remote Sensing

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Long-term climate records of soil moisture are of increased importance to climate researchers. In this study, we aim to evaluate the quality of three different fusion approaches that combine soil moisture retrieval from multiple satellite sensors. The arrival of L-band missions has led to an increased focus on the integration of L-band-based soil moisture retrievals in climate records, emphasizing the need to improve our understanding based on its added value within a multi-sensor framework. The three evaluated approaches were developed on 10-year passive microwave data (2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013) from two different satellite sensors, i.e., SMOS (2010SMOS ( -2013 and AMSR-E (2003AMSR-E ( -2011, and are based on a neural network (NN), regressions (REG), and the Land Parameter Retrieval Model (LPRM). The ability of the different approaches to best match AMSR-E and SMOS in their overlapping period was tested using an inter-comparison exercise between the SMOS and AMSR-E datasets, while the skill of the individual soil moisture products, based on anomalies, was evaluated using two verification techniques; first, a data assimilation technique that links precipitation information to the quality of soil moisture (expressed as the R value ), and secondly the triple collocation analysis (TCA). ASCAT soil moisture was included in the skill evaluation, representing the active microwave-based counterpart of soil moisture retrievals. Besides a semi-global analysis, explicit focus was placed on two regions that have strong land-atmosphere coupling, the Sahel (SA) and the central Great Plains (CGP) of North America. The NN approach gives the highest correlation coefficient between SMOS and AMSR-E, closely followed by LPRM and REG, while the absolute error is approximately the same for all three approaches. The R value and TCA show the strength of using different satellite sources and the impact of different merging approaches on the skill to correctly capture soil moisture anomalies. The highest performance is found for AMSR-E over sparse vegetation, for SMOS over moderate vegetation, and for ASCAT over dense vegetation cover. While the two SMOS datasets (L3 and LPRM) show a similar performance, the three AMSR-E datasets do not. The good performance for AMSR-E over spare vegetation is mainly perceived for AMSR-E LPRM, benefiting from the physically based model, while AMSR-E NN shows improved skill in densely vegetated areas, making optimal use of the SMOS L3 training dataset. AMSR-E REG has a reasonable performance over sparsely vegetated areas; however, it quickly loses skill with increasing vegetation density. The findings over the SA and CGP mainly reflect results that are found in earlier sections. This confirms that historical soil

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“…2018, 10, x FOR PEER REVIEW 3 of 20 [2,6,16,17,24,29]. These remotely sensed data have been used to obtain drought indices with monitoring goals [10,12], including SMOS soil moisture data [1,5,8,30].…”

Section: Study Areamentioning

confidence: 99%

“…Both sensors use the L-band to collect soil moisture. These missions have conducted campaigns to assess the remotely-obtained products compared with in situ data in different climates in order to study drought effects [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

“…Several processes have been developed by the scientific community to validate soil moisture remote sensing products from satellites, such as the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) [10,12,25], SMAP [26][27][28], and SMOS [2,6,16,17,24,29]. These remotely sensed data have been used to obtain drought indices with monitoring goals [10,12], including SMOS soil moisture data [1,5,8,30].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Use of SMOS L3 Soil Moisture Data: Validation and Drought Assessment for Pernambuco State, Northeast Brazil

et al. 2018

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Abstract:The goal of this study was to validate soil moisture data from Soil Moisture Ocean Salinity (SMOS) using two in situ databases for Pernambuco State, located in Northeast Brazil. The validation process involved two approaches, pixel-station comparison and areal average, for three regions in Pernambuco with different climatic characteristics. After validation, the SMOS data were used for drought assessment by calculating soil moisture anomalies for the available period of data. Four statistical criteria were used to verify the quality of the satellite data: Pearson correlation coefficient, Willmott index of agreement, BIAS, and root mean squared difference (RMSD). The average RMSD calculated from the daily time series in the pixel and the areal assessment were 0.071 m 3 ·m −3 and 0.04 m 3 ·m −3 , respectively. Those values are near to the expected 0.04 m 3 ·m −3 accuracy of the SMOS mission. The analysis of soil moisture anomalies enabled the assessment of the dry period between 2012 and 2017 and the identification of regions most impacted by the drought. The driest year for all regions was 2012, when the anomaly values achieved −50% in some regions. The use of SMOS data provided additional information that was used in conjunction with the precipitation data to assess drought periods. This may be particularly relevant for planning in agriculture and supporting decision makers and farmers.

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SMOS retrieval over forests: Exploitation of optical depth and tests of soil moisture estimates

Cited by 71 publications

References 46 publications

Evaluation of the SMOS-Derived Soil Water Deficit Index as Agricultural Drought Index in Northeast of Brazil

Evaluation of the SMOS-Derived Soil Water Deficit Index as Agricultural Drought Index in Northeast of Brazil

The Effect of Three Different Data Fusion Approaches on the Quality of Soil Moisture Retrievals from Multiple Passive Microwave Sensors

Use of SMOS L3 Soil Moisture Data: Validation and Drought Assessment for Pernambuco State, Northeast Brazil

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