Soil moisture estimation through ASCAT and AMSR-E sensors: An intercomparison and validation study across Europe

Brocca, Luca; Hasenauer, S.; Lacava, Teodosio; Melone, F.; Moramarco, Tommaso; Wagner, Wolfgang; Dorigo, Wouter; Matgen, Patrick; Martínez-Fernández, José; Llorens, Pilar; Latron, Jérôme; Martin, C. Wayne; Bittelli, Marco

doi:10.1016/j.rse.2011.08.003

Cited by 509 publications

(389 citation statements)

References 72 publications

Supporting

Mentioning

355

Contrasting

Unclassified

Order By: Relevance

“…In RUSLE2, runoff prediction for storm events is obtained using the SCS-CN method with empirical equations that vary the values of CN in association with both soil moisture and rainfall intensity (Kinnell, 2014). Todisco et al (2012b) evaluated the efficiency of the MISDc model (Modello idrologico semidistribuito in continuo, Brocca et al, 2011a), coupled with an USLE-derived model, for the estimation of surface runoff and soil loss at the event timescale at Masse experimental station. The model performance is found to be promising, but it was underlined that the antecedent soil moisture proved to be a good alternative with respect to runoff for correcting the rainfall-runoff erosivity factor in the USLE-MM model.…”

Section: F Todisco Et Almentioning

confidence: 99%

“…The reader is referred to Wagner et al (1999) for more details on the SWI approach. Lastly, the data were converted in volumetric units (m 3 m −3 ) through a linear rescaling approach (Brocca et al, 2011b) for matching the range of variability of satellite and modeled soil moisture data provided by the SWBM. The AS-CAT data for the pixel closest to the Masse study area were used.…”

Section: Soil Moisture From Satellite Datamentioning

confidence: 99%

See 1 more Smart Citation

Use of satellite and modeled soil moisture data for predicting event soil loss at plot scale

Todisco

Brocca

Termite

et al. 2015

Hydrol. Earth Syst. Sci.

Self Cite

View full text Add to dashboard Cite

Abstract. The potential of coupling soil moisture and a Universal Soil Loss Equation-based (USLE-based) model for event soil loss estimation at plot scale is carefully investigated at the Masse area, in central Italy. The derived model, named Soil Moisture for Erosion (SM4E), is applied by considering the unavailability of in situ soil moisture measurements, by using the data predicted by a soil water balance model (SWBM) and derived from satellite sensors, i.e., the Advanced SCATterometer (ASCAT). The soil loss estimation accuracy is validated using in situ measurements in which event observations at plot scale are available for the period 2008-2013. The results showed that including soil moisture observations in the event rainfall-runoff erosivity factor of the USLE enhances the capability of the model to account for variations in event soil losses, the soil moisture being an effective alternative to the estimated runoff, in the prediction of the event soil loss at Masse. The agreement between observed and estimated soil losses (through SM4E) is fairly satisfactory with a determination coefficient (log-scale) equal to ∼ 0.35 and a root mean square error (RMSE) of ∼ 2.8 Mg ha −1 . These results are particularly significant for the operational estimation of soil losses. Indeed, currently, soil moisture is a relatively simple measurement at the field scale and remote sensing data are also widely available on a global scale. Through satellite data, there is the potential of applying the SM4E model for large-scale monitoring and quantification of the soil erosion process.

show abstract

Section: F Todisco Et Almentioning

confidence: 99%

Section: Soil Moisture From Satellite Datamentioning

confidence: 99%

Use of satellite and modeled soil moisture data for predicting event soil loss at plot scale

Todisco

Brocca

Termite

et al. 2015

Hydrol. Earth Syst. Sci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Bu yöntem doyma oranları ile geri saçılım değerleri arasında kuvvetli bir ilişki olduğu varsayımı üzerine kuruludur ve aradaki değişimler için doğrusal interpolasyon yapılarak toprak nemi değerleri bulunmaktadır. ASCAT kaynaklı toprak nemi değerlerinin diğer toprak nemi değerleri ile karşılaştırılması şu ana kadar birçok çalışmada yapılmıştır [18,19]. Bu ürün için ayrıntılı bilgi [20,21] çalışmalarında mevcuttur.…”

Section: Ascat Uydu Kaynaklı Toprak Nemiunclassified

Üçlü Eşleştirme Yöntemi ile Uydu ve Hidrolojik Model Kaynaklı Toprak Nemi Değerlerinin Hata Oranlarının Bulunması

Yılmaz¹

2016

Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi

View full text Add to dashboard Cite

ÖzetToprak neminin iklim, taşkın, kuraklık ve rekolte gibi birçok çalışmada çok kritik bir öneme sahip olmasına karşın, toprak nemi ile alakalı çalışmalar ülkemizde çok kısıtlı kalmıştır. Hidrolojik model ve uydu verisi gibi değişik kaynaklardan elde edilen toprak nemi değerlerinin birleştirilerek daha iyi veri elde edilmesi, ancak bu verilerin hata oranlarının belirlenmesi ile mümkündür. Toprak nemi son yıllarda bilhassa hidroloji kaynaklı çalışmalarda sıklıkla kullanılmaktadır. Buna karşın üçlü eşleştirme tekniği kullanılarak veri setlerinin hata oranları ülkemizi çalışma sahası olarak alan bir çalışmada henüz elde edilmemiştir. Bu çalışmada NOAH hidroloji modeli ile LPRM ve ASCAT uydu verileri kaynaklı günlük toprak nemi değerlerinin hata oranları ülkemizin bitki örtüsünün dağılımını kapsayacak şekilde 10 alan üzerinde, 2007-2011 yılları verileri kıyaslanarak elde edilmiştir. Elde edilen sonuçlar NOAH hidroloji verilerinin LPRM ve ASCAT verilerine kıyasla ortalama olarak daha az hata içerdiğini ortaya koymuştur.Anahtar Kelimeler: Toprak nemi, Üçlü eşleştirme, Uzaktan algılama, Hidrolojik modelleme Estimation of the Errors of Remote Sensing and Hydrological Model-Based Soil Moisture Values Using Triple Collocation AbstractOnly limited studies in Turkey have focused their attention on soil moisture even though it has a key role in climate, flood, drought and agricultural yield related studies. Estimation of soil moisture product obtained via merging of hydrological model-and remote sensing-based soil moisture data sets is only possible if the errors of the merged products are estimated. Triple collocation methodology has been used in many hydrological studies, yet estimation of soil moisture product errors over Turkey has not been implemented before. In this study, errors of NOAH hydrological model-based and LPRM and ASCAT remote sensing-based soil moisture products are found between 2007-2011 over 10 locations that are considered to have different vegetation covers over Turkey. Results show NOAH hydrological model based soil moisture products have smaller errors on average compared to LPRM and ASCAT products for these 10 locations.

show abstract

“…Several studies have demonstrated soil moisture estimation from space using passive microwave (e.g., [2][3][4][5]) and active microwave (scatterometer) (e.g., [4,6,7]) at a coarse (25-150 km) spatial resolution. On the other hand, active microwave SAR data have been successfully used to retrieve the soil moisture at a finer (less than 100 m) spatial resolution but with a coarse (approximately 3 weeks) temporal resolution [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

MAPSM: A Spatio-Temporal Algorithm for Merging Soil Moisture from Active and Passive Microwave Remote Sensing

et al. 2016

View full text Add to dashboard Cite

Simple Summary: -A novel algorithm delivering high resolution soil moisture maps is developed by merging active (SAR) and passive microwave.-MAPSM is based on the concept of Water Change Capacity. -A case study using MAPSM is presented by using the RADARSAT-2 and SMOS retrieved soil moisture data products over Berambadi watershed, Karnataka, India. -The algorithm parameters show scalability from the spatial resolution of 20 m to 2000 m.Abstract: Availability of soil moisture observations at a high spatial and temporal resolution is a prerequisite for various hydrological, agricultural and meteorological applications. In the current study, a novel algorithm for merging soil moisture from active microwave (SAR) and passive microwave is presented. The MAPSM algorithm-Merge Active and Passive microwave Soil Moisture-uses a spatio-temporal approach based on the concept of the Water Change Capacity (WCC) which represents the amplitude and direction of change in the soil moisture at the fine spatial resolution. The algorithm is applied and validated during a period of 3 years spanning from 2010 to 2013 over the Berambadi watershed which is located in a semi-arid tropical region in the Karnataka state of south India. Passive microwave products are provided from ESA Level 2 soil moisture products derived from Soil Moisture and Ocean Salinity (SMOS) satellite (3 days temporal resolution and 40 km nominal spatial resolution). Active microwave are based on soil moisture retrievals from 30 images of RADARSAT-2 data (24 days temporal resolution and 20 m spatial resolution). The results show that MAPSM is able to provide a good estimate of soil moisture at a spatial resolution of 500 m with an RMSE of 0.025 m 3 /m 3 and 0.069 m 3 /m 3 when comparing it to soil moisture from RADARSAT-2 and in-situ measurements, respectively. The use of Sentinel-1 and RISAT products in MAPSM algorithm is envisioned over other areas where high number of revisits is available. This will need an update of the algorithm to take into account the angle sampling and resolution of Sentinel-1 and RISAT data.

show abstract

Soil moisture estimation through ASCAT and AMSR-E sensors: An intercomparison and validation study across Europe

Cited by 509 publications

References 72 publications

Use of satellite and modeled soil moisture data for predicting event soil loss at plot scale

Use of satellite and modeled soil moisture data for predicting event soil loss at plot scale

Üçlü Eşleştirme Yöntemi ile Uydu ve Hidrolojik Model Kaynaklı Toprak Nemi Değerlerinin Hata Oranlarının Bulunması

MAPSM: A Spatio-Temporal Algorithm for Merging Soil Moisture from Active and Passive Microwave Remote Sensing

Contact Info

Product

Resources

About