Inter-comparison of SMOS and AMSR-E soil moisture products during flood years (2010–2011) over Pakistan

Anam, Roha; Chishtie, Farrukh; Ghuffar, Sajid; Qazi, Waqas A.; Shahid, Imran

doi:10.1080/22797254.2017.1352461

Cited by 7 publications

(4 citation statements)

References 43 publications

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“…At present, both SMAP and SMOS operate on L‐band microwave radiometers (1–2 GHz), where the L‐band observation can penetrate to a deeper depth than other bands (e.g., X and C bands), which makes it more reliable for SM retrieval. The soil moisture retrieved from the SMOS and SMAP observations on a global scale is released freely to users, supporting many applications and associated soil moisture research (Anam et al, ; Shellito et al, ). In addition to the capabilities of these two satellite missions, there are many operational sensors (active and passive), which provide surface SM observations at C band and X band.…”

Section: Introductionmentioning

confidence: 99%

Spatial Evaluation and Assimilation of SMAP, SMOS, and ASCAT Satellite Soil Moisture Products Over Africa Using Statistical Techniques

Mousa

Hong

2020

Earth and Space Science

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The limited number of in situ stations of surface soil moisture (SM) in Africa creates a shortage in the validation of SM satellite products. Therefore, this study investigates the performance of Soil Moisture Active Passive (SMAP), Soil Moisture and Ocean Salinity (SMOS), and the H113 product from the Advanced Scatterometer (ASCAT) on the regional scale over Africa through these goals: (1) validate of satellite SM products against in situ stations and SM data from the ERA-Interim atmospheric reanalysis product, (2) study the spatiotemporal variability of satellite SM products on the regional scale, and (3) evaluate the regional scale error patterns and investigate regions where the assimilation of satellites SM data may add improvement to ERA-Interim. Standard statistical metrics, hovmöller diagrams, and the Triple Collocation (TC) model were used to achieve these goals. Land cover data, Normalized Difference Vegetation Index, and precipitation data were used to interpret results. The validation results based on statistical metrics and TC indicate that over the desert and shrub, passive products showed better performance than ASCAT, while over moderate vegetation areas (grassland), SMAP had the best among SM products. Over high densely vegetated regions, ASCAT showed a high comparatively performance than passive products. The potential regions for assimilation of satellite data sets were selected to be over savannas and grassland regions for ASCAT, and over shrub and grassland regions for SMAP. In particular, SMAP and ASCAT SM data sets are considered more stable than SMOS for data assimilation and capturing the spatial distribution of SM on the regional scale over Africa.

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Section: Introductionmentioning

confidence: 99%

Spatial Evaluation and Assimilation of SMAP, SMOS, and ASCAT Satellite Soil Moisture Products Over Africa Using Statistical Techniques

Mousa

Hong

2020

Earth and Space Science

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“…At present, Soil Moisture Active Passive (SMAP, 1.41 GHz) and Soil Moisture and Ocean Salinity (SMOS, 1.4 GHz) are currently available satellites carrying L-band microwave radiometers. Global surface SM products retrieved from the observations of SMAP and SMOS have been released to the public, which have great value for related researches and applications (Anam, Chishtie, Ghuffar, Qazi, & Shahid, 2017;Shellito et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Spatial evaluation of L-band satellite-based soil moisture products in the upper Huai River basin of China

Zhu

Liu

Zhu

et al. 2019

European Journal of Remote Sensing

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Using dense soil moisture (SM) measurements in the upper Huai River basin of China, this study evaluated the spatial patterns of L-band satellite-based SM products, including Soil Moisture Active Passive (SMAP) L3, Soil Moisture and Ocean Salinity (SMOS) L3 and the European Space Agency's Climate Change Initiative (ESA CCI) SM products. The mean difference (MD), root mean squared error (RMSE), unbiased root mean square error (ubRMSE) and Pearson correlation coefficient (R), were used in the evaluation. The evaluation results presented that SMAP and ESA CCI products can well capture the temporal variation of SM at single points quite well, with average R values of 0.51 and 0.46, respectively. And SMAP had the highest overall accuracy among the three satellite-based products in study area. We also analyzed the correlations between the four accuracy indexes and six environmental factors including the proportions of five land use/land cover types (i.e. water bodies, paddy fields, construction land, dryland and forest) and the average NDVI (Normalized Difference Vegetation Index) in 2016 in each grid. Analysis showed that the proportions of paddy fields and water bodies in each grid had significant positive correlations with MD, RMSE and ubRMSE, while NDVI, and the proportions of dryland and construction land had significant negative correlations with these three indexes. The significant correlations between the accuracy of SMAP, SMOS and ESA CCI SM products and environmental factors indicate that there exist systematic biases in these products, which can provide valuable insights into algorithm improvements.

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“…Thus, the validation of SMOS products is very important to determine the accuracy and the quality of retrievals before being used for different applications. Several recent studies have validated SMOS products in comparison to ground-based in situ observations at a global scale [24][25][26][27][28][29] and over local and regional scales such as Canada [30,31], the United States [32][33][34][35][36][37], Europe [37][38][39][40][41][42][43][44], Africa [42,45], and Australia [17,46], and some research has been carried out in Asia such as over the Tibetan Plateau [47,48], Pakistan [49], and China [50,51] that represents the accuracy and the quality of SMOS products. However, very few calibration and validation activities have been done for SMOS products in Iran.…”

Section: Introductionmentioning

confidence: 99%

Validation of the SMOS Level 1C Brightness Temperature and Level 2 Soil Moisture Data over the West and Southwest of Iran

et al. 2020

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The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) mission with the MIRAS (Microwave Imaging Radiometer using Aperture Synthesis) L-band radiometer provides global soil moisture (SM) data. SM data and products from remote sensing are relatively new, but they are providing significant observations for weather forecasting, water resources management, agriculture, land surface, and climate models assessment, etc. However, the accuracy of satellite measurements is still subject to error from the retrieval algorithms and vegetation cover. Therefore, the validation of satellite measurements is crucial to understand the quality of retrieval products. The objectives of this study, precisely framed within this mission, are (i) validation of the SMOS Level 1C Brightness Temperature (TBSMOS) products in comparison with simulated products from the L-MEB model (TBL-MEB) and (ii) validation of the SMOS Level 2 SM (SMSMOS) products against ground-based measurements at 10 significant Iranian agrometeorological stations. The validations were performed for the period of January 2012 to May 2015 over the Southwest and West of Iran. The results of the validation analysis showed an RMSE ranging between 9 to 13 K and a strong correlation (R = 0.61–0.84) between TBSMOS and TBL-MEB at all stations. The bias values (0.1 to 7.5 K) showed a slight overestimation for TBSMOS at most of the stations. The results of SMSMOS validation indicated a high agreement (RMSE = 0.046–0.079 m3 m−3 and R = 0.65–0.84) between the satellite SM and in situ measurements over all the stations. The findings of this research indicated that SMSMOS shows high accuracy and agreement with in situ measurements which validate its potential. Due to the limitation of SM measurements in Iran, the SMOS products can be used in different scientific and practical applications at different Iranian study areas.

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Inter-comparison of SMOS and AMSR-E soil moisture products during flood years (2010–2011) over Pakistan

Cited by 7 publications

References 43 publications

Spatial Evaluation and Assimilation of SMAP, SMOS, and ASCAT Satellite Soil Moisture Products Over Africa Using Statistical Techniques

Spatial Evaluation and Assimilation of SMAP, SMOS, and ASCAT Satellite Soil Moisture Products Over Africa Using Statistical Techniques

Spatial evaluation of L-band satellite-based soil moisture products in the upper Huai River basin of China

Validation of the SMOS Level 1C Brightness Temperature and Level 2 Soil Moisture Data over the West and Southwest of Iran

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