Evaluation of the ESA CCI soil moisture product using ground-based observations

Dorigo, Wouter; Gruber, A.; Jeu, R. A. M. de; Wagner, Wolfgang; Stacke, Tobias; Loew, Alexander; Albergel, Clément; Brocca, Luca; Chung, Daniel; Parinussa, Robert; Kidd, Richard

doi:10.1016/j.rse.2014.07.023

Cited by 497 publications

(488 citation statements)

References 95 publications

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“…While the general accuracy of the ESA CCI soil moisture data [Loew et al, 2013;Dorigo et al, 2015] and its usefulness in diagnosing land-atmosphere coupling [Hirschi et al, 2014] have been examined, its capability in representing the short-term soil moisture drought has received little attention.…”

Section: 1002/2015gl064125mentioning

confidence: 99%

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Microwave remote sensing of short‐term droughts during crop growing seasons

Yuan

Pan

et al. 2015

Geophysical Research Letters

150

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Severe short‐term (monthly to seasonal) droughts frequently occurred over China in recent years, with devastating impacts on crop production. This study assesses the capability of microwave remote sensing in detecting soil moisture (agricultural) droughts over China and in providing early warnings. The 22 year (1992–2013) European Space Agency satellite soil moisture retrievals are compared against the in situ observations at 312 stations in China, the global soil moisture reanalysis, and the observed rainfall deficit. Both the reanalysis and remote sensing products can only detect less than 60% of drought months at in situ station scale, but they capture the interannual variations of short‐term drought area at river basin scales quite well. As compared with reanalysis, the passive and merged microwave products have better drought detection over sparsely vegetated regions in northwestern China and the active microwave product with better vegetation penetration works the best in eastern China.

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Section: 1002/2015gl064125mentioning

confidence: 99%

“…This is because the southern China is densely vegetated, where the active product has a better penetration than the passive product [Dorigo et al, 2015]. The active product is also the best in capturing the north-southeast dipole pattern in eastern China for the response of summer time drought Figure 4.…”

Section: Geophysical Research Lettersmentioning

confidence: 99%

Microwave remote sensing of short‐term droughts during crop growing seasons

Yuan

Pan

et al. 2015

Geophysical Research Letters

150

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“…Additionally, the ECV SM data displayed higher correlations (R between 0.84 and 0.85) with the in-situ measurements in comparison to the Kilworth site. As identified in Dorigo et al (2015), sites which exhibit a pronounced annual soil moisture cycle and where this seasonality is suitably detected by the ECV SM product, usually result in high correlations between the ECV SM and in-situ data such as those obtained at the Irish sites. At the Spanish sites, the soil moisture is a lot less variable at Zamarron, compared to Las Arenas (see Figure 4).…”

Section: Time Series Temporal Analysismentioning

confidence: 93%

“…The ECV SM product has been validated across different regions using in-situ, model and SAR-derived soil moisture datasets in previous studies (e.g. Albergel et al, 2013b;Loew et al, 2013;Dorigo et al, 2015;Pratola et al, 2014;Zeng et al, 2015) where good agreement between the datasets was generally found. For example, Zeng et al (2015) found the ECV SM product to be highly related to in-situ data from two different soil moisture networks at the Tibetan Plateau, with the highest R values (0.70 -0.85) and smallest ubRMSD values (0.034 -0.042m 3 m -3 ) compared to six other satellite derived soil moisture products (AMSR-E (NASA product), AMSR-E (JAXA product), AMSR-E (LPRM product), AMSR-2, ASCAT, and SMOS).…”

Section: Ecv Soil Moisture Observationsmentioning

confidence: 99%

“…For example, Albergel et al (2013c) validated three global soil moisture products using a combination of 196 in-situ stations taken from five different soil moisture networks across the world. Similarly, Paulik et al (2014) and Dorigo et al (2015) used over 600 in-situ stations for validating ASCAT and ECV SM soil moisture products respectively. All these studies generally found good agreement between the satellite derived and in-situ observations.…”

Section: Introductionmentioning

confidence: 99%

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Intercomparison of Soil Moisture Retrievals From In Situ, ASAR, and ECV SM Data Sets Over Different European Sites

Barrett¹,

Pratola²,

Gruber³

et al. 2016

Satellite Soil Moisture Retrieval

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The availability of satellite-derived global surface soil moisture products during the last decade has opened up great opportunities to incorporate these observations into applications in hydrology, meteorology and climatic modelling. This study evaluates a new global soil moisture product developed under the framework of the European Space Agency (ESA) Climate Change Initiative (CCI), using finer spatial resolution Synthetic Aperture Radar (SAR) and ground-based measurements of soil moisture. The analysis is carried out over selected in-situ networks over Ireland, Spain, and Finland with the aim of assessing the temporal representativeness of the coarse scale CCI Essential Climate Variable (ECV) soil moisture product (ECV SM) in these different areas. A good agreement (correlation coefficient (R) values between 0.53 and 0.92) was observed between the three soil moisture datasets for the Irish and Spanish sites while a reasonable agreement (R values between 0.41 and 0.52) was observed between the SAR and ECV SM soil moisture datasets at the Finnish sites. Overall, the two different satellite derived products captured the soil moisture temporal variations well and were in good agreement with each other, highlighting the confidence of using the coarse scale ECV SM product to track soil moisture variability in time.

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Prediction of vegetation anomalies to improve food security and water management in India

Asoka

Mishra

2015

Geophysical Research Letters

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Prediction of vegetation anomalies at regional scales is essential for management of food and water resources. Forecast of vegetation anomalies at 1–3 months lead time can help in decision making. Here we show that normalized difference vegetation index (NDVI) along with other hydroclimatic variables (soil moisture and sea surface temperature) can be effectively used to predict vegetation anomalies in India. The spatiotemporal analysis of NDVI showed significant greening over the region during the period of 1982–2013. The root‐zone soil moisture showed a positive correlation with NDVI, whereas the El Niño–Southern Oscillation index (Nino 3.4) is negatively correlated in most of the regions. We extended this relationship to develop a model to predict NDVI in 1 to 3 months lead time. The predicted vegetation anomalies compare well with observations, which can be effectively utilized in early warning and better planning in water resources and agricultural sectors in India.

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Evaluation of the ESA CCI soil moisture product using ground-based observations

Cited by 497 publications

References 95 publications

Microwave remote sensing of short‐term droughts during crop growing seasons

Microwave remote sensing of short‐term droughts during crop growing seasons

Intercomparison of Soil Moisture Retrievals From In Situ, ASAR, and ECV SM Data Sets Over Different European Sites

Prediction of vegetation anomalies to improve food security and water management in India

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