Details

Autor(en) / Beteiligte

• Hodges, Erik
• Chew, Clara
• Small, Eric
• Al-Khaldi, Mohammad
• Ouellette, Jeffrey D.
• Johnson, Joel T.
• Lei, Fangni
• Kurum, Mehmet
• Gurbuz, Ali
• Senyurek, Volkan
• Xu, Xiaolan
• Shah, Rashmi
• Yueh, Simon
• Hayashi, Akiko
• Setti, Paulo T.
• Tabibi, Sajad
• Santi, Emanuele
• Pettinato, Simone
• Roberts, T. Max
• Colwell, Ian
• Lowe, Stephen
• Ruf, Christopher S.
• Moghaddam, Mahta

Titel

A Blended CYGNSS Soil Moisture Product Partitioned with Ancillary Data

Ist Teil von

• 2024 United States National Committee of URSI National Radio Science Meeting (USNC-URSI NRSM), 2024, p.174-174

Ort / Verlag

USNC-URSI

Erscheinungsjahr

2024

Quelle

IEEE

Beschreibungen/Notizen

• Observations recorded by the NASA Cyclone Global Navigation Satellite System (CYGNSS) mission have demonstrated significant sensitivity to soil moisture, motivating the development of several soil moisture products. An assessment of these products was conducted by the CYGNSS science team. The results of the assessment showed that the accuracy of each product may vary based on environmental factors such as surface roughness, vegetation, or terrain complexity. The varied responses led to an effort to combine these products into a single blended product, such that the best features of each product can be used to construct one optimum product. To achieve a preliminary result, five different CYGNSS soil moisture products were combined using a minimum variance estimator (MVE). This approach used in situ soil moisture data to compute the covariance matrix of the soil moisture error for the products. From this, a weighted averaging scheme was derived that minimizes the variance of the blended soil moisture values and therefore the root-mean-square error (RMSE). The performance of the blended product produced by this work appears to be comparable to the performance of the Soil Moisture Active Passive (SMAP) radiometer products.