A vector radiative transfer model for sea-surface salinity retrieval from space: a non-raining case

• Published in: International journal of remote sensing Vol. 39; no. 22; pp. 8361 - 8385
• Main Authors: Jin, Xu-Chen, Pan, De-Lu, He, Xian-Qiang, Bai, Yan, Shanmugam, Palanisamy, Gong, Fang, Zhu, Qian-Kun
• Format: Journal Article
• Language: English
• Published: London Taylor & Francis 17.11.2018; Taylor & Francis Ltd
• Subjects: Accuracy; Atmosphere; Atmospheric models; Brightness temperature; Computer simulation; Computing time; Estimation errors; Evaluation; Mathematical analysis; Matrix methods; Meteorological satellites; Microwave sensors; Ocean-atmosphere system; Oceans; Predictions; Radiance; Radiative transfer; Remote sensing; Retrieval; Salinity; Satellites; Scientific research; Sea surface temperature; Sensors; Soil; Soil moisture; Surface radiation temperature; Surface salinity; Surface temperature; Temperature
• ISSN: 0143-1161; 1366-5901
• DOI: 10.1080/01431161.2018.1488283

Sea-surface salinity (SSS) can be measured from space using a microwave sensor. However, achieving the desired accuracy in SSS retrieval is challenging due to the lower sensitivity of the brightness temperature to SSS especially at low sea-surface temperature conditions. The retrieval accuracy can be further degraded due to the atmospheric and sea-surface effects (including emission and reflection), which require more accurate correction methods based on the radiative transfer model. In this article, a vector radiative transfer model (VRTM) was developed based on a matrix operator method that considers the ocean-atmosphere system under non-raining conditions. The results from this model were compared with measurement data provided by the Soil Moisture and Ocean Salinity (SMOS) satellite sensor and the results from two other RT models (RT4 model and a forward model of the European Space Agency, ESA). Statistical evaluation of these results revealed that estimation errors of top of atmosphere (TOA) radiance by the VRTM model was less than 0.3% as compared to the RT4 model results. The difference of the brightness temperatures predicted by the VRTM model and measured by the SMOS was within 1.5 K which was better than the ESA's forward model predictions. These results suggest that the VRTM is relatively more accurate and has high computational efficiency for simulating the TOA brightness temperature for various scientific research and remote-sensing applications.