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Knowledge on the spatial-temporal variation of soil moisture is essential to many hydrometeorology applications. In this study, we proposed a new soil moisture index (SMI) from passive microwave observations, aiming to capture the soil moisture variability. The new SMI is developed based on the underlying physical basis that vegetation and surface roughness exert similar effects on the variation of land surface emissivity and microwave polarization difference radio (MPDI), but they act in an opposite way compared with soil moisture. Hence, we can obtain the SMI value in a two-dimensional space by combining use of land surface emissivity and MPDI to isolate the contribution of soil moisture and that of vegetation and surface roughness. We calculated the SMI by using the L-band SMAP Level-3 datasets and validated it with five well calibrated and dense soil moisture networks and also compared it with SMAP and ESA CCI soil moisture products. The results show the SMI exhibits the highest R (0.87) and lowest RMSE (0.028 m3 m−3) value after removing the systematic bias by using the cumulative distribution function (CDF) matching technique among the satellite products during the whole study period, thus demonstrating its good capability of tracking the temporal variation of soil moisture and its potential usage in various hydrometeorology applications.

To understand carbon cycling in marginal seas better, particulate organic carbon (POC) concentrations, POC fluxes and primary production (PP) were measured in the East China Sea (ECS) in summer 2007. Higher concentrations of POC were observed in the inner shelf, and lower POC values were found in the outer shelf. Similar to POC concentrations, elevated uncorrected POC fluxes (720–7300 mg C m−2 d−1) were found in the inner shelf, and lower POC fluxes (80–150 mg C m−2 d−1) were in the outer shelf, respectively. PP values (~ 340–3380 mg C m−2 d−1) had analogous distribution patterns to POC fluxes, while some of PP values were significantly lower than POC fluxes, suggesting that contributions of resuspended particles to POC fluxes need to be appropriately corrected. A vertical mixing model was used to correct effects of bottom sediment resuspension, and the lowest and highest corrected POC fluxes were in the outer shelf (58 ± 33 mg C m−2 d−1) and the inner shelf (785 ± 438 mg C m−2 d−1), respectively. The corrected POC fluxes (486 to 785 mg C m−2 d−1) in the inner shelf could be the minimum value because we could not exactly distinguish the effect of POC flux from Changjiang influence with turbid waters. The results suggest that 27–93% of the POC flux in the ECS might be from the contribution of resuspension of bottom sediments rather than from the actual biogenic carbon sinking flux. While the vertical mixing model is not a perfect model to solve sediment resuspension because it ignores biological degradation of sinking particles, Changjiang plume (or terrestrial) inputs and lateral transport, it makes significant progress in both correcting the resuspension problem and in assessing a reasonable quantitative estimate of POC flux in a marginal sea.

Multispectral remote sensing images are widely used for landuse/landcover (LULC) classification. Performance of such classification practices is normally evaluated through the confusion matrix which summarizes the producer’s and user’s accuracies and the overall accuracy. However, the confusion matrix is based on the classification results of a set of multi-class training data. As a result, the classification accuracies are heavily dependent on the representativeness of the training data set and it is imperative for practitioners to assess the uncertainties of LULC classification in order for a full understanding of the classification results. In addition, the Gaussian-based maximum likelihood classifier (GMLC) is widely applied in many practices of LULC classification. The GMLC assumes the classification features jointly form a multivariate normal distribution, whereas as, in reality, many features of individual landcover classes have been found to be non-Gaussian. Direct application of GMLC will certainly affect the classification results. In a pilot study conducted in Taipei and its vicinity, we tackled these two problems by firstly transforming the original training data set to a corresponding data set which forms a multivariate normal distribution before conducting LULC classification using GMLC. We then applied the bootstrap resampling technique to generate a large set of multi-class resampled training data from the multivariate normal training data set. LULC classification was then implemented for each resampled training data set using the GMLC. Finally, the uncertainties of LULC classification accuracies were assessed by evaluating the means and standard deviations of the producer’s and user’s accuracies of individual LULC classes which were derived from a set of confusion matrices. Results of this study demonstrate that Gaussian-transformation of the original training data achieved better classification accuracies and the bootstrap resampling technique is a very helpful tool for assessing uncertainties of LULC classification. The uncertainties in classification accuracies were also found to be affected by the sizes of class-specific training samples.