SMAP Algorithm Research Articles

HIGH-RESOLUTION ENHANCED PRODUCT BASED ON SMAP ACTIVE-PASSIVE APPROACH USING SENTINEL 1A AND 1B SAR DATA

Abstract. SMAP project released a new enhanced high-resolution (3km) soil moisture active-passive product. This product is obtained by combining the SMAP radiometer data and the Sentinel-1A and -1B Synthetic Aperture Radar (SAR) data. The approach used for this product draws heavily from the heritage SMAP active-passive algorithm. Modifications in the SMAP active-passive algorithm are done to accommodate the Copernicus Program’s Sentinel-1A and -1B multi-angular C-band SAR data. Assessment of the SMAP and Sentinel active-passive algorithm has been conducted and results show feasibility of estimating surface soil moisture at high-resolution in regions with low vegetation density (∼ < 3 kg m−2). The beta version of this product is released to public on Nov 1st, 2017. This high resolution (3 km) soil moisture product is useful for agriculture, flood mapping, watershed/rangeland management, and ecological/hydrological applications.

Bioclimatic modelling using Gaussian mixture distributions and multiscale segmentation

ABSTRACTAim To introduce Gaussian mixture distributions and sequential maximum a posteriori image segmentation (GM‐SMAP) as a model that predicts species ranges from mapped climatic variables, and to compare its predictive capacity with two commonly used bioclimatic models: regression tree analysis (RTA) and smoothed response surfaces (SRS).Location North‐west North America.Methods We compared models for their ability to predict the distributional range of western hemlock (Tsuga heterophylla). We calculated and projected nine climatic and water‐balance variables to a 2‐km grid up to 140 km from the T. heterophylla range. Models were trained using the five variables selected by RTA, as well as subsets of three variables. Goodness of fit was assessed using models trained and tested on the entire study area. Predictive capacity was assessed using 100 cross‐validation tests, each trained on a randomly sampled 1% of the study area and tested on the complement of the study area.Results Models using all five variables were significantly better than three‐variable models. Model fit was greatest for SRS. GM‐SMAP misclassified slightly more area and RTA misclassified almost twice the area compared to SRS. However, cross‐ validation showed that the predictive capacity was clearly greatest for GM‐SMAP and lowest for SRS, indicating that GM‐SMAP makes more accurate predictions from sparse data.Main conclusions GM distributions prevent overfitting using an information‐theoretic approach, and the SMAP algorithm minimizes the spatial extent of the largest misclassified area using a multiscale method. These properties, useful for image classification, also aid their strong predictive capacity as a bioclimatic model. SRS overfit the data, lowering its predictive capacity, and RTA failed to capture details of interactions among variables, yielding a poor fit. These results demonstrate the strong potential of GM‐SMAP as a bioclimatic model.

Comparison of Algorithms for Classifying Swedish Landcover Using Landsat TM and ERS-1 SAR Data

Sixteen landcover classes in a representative Swedish environment were analyzed and classified using one Landsat TM scene and seven ERS-1 SAR.PRI images acquired during 1993. Spectral and backscattering signature separabilities are analyzed using the Jeffries–Matusita distance measure to determine which combinations of channels/images contained the most information. Maximum likelihood, sequential maximum a posteriori (SMAP, a Bayesian image segmentation algorithm), and back propagation neural network classification algorithms were applied and their performances evaluated. Results of the separability analyses indicated that the multitemporal SAR data contained more separable landcover information than did the multispectral TM data; the highest separabilities were achieved when the TM and SAR data were combined. Classification accuracy evaluation results indicate that the SMAP algorithm outperformed the maximum likelihood algorithm which, in turn, outperformed the neural network algorithm. The best KAPPA values, using combined data, were 0.495 for SMAP, 0.445 for maximum likelihood, and 0.432 for neural network. Corresponding overall accuracy values were 57.1%, 52.4%, and 51.2%, respectively. A comparison between lumped crop area statistics with areal sums calculated from the classified satellite data gave the highest correspondence where the SMAP algorithm was used, followed by the maximum likelihood and neural network algorithms. Based on our application, we can therefore confirm the value of a multisource optical/SAR approach for analyzing landcover and the improvements to classification achieved using the SMAP algorithm.