Description: Quantification of tree canopy area and aboveground biomass is essential for monitoring ecosystems’ ecological functionalities, e.g., carbon sequestration and habitat provision. Miombo woodlands are vastly existent in developing countries that often lack resources to acquire LiDAR data or high spatiospectral resolution remote sensing data that have been proven to accurately estimate these structural attributes. This study explored the utility of freely available (via Google Maps) high (1-m) resolution red, green, and blue (RGB) satellite imagery in combination with object-based image analysis (OBIA) for estimating tree canopy area and aboveground biomass in Miombo woodlands. We randomly established 41 225-m 2 plots in Mukuvisi Woodland, Zimbabwe, and used RGB data with OBIA to estimate tree canopy area in those plots. We also field measured the height, canopy area, and trunk diameter at breast height of all trees that fell in those plots, then used the field data and a published allometric equation to estimate aboveground tree biomass (AGB). OBIA classification accuracy was high (Jaccard similarity index = 0.96). Data analysis showed significant positive linear relationship between AGB and field-measured canopy area $(R^{2} = {{0.87}}, p 〈 {{0.003}})$ , and significant exponential relationships between: 1) OBIA-derived canopy area and AGB $(R^{2} = {{0.56}}, p 〈 {{0.0001}})$ ; and 2) field-measured canopy area and OBIA-derived canopy area $(R^{2} = {{0.63}}, p 〈 {{0.0001}})$ , and no significant differences $(t = {{19.67}}, df = {{78}}, p = {{0.28}})$ between field-measured canopy are- ( $bar{ times } = 187.11,{{rm{m}}^2},sigma = 127.03$ ) and OBIA-derived canopy area ( $bar{ times } = 163.00,{{rm{m}}^2},sigma = 50.08$ ). We conclude that RGB data with OBIA are suitable for estimating tree canopy area in Miombo woodlands for various low-accuracy purposes (e.g., biomass estimation).

Description: The parameters used for passive soil moisture retrieval algorithms reported in the literature encompass a wide range, leading to a large uncertainty in the applicability of those values. This paper presents an evaluation of the proposed parameterizations of the tau–omega model from 1) the soil moisture active passive (SMAP) algorithm theoretical basis document (ATBD) for global condition and 2) calibrated parameters from the National Airborne Field Experiment (NAFE’05) for Australian conditions, with special focus on the vegetation parameter b and roughness parameter $H_{R}$ . This study uses airborne L -band data and field observations from the SMAP experiments conducted in south-eastern Australia. Results show that the accuracy with the proposed parameterizations from SMAP ATBD was satisfactory at 100-m spatial resolution for maize (0.07 m 3 /m 3 ) and pasture (0.07 m 3 /m 3 ), while it decreased to 0.19 m 3 /m 3 for wheat. Calibrated parameters from the NAFE’05 did not provide better results, with the accuracy of wheat degrading to 0.23 m 3 /m 3 . After a comprehensive site-specific calibration and validation at 100-m spatial resolution, this result was improved to 0.10 m 3 /m 3 . Further calibration and validation were performed at 1-km resolution against intensive ground sampling and at 3-km against in situ monitoring stations. Results showed an accuracy over grassland and cropland of 0.04 m 3 /m 3 and 0.05 m 3 /m 3 , respectively. This study also suggests that the paramet- rs from SMAP ATBD show an underestimation of soil moisture, with the roughness parameter $H_{R}$ being too low for south-eastern Australian condition. Therefore, a new set of b and $H_{R}$ parameters for ten different land cover types was proposed in this study.

Description: Tea is an important cash crop in Kenya, grown in a climatically restricted geographic area where climatic variability is starting to affect yield productivity levels. This paper assesses the feasibility of monitoring tea growth between, but also within fields, using X-band COSMO-SkyMed SAR images (five images at VV polarization and five images at HH polarization). We detect the harvested and nonharvested areas for each field, based on the loss of interferometric coherence between two images, with an accuracy of 52% at VV polarization and 74% at HH polarization. We then implement a normalization method to isolate the scattering component related to shoot growth and eliminate the effects of moisture and local incidence angle. After normalization, we analyze the difference in backscatter between harvested and nonharvested areas. At HH polarization, our backscatter normalization reveals a small decrease ( $sim0.1$  dB) in HH backscatter after harvest. However, this decrease is too small for monitoring shoot growth. The decrease is not clear at VV polarization. This is attributed to the predominantly horizontal orientation of the harvested leaves.

Description: A novel method that combines joint clusters and iterative graph cuts for ALS point cloud filtering is proposed in this paper. The method first extracts clusters of points from the raw point cloud, and then classifies ground points at the cluster level. There are four main steps, i.e., two-step point cloud clustering, critical point extraction, initial terrain determination, and terrain densification based on iterative graph cuts. Smooth clusters, rough clusters, and scattered points are extracted by the two-step clustering to depict the raw point cloud, which reduces the complexity of raw data and the judgment difficulty in the subsequent procedures. Critical points of each cluster are extracted, and the initial terrain is determined among the smooth clusters. Using the initial terrain and critical points, iterative graph cuts is performed to segment ground and nonground points at the cluster level. Experiments on ISPRS dataset with a low point density and Utah dataset with a moderate point density show that our approach provides a satisfactory trade off between Type I and Type II errors. Moreover, our method significantly outperforms progressive TIN densification based filters, and successfully controls Type II errors.

Description: Pan-sharpening is a fundamental and significant task in the field of remote sensing imagery processing, in which high-resolution spatial details from panchromatic images are employed to enhance the spatial resolution of multispectral (MS) images. As the transformation from low spatial resolution MS image to high-resolution MS image is complex and highly nonlinear, inspired by the powerful representation for nonlinear relationships of deep neural networks, we introduce multiscale feature extraction and residual learning into the basic convolutional neural network (CNN) architecture and propose the multiscale and multidepth CNN for the pan-sharpening of remote sensing imagery. Both the quantitative assessment results and the visual assessment confirm that the proposed network yields high-resolution MS images that are superior to the images produced by the compared state-of-the-art methods.

Description: Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Description: Provides instructions and guidelines to prospective authors who wish to submit manuscripts.

Description: This paper presents the first results on comparisons of Scatterometer Satellite-1 (SCATSat-1) derived wind datasets with the in situ , reanalysis as well as another operational scatterometer derived winds in the Bay of Bengal during the period November 2016–March 2017. The comparisons of daily gridded wind products of SCATSat-1 with buoys show good correlations (>0.83), higher skill scores (>0.92), and lower root mean square errors (RMSEs) of 0–2 m/s for wind speeds (WS) at the buoy locations. Similarly, the results corresponding to wind directions (WD) show higher correlations (>0.95), higher skill scores (>0.96), and relatively lower RMSEs (15–30°). Further, the intercomparisons of SCATSat-1 with Advanced Scatterometer and European Centre for Medium Range Weather Forecasts reanalysis winds show strong correlations for both WS (>0.85) and WD (>0.94). This paper also reports the capability of SCATSat-1 to capture three tropical cyclones Kyant, Vardah, and Mora during the period of study with the highest WS of 23.5 m/s.

Description: A trapezoid interpolation thermal disaggregation (TI_DisTrad) model was proposed in this study. This model can disaggregate coarse resolution land surface temperature (LST) to fine resolution LST based on fractional vegetation cover (FVC) versus LST space. The proposed model assumes that the quantitative relationships among the Bowen ratio, FVC and LST can work for the pixels inside the FVC-LST space at both coarser and finer resolutions. Pixels that were outside the FVC-LST space were addressed with a support vector machine regression. We evaluated the TI_DisTrad model over an agricultural region in central Iowa (USA) and an urban region in Beijing (China). The performance of the TI_DisTrad model was assessed by comparing results against those of five other popular benchmark models. The results show that the TI_DisTrad model was slightly superior to three of the benchmark models over the agricultural regions and achieved more accurate LST compared to two of the benchmark models over the urban region. When using two surface energy balance models (the one-source model and the two-source model), the estimated evapotranspiration (ET) from the TI_DisTrad disaggregated LST data was more accurate than the estimated ET from the disaggregated LST obtained using the other benchmark approaches, corresponding to an increase in average accuracy of the TI_DisTrad model.

Description: The fuzzy c-means (FCM) algorithm and many improved algorithms incorporating spatial information have been proven to be effective in image segmentation. However, these methods are not adaptable to process synthetic aperture radar (SAR) images owing to the intrinsic speckle noise. Our solution, which enables the effective segmentation of SAR images by guaranteeing noise-immunity and edge detail preservation simultaneously, is to propose a robust FCM algorithm based on Bayesian nonlocal spatial information (RFCM $_$ BNL). The nonlocal idea considers more useful information for generating an auxiliary image. We measure the similarity between patches by utilizing a dedicated noise model for SAR images, and then apply it to the Bayesian formulation. Then we derive a new statistical distance, which is insensitive to speckle noise. Additionally, we ensure that the algorithm is robust to outliers by employing the entropy of the local gray-level histogram to control the extent to which the nonlocal spatial information term is adaptive to pixels. Experiments on simulated and real SAR images show that RFCM $_$ BNL obtains the best result for SAR image segmentation compared with seven other fuzzy clustering algorithms.