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The Landsat 1–5 multispectral scanner system (MSS) collected records of land surface mainly during 1972–1992. Investigations on MSS have been relatively limited compared with the numerous investigations on its successors, such as Thematic Mapper (TM) and Enhanced TM Plus (ETM+). The benefits of the Landsat program are not fully accomplished without the inclusion of MSS archives. Investigations on the Landsat 1–5 MSS channel reflectance characteristics wereperformed followed by derived vegetation spectral indices and the Tasseled Cap (TC) transformed features mainly using a collection of synthesized records. On average, the Landsat 4 MSS is generally comparable to the Landsat 5 MSS. The Landsat 1–3 MSSs show disagreement in channel reflectance compared with the Landsat 5 MSS, especially for the red channel (600–700 nm) and the near-infrared channel (700–800 nm). Meanwhile, the relative differences for vegetation spectral indices of the Landsat 3 MSS are mainly from −16% to −5% with the median about −11.5%, while those of the Landsat 2 MSS are mainly from −15% to −7%. Cross-validation tests and two case applications suggested that between-sensor consistency was improved generally through the transformation models generated by ordinary least-squares regression. To improve the consistency of the vegetation indices and the TC greenness, direct strategy employing respective transformation models was more effective than calculations based on the transformed channel reflectance. Considering the shortages of the Landsat MSS archives, further efforts are needed to improve its comparability with observations by other successive Landsat sensors.

Compact polarimetric synthetic aperture radar (CP SAR), as a new technique or observation system, has attracted much attention in recent years. Compared with quad-polarization SAR (QP SAR), CP SAR provides an observation with a wider swath, while, compared with linear dual-polarization SAR, retains more polarization information in observations. These characteristics make CP SAR a useful tool in marine environmental applications. Previous studies showed the potential of CP SAR images for ship detection. However, false alarms, caused by ocean clutter and the lack of detailed information about ships, largely hinder traditional methods from feature selection for ship discrimination. In this paper, a segmentation method designed specifically for ship detection from CP SAR images is proposed. The pixel-wise detection is based on a fully convolutional network (i.e., U-Net). In particular, three classes (ship, land, and sea) were considered in the classification scheme. To extract features, a series of down-samplings with several convolutions were employed. Then, to generate classifications, deep semantic and shallow high-resolution features were used in up-sampling. Experiments on several CP SAR images simulated from Gaofen-3 QP SAR images demonstrate the effectiveness of the proposed method. Compared with Faster RCNN (region-based convolutional neural network), which is considered a popular and effective deep learning network for object detection, the newly proposed method, with precision and recall greater than 90% and a F1 score of 0.912, performs better at ship detection. Additionally, findings verify the advantages of the CP configuration compared with single polarization and linear dual-polarization.

Comprehensive investigations on the between-sensor comparability among Landsat sensors have been relatively limited compared with the increasing use of multi-temporal Landsat records in time series analyses. More seriously, the sensor-related difference has not always been considered in applications. Accordingly, comparisons were conducted among all Landsat sensors available currently, including Multispectral Scanner (MSS), Thematic Mappers (TM), Enhanced Thematic Mappers (ETM+), and Operational Land Imager (OLI)) in land cover mapping, based on a collection of synthesized, multispectral data. Compared to TM, OLI showed obvious between-sensor differences in channel reflectance, especially over the near infrared (NIR) and shortwave infrared (SWIR) channels, and presented positive bias in vegetation spectral indices. OLI did not always outperform TM and ETM+ in classification, which related to the methods used. Furthermore, the channels over SWIR of TM and its successors contributed largely to enhancement of inter-class separability and to improvement of classification. Currently, the inclusion of MSS data is confronted with significant challenges regarding the consistency of surface mapping. Considering the inconsistency among the Landsat sensors, it is applicable to generate a consistent time series of spectral indices through proper transformation models. Meanwhile, it suggests the generation of specific class(es) based on interest instead of including all classes simultaneously.

In recent years, the compact polarimetric SAR (CP SAR) imaging mode has received much attention because of its advantages in swath width compared to the quad-polarization mode and in information of scattering targets compared to the linear dual-polarization mode, respectively. For object detection (e.g., ship, sea ice, and drilling platform) in maritime monitoring which has been widely discussed, it is still a challenge to eliminate the false alarms caused by ocean clutter. Accordingly, a modified feature-based framework for ship detection using CP SAR data was proposed in this paper. In particular, a Guard-filter was used after feature extraction to reduce the effect of ocean clutter. Several simulated CP SAR imageries based on Gaofen-3 quad-polarization SAR image were selected for further investigations on ship detection. A SVM classifier was included in the framework to do a pixel-wise classification. The result shows that the proposed method has a better performance compared to traditional methods (e.g., constant false alarm rate and polarimetric whitening filter) and the feature-based method without filtering. Overall false alarm rates are decreased by 35% with the proposed method.

In this paper, the impact of radiometric calibration on land surface temperature (LST) retrieval from a single thermal channel, particularly over urban areas, was investigated. To measure the impact, an error coefficient (EC) was defined as the ratio of LST error to radiometric calibration uncertainty. The EC was estimated based on a sensitivity model, with focusing on HJ-1B thermal channel. Generally, under a moderate condition, a 0.1 (W•m−2•sr−1•μm−1) uncertainty in radiometric calibration may result in an error of 1.1 K in LST retrieval. Further investigations on the spatial and temporal variations of EC were based on MODIS/Terra MOD11_C3 and MOD08_M3 as well as a yearly land cover product (MCD12_C1). In addition, discussions for urban areas were demonstrated. Major findings include: (1) the EC over urban areas located in low latitudes are larger than those in middle latitudes; (2) seasonal variation of the ECs over urban is visible, which is associated with the backgrounds of geography and climatology; (3) the EC differences between urban and non-urban vary spatially and temporally, which show significant contribution to LST retrieval, especially over the transition region of middle and low latitudes.