The new Ku-band scatterometer (HSCAT-B) onboard the HY-2B satellite was launched on October 25, 2018, and soon after the C-band scatterometer (Advanced Scatterometer (ASCAT)-C) onboard the MetOp-C satellite was launched on November 6, 2018. This article aims to validate the new sea surface wind products from them, and also to summarize the common issues in current scatterometer wind products. Thus, other scatterometer data are also used for comparisons, including the C-band MetOp-B/ASCAT, and Ku-band SCATSAT-1/OSCAT2 and HY-2A/SCAT winds. In this study, the C-band and Ku-band scatterometer wind products were each reproduced using the same procedures, in terms of backscatter calibration, wind retrieval, and quality control. The scatterometer winds are compared to the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 winds or buoy winds, and the results show that the quality of ASCAT-C winds is almost the same as the well-known ASCAT-B; the HSCAT-B winds show quite good quality and similar validating statistics as ASCAT winds. Noticeable wind-speed-dependent biases are found in all Ku-band scatterometer winds, which suggests that refinements are needed for the NSCAT-4 geophysical model function, especially in terms of wind speed dependence for all incidence angles.

The HY-2B scatterometer (HSCAT-B) has now completed a one-year orbit. The instrument was declared operational after six months of in-orbit testing. The ocean wind field products are routinely being made available to the global science community. The HY-2B will work with the HY-2A to provide the Ku-band sea surface observations on a long-term scale, and this collaboration is expected to be important for disaster prevention and mitigation, as well as globally focused climate change research. However, to obtain reasonable evaluations of the system's functional, performance, and instrument stability results, the temperature, internal calibration parameters, attitude angles, and normalized radar cross section (NRCS) of the instrument were rigorously analyzed during the in-orbit tests. This study provided an overview of the six-month in-orbit testing process results. The temperature levels of some important parts of the scatterometer were found to meet the design requirements. The internal calibration and noise power had shown similar trends and were both within the normal range. The attitude angles, which mainly included the roll and pitch, had displayed variations within a very small scope. The evaluation results of the Amazon rainforest observations indicated that the scatterometer instrument had met all the design requirements and had established a good database for geophysical processing.

The Chinese HY-2D satellite was launched on 19 May 2021, carrying a Ku-band scatterometer. Together with the operating scatterometers onboard the HY-2B and HY-2C satellites, the HY-2 series scatterometer constellation was built, constituting different satellite orbits and hence opportunity for mutual intercomparison and intercalibration. To achieve intercalibration of backscatter measurements for these scatterometers, this study presents and performs three methods including: (1) direct comparison using collocated measurements, in which the nonlinear calibrations can also be derived; (2) intercalibration over the Amazon rainforest; (3) and the double-difference technique based on backscatter simulations over the global oceans, in which a geophysical model function and numerical weather prediction (NWP) model winds are needed. The results obtained using the three methods are comparable, i.e., the differences among them are within 0.1 dB. The intercalibration results are validated by comparing the HY-2 series scatterometer wind speeds with NWP model wind speeds. The curves of wind speed bias for the HY-2 series scatterometers are quite similar, particularly in wind speeds ranging from 4 to 20 m/s. Based on the well-intercalibrated backscatter measurements, consistent sea surface wind products from HY-2 series scatterometers can be produced, and greatly benefit data applications.

Rice false smut (RFS) is a late-onset fungal disease that primarily affects rice panicle in recent years. Severe RFS can decrease the yield by 20–30% and severely affect rice quality. This research used hyperspectral remote sensing data from unmanned aerial vehicles (UAV). On the basis of genetic algorithm combined with partial least squares to select the feature bands, this paper creates a new method to use the Pearson correlation coefficient method and Instability Index between Classes (ISIC) method to further select characteristic bands, which further eliminated 27.78% of the feature bands when the model monitoring accuracy was improved overall. The prediction accuracy of the Gradient Boosting Decision Tree model and Random Forest model was the best, which were 85.62% and 84.10%, respectively, and the monitoring accuracy was improved by 2.22% and 2.4% compared with that before optimization. Then, based on the UAV hyperspectral data and the combination of characteristic bands selected by the three band optimization methods, the sensitive band ranges of rice false smut monitoring were determined, which were 698–800 nm and 974–997 nm. This paper provides an effective method of selecting characteristic bands of hyperspectral data and a method of monitoring crop diseases’ using unmanned aerial vehicles.

The Chinese HY-2A satellite microwave scatterometer (HSCAT) is designed to measure the ocean surface wind vector with a large observation swath. HSCAT is a Ku-band scatterometer that has continuously observing the earth's surface for more than 6 years. Approximately, 30,000 orbit revolutions of scientific data are archived in its ground application system. To further understand the characteristics of the instrument, deeper analyses of these scientific data must be carried out. Currently, the geolocation accuracy of the normalized radar cross section (NRCS) has yet to be documented. However, geolocation errors can cause significant measurement bias, and have large impacts on the calibration and validation results. In this paper, the coastline crossing detection method is used to evaluate the geolocation errors of the NRCS data. Some coastal areas and islands where the NRCS data have large gradients are selected for the geolocation error analysis. The coastline inflection points can be identified on the basis that the radar backscatter of land and water exhibits a significant difference when the ocean surface wind speed is relatively low. The longitudes and latitudes of these points from different time periods are then fit to the global self-consistent hierarchical high-resolution shorelines map. The comparison results show that the overall distance error of HSCAT is approximately 2.59 km. This geolocation accuracy meets the instrument design requirements. The sources of geolocation errors are also analyzed in this paper in order to improve the geolocation accuracy.