This study first quantifies the virga precipitation occurrence percentage using three spaceborne radar observations, including Tropical Rainfall Measuring Mission Precipitation Radar (PR), Global Precipitation Measurement dual-frequency PR (Ku-band PR/Ka-band PR [KuPR/KaPR]), and CloudSat Cloud Profiling Radar (CPR). PR and KuPR/KaPR show that virga occurrence percentage is over 30% in arid regions (e.g., Sahara desert and deserts of Australia). CPR reveals similar virga geospatial distribution. However, the virga percentage based on CPR is about twice as large as that based on PR and KuPR/KaPR due to better detection sensitivity. Results also show that the majority of the virga clouds are altostratus and cirrus. Second, we investigate the virga precipitation contribution to the passive microwave radiometer false precipitation detection. Virga precipitation accounts for as much as 50% (30%) of Tropical Rainfall Measuring Mission Microwave Imager (Global Precipitation Measurement Microwave Imager) false detection results in arid regions. The underlying reason is because precipitation detection over land primarily relies on the ice scattering signature, while virga and light precipitation (e.g., 1 mm/hr) have similar amounts of ice water path in arid regions. Plain Language SummaryVirga precipitation is defined as the "wisps or streaks of water or ice particles falling out of a cloud but vaporizing before reaching the earth's surface as precipitation." We show the global virga precipitation occurrence percentage using three spaceborne radar observations. Virga precipitation occurrence percentage is over 30% in arid regions (e.g., Sahara desert, Arabian Peninsula, and deserts of Australia), with the largest values over the Sahara desert. In addition, the virga precipitation is often misidentified as the precipitation on the ground by passive microwave radiometer, which is a key instrument to map the global precipitation. We show that virga precipitation accounts for as much as 50% of the false precipitation detection results in the arid regions.

The timely and accurate acquisition of winter wheat acreage is crucial for food security. This study investigated the feasibility of extracting the spatial distribution map of winter wheat in Henan Province by using synthetic aperture radar (SAR, Sentinel-1A) and optical (Sentinel-2) images. Firstly, the SAR images were aggregated based on the growth period of winter wheat, and the optical images were aggregated based on the moderate resolution imaging spectroradiometer normalized difference vegetation index (MODIS-NDVI) curve. Then, five spectral features, two polarization features, and four texture features were selected as feature variables. Finally, the Google Earth Engine (GEE) cloud platform was employed to extract winter wheat acreage through the random forest (RF) algorithm. The results show that: (1) aggregated images based on the growth period of winter wheat and sensor characteristics can improve the mapping accuracy and efficiency; (2) the extraction accuracy of using only SAR images was improved with the accumulation of growth period. The extraction accuracy of using the SAR images in the full growth period reached 80.1%; and (3) the identification effect of integrated images was relatively good, which makes up for the shortcomings of SAR and optical images and improves the extraction accuracy of winter wheat.

The recent advancement in remote sensing technologies has resulted in the availability of different imaging modes and higher resolution satellite images. Accessibility of these remote sensing or satellite images, automatic ship detection and tracking has become an important research topic in the field of maritime surveillance. In this paper, a novel method for ship detection using satellite images is proposed. First the preprocessing is carried out to remove the noise from the images using Ship Detection and Tracking (SDT) filter. Then, the land masking (sea-land area separation) and cloud masking is carried out based on the gradient feature extraction using SDT edge detection, along with SDT segmentation. Finally, the ships are identified using the Machine Learning (ML) classifiers like Support Vector Machine (SVM), Random Forest Classifier (RFC), Linear Discriminant Analysis (LDA), Logistic Regression (LR), KNN, and Gaussian Naïve Bayes-based classifier based on the features extracted from Histogram of Oriented Gradients (HOG). The proposed work is cross validated using the Google earth data. Performance of our proposed method is evaluated using the recall and the precision values. Further, for tracking ships, an improved multiple hypothesis tracking (MHT) algorithm is proposed and tested using the Kaggle dataset.

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