Tar ‘specks’ in the North Atlantic

A number of dark specks retained on filters, through which seawater had been filtered, contained material that absorbed UV light very strongly. This material was most likely sunken pieces of tar. The amount was 0.12 μg/l on an average and was independent of hydrographic parameters.     

Evaluation of machine learning models for rice dry biomass estimation and mapping using quad-source optical imagery

ABSTRACT

Several machine learning regression models have been advanced for the estimation of crop biophysical parameters with optical satellite imagery. However, literature on the comparative performances of such models is still limited in range and scope, especially under multiple data sources, despite the potential of multi-source imagery to improving crop monitoring in cloudy areas. To fill in this knowledge gap, this study explored the synergistic use of Landsat-8, Sentinel-2A, China’s environment and disaster monitoring and forecasting satellites (HJ-1 A and B) and Gaofen-1 (GF-1) data to evaluate four machine learning regression models that include Random Forest (RF), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), and Gradient Boosting Decision Tree (GBDT), for rice dry biomass estimation and mapping. Taking a major rice cultivation area in southeast China as case study during the 2016 and 2017 growing seasons, a cross-calibrated time series of the Enhanced Vegetation Index (EVI) was obtained from the quad-source optical imagery and on which the aforementioned models were applied, respectively. Results indicate that in the before rice heading scenario, the most accurate dry biomass estimates were obtained by the GBDT model (R² of 0.82 and RMSE of 191.8 g/m²) followed by the RF model (R² of 0.79 and RMSE of 197.8 g/m²). After heading, the k-NN model performed best (R² of 0.43 and RMSE of 452.1 g/m²) followed by the RF model (R² of 0.42 and RMSE of 464.7 g/m²). Whist the k-NN model performed least in the before heading scenario, SVM performed least in the after heading scenario. These findings may suggest that machine learning regression models based on an ensemble of decision trees (RF and GBDT) are more suitable for the estimation of rice dry biomass, at least with optical satellite imagery. Studies that would extend the evaluation of these machine learning models, to other parameters like leaf area index, and to microwave imagery, are hereby recommended.     

Seasonal variations between sampling and classical mean turbulent heat flux estimates in the eastern North Atlantic

The two commonly used statistical measures of the air-sea heat flux, the sampling and classical means, have been compared using hourly reports over a 7-year-period from a weather ship stationed in the NE Atlantic. The sampling mean is the average over all flux estimates in a given period, where individual flux estimates are determined from ship reports of meteorological variables using the well-known bulk formulae. The classical mean is the flux derived by substituting period-averaged values for each of the meteorological variables into the bulk formula (where the averaging period employed is the same as that over which the fluxes are to be determined). Monthly sampling and classical means are calculated for the latent and sensible heat fluxes. The monthly classical mean latent heat flux is found to overestimate the sampling mean by an amount which increases from 1–2 W m⁻² in summer to 7 W m⁻² in winter, on average, over the 7-year-period. In a given winter month, the excess may be as great as 15 W m⁻², which represents about 10% of the latent heat flux. For the sensible heat flux, any seasonal variation between the two means is of the order of 1 W m⁻² and is not significant compared to the interannual variation. The discrepancy between the two means for the latent heat flux is shown to arise primarily from a negative correlation between the wind speed and sea-air humidity difference, the effects of which are implicitly included in the sampling method but not in the classical. The influence of the dominant weather conditions on the sign and magnitude of this correlation are explored, and the large negative values that it takes in winter are found to depend on the typical track of the mid-latitude depressions with respect to the position sampled. In conclusion, it is suggested that sampling means should be employed where possible in future climatological studies.     

Measuring Earth orientation with the global positioning system

Summary A globally distributed network of high-precision receivers which obtain data from the full Global Positioning System (GPS) configuration of 18 or more satellites may soon become an efficient and economical method for the rapid determination of short-term variations in Earth orientation. A covariance analysis has been performed to evaluate the errors associated with GPS monitoring of Earth orientation. Earth orientation parameters were modeled either as constants over observing windows of various lengths, or as stochastic process-noise variables. The sensitivity of Earth orientation estimates to systematic errors in selected model parameters was also examined. GPS measurements appear to be highly competitive with those from other techniques, and have the potential to generate nearly continuous centimeter-level Earth orientation information to aid both spacecraft navigation and the study of high-frequency Earth orientation-related processes.     

Mid-Holocene evolution of a tidal basin in the western Netherlands: a model for future changes in the northern Netherlands under conditions of accelerated sea-level rise?

A scenario for the future development of the Dutch Wadden Sea is derived from an evolutionary model for tidal basins during a rise in sea level. The model is based on the evolution of the Atlantic/Subboreal Holland tidal basin, between 7000 BP and 3500 BP. It emphasizes the balance between the storage capacity created by a sea-level rise and the amount of sediment available.

If the rate of relative sea-level rise exceeds the rate of sediment supply, the innermost (central) portions of the basin will not receive sufficient sediment for an intertidal morphology to be preserved. Eventually, sand will be deposited only in tidal channels and in the flood-tidal delta through which the sediment is supplied, mud deposition will occur in the interchannel areas and salt marshes will disappear.     

变形模型可区分度量

在监测网设计阶段，变形体的变形特性一般不是完全清楚。因此，设计的监测方案应当能有效地和足够灵敏地区分几种可能的模型。本文研究了模型的可区分度量，所用的方法具有思路明确、清晰等特点，特别适用于在设计阶段设置指标。文中通过一个模拟网的分析和讨论，说明采用模型可区分度量作为设计准则的必要性。分析的结果还和Forstner法所得的结果进行初步的比较。     

Spatial and temporal variations of sound in an Andean City: Cajamarca,Peru

This study provides a preliminary examination of the temporal and geographic variations of sound in Cajamarca, a provincial city in the Andes of northern Peru. Sound levels were sampled in four neighborhoods of the city each representing different land-use characteristics and hypothetically distinct environmental sound profiles. The sample sites included the central plaza, the business district, a residential neighborhood, and a small residential community on the urban periphery. Sound readings were taken each hour between 6 am and 12 pm on every day of the week. Sound profiles for each location for a weekday, a Saturday, and a Sunday are presented. Distinct decibel profiles emerge, both geographically and temporally. These changing sound patterns reflect the activities of the town's residents as they move between home, work, and social/recreational activities during the course of a week. Cajamarca's sound profiles are believed to be characteristic of other provincial cities throughout the Andean region.