Rice is one of the most important crops in the world; meanwhile, the rice field is also an important contributor to greenhouse gas methane emission. Therefore, it is important to get an accurate estimation of rice acreage for both food production and climate change related studies. The eastern plain region is one of the major single-cropped rice (SCR) growing areas in China. Subjected to the topography and intensified human activities, the rice fields are generally fragmented and irregular. How remote sensing can meet this challenge to accurately estimate the acreage of the rice in this region using medium-resolution imagery is the topic of this study. In this study, the applicability of the Chinese HJ-1A/B satellites and a two-band enhanced vegetation index (EVI2) was investigated. Field campaigns were carried out during the rice growing season and ground-truth data were collected for classification accuracy assessments in 2012. A stepwise classification strategy utilizing the EVI2 signatures during key phenology stages, i.e., the transplanting and the vegetative to reproductive transition phases, of the SCR was proposed, and the overall classification accuracy was 91.7%. The influence of the mixed pixel and boundary effects to classification accuracy was also investigated. This work demonstrates that the Chinese HJ-1A/B data are suitable data source to estimating SCR cropping area under complex land cover composition.

Wind energy, as a vital renewable energy source, also plays a significant role in reducing carbon emissions and mitigating climate change. It is therefore of utmost necessity to evaluate ocean wind energy resources for electricity generation and environmental management. Ocean wind distribution around the globe can be obtained from satellite observations to compensate for limited in situ measurements. However, previous studies have largely ignored uncertainties in ocean wind energy resources assessment with multiple satellite data. It is against this background that the current study compares mean wind speeds (MWS) and wind power densities (WPD) retrieved from scatterometers (QuikSCAT, ASCAT) and radiometers (WindSAT) and their different combinations with National Data Buoy Center (NDBC) buoy measurements at heights of 10 m and 100 m (wind turbine hub height) above sea level. Our results show an improvement in the accuracy of wind resources estimation with the use of multiple satellite observations. This has implications for the acquisition of reliable data on ocean wind energy in support of management policies.

Wind resources assessment plays a significant role in site selection for the construction of offshore wind farms. Mean wind speeds (MWS), wind power densities (WPD), and Weibull parameters are the most important variables for wind resources assessment. These variables were estimated with the synergetic use of multiple satellite data (QuikSCAT + WindSAT + ASCAT) and meteorological data from coastal stations using spatial interpolation methods, including inverse distance weighting (IDW), ordinary kriging (OK), and ordinary co-kriging (OCK). The spatial variability of offshore wind energy resources over the China Sea is assessed at heights of 10 m and 100 m (hub height of wind turbine). Then, 8 buoy measurements were used to evaluate the accuracy of the offshore wind resources assessment. Our results show that combining multiple satellite data and coastal meteorological data improves the accuracy of wind resources assessment in the offshore areas and the OCK method show the best performance for accuracy in most cases. The statistical results comparing buoy-derived MWS and interpolated MWS show a root mean square error (RMSE) of 0.17 m/s and correlation coefficient (Corr.) of 0.987 at a height of 10 m. Statistics of the comparison between buoy-derived WPD and interpolated WPD by OCK show a RMSE of 23.38 W/m² at a height of 10 m. The results show that the highest wind resources are mainly found in the Taiwan Strait and offshore regions in Fujian province.

High-spatial-resolution wind fields derived from synthetic aperture radar (SAR) instruments are crucial to a wide range of applications. However, the spatial scale effect on wind speed retrieval accuracy has seldom been reported. For the purpose of understanding this issue, this letter makes a quality assessment of wind speed retrieval accuracy based on four commonly used C-band geophysical model functions (CMOD4, CMOD-IFR2, CMOD5, and CMOD5.N) at spatial resolutions ranging from 50 m to 50 km using Sentinel-1 interferometric wide (IW) swath mode images. Our results show that the CMOD5 function is the most effective among these functions, owing to a low root-mean-square error (RMSE) of 1.17 m/s and a bias of -0.28 m/s for wind speed retrieval at a spatial resolution of 500 m. It is further observed that the variance of wind speeds retrieved from copolarized SAR images decreases exponentially with the decrease of spatial resolutions. For Sentinel-1 IW mode images, the variance of wind speeds retrieved with CMOD5 decreases rapidly from 50 to 500 m, with a drop in RMSE of 40%, and thereafter levels off. Thus, a spatial resolution of 500 m, with the CMOD5 function, is recommended optimal in this letter, for wind speed retrieval using Sentinnel-1 IW mode data.