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The surface air temperature seasonal cycle in China has clearly changed in response to global warming, but its future projection has yet to be studied. Here, we investigate the performance and projection of the temperature seasonal cycle in China in 32 global climate models participating in the Coupled Model Intercomparison Project 6 (CMIP6). The characteristics of the temperature seasonal cycle, as represented by the amplitude and phase, are measured by the Fourier transform. The results show that the CMIP6 models can reasonably capture the observed typical temperature seasonal cycle feature with clear north–south gradients both for amplitude and phase in China. In the twenty‐first century, the models robustly project a delay in the phase across the whole country and a reduction in the amplitude in most of China relative to the reference period. Those changes are similar in the two shared socioeconomic pathway (SSP) scenarios, with overall larger magnitudes in the SSP5‐8.5 scenario than in the SSP2‐4.5 scenario. Projected amplitude changes are mainly linked to surface energy flux changes, especially latent heat flux changes. In response to future global warming, larger increases in the latent heat flux and thus stronger surface evaporative cooling lead to weaker surface warming in summer than in winter, contributing to a decrease in the amplitude of the temperature seasonal cycle in most regions of China. [ABSTRACT FROM AUTHOR]

Northern China is vigorously promoting cogeneration and clean heating technologies. The accurate prediction of building energy consumption is the basis for heating regulation. In this paper, the daily, weekly, and annual periods of building energy consumption are determined by Fourier transformation. Accordingly, a period-based neural network (PBNN) is proposed to predict building energy consumption. The main innovation of PBNN is the introduction of a new data structure, which is a time-discontinuous sliding window. The sliding window consists of the past 24 h, 24 h for the same period last week, and 24 h for the same period the previous year. When predicting the building energy consumption for the next 1 h, 12 h, and 24 h, the prediction errors of the PBNN are 2.30%, 3.47%, and 3.66% lower than those of the traditional sliding window PBNN (TSW-PBNN), respectively. The training time of PBNN is approximately half that of TSW-PBNN. The time-discontinuous sliding window reduces the energy consumption prediction error and neural network model training time. [ABSTRACT FROM AUTHOR]

The annual temperature cycle affects atmospheric circulation, biomass cycling, and human life. Its variation is influenced by surface solar radiation, which plays a key role in surface energy budgets. In this study, the Fourier transform method is used to analyse the spatial distribution and long‐term variations in temperature phase, amplitude, and baseline terms between 1960 and 2016 in China. Mean surface air temperature has a north–south gradient for phase, amplitude, and baseline. The regional mean phase values of the maximum, minimum, and mean temperatures in South China are larger than in North China, indicating that the timing of the annual cycle in Southern China is several days later than in Northern China. At the national scale, the annual maximum, minimum, and mean surface air temperature cycles are advancing, decreasing in range, and have a rising baseline. Linear regression is used to quantify the effect of surface solar radiation on temperature by removing its contribution on the raw maximum and mean temperatures. The surface solar radiation increases the phase of maximum and mean daily air temperatures, but reduces the amplitude and baseline. The surface solar radiation effect on maximum temperature is especially significant on the Yunnan‐Guizhou Plateau located in the south‐southwest. The phase of maximum temperature adjusted for solar radiation for the Plateau is similar to South China at the same latitude and later than the raw maximum temperature. [ABSTRACT FROM AUTHOR]

Terraces are important objects of soil and water conservation and land use surveys in the Loess Plateau and considerably impact soil erosion and agricultural production. Obtaining the spatial distribution of terraces has great significance for soil and water conservation research and government policy-making. A new method of terrace extraction was proposed in this study based on remote sensing images and digital elevation model (DEM) used as experimental data. The extraction method combines Fourier transform and digital terrain analysis. First, by analyzing the spatial and frequency domain characteristics of the terraced image, we determined the analysis window to be 50 × 50, whereas the area having a direction maximum energy value greater than 0.25 and image standard deviation greater than 2000 was considered as the terraced candidate. Then, the positive terrain was automatically extracted from the DEM based on the regional growth method, and the terraces on the positive terrain were obtained. After a series of morphological image processing methods were conducted, the final terraces were extracted. The results showed that the accuracy of drawing and user accuracy of terrace extraction were 79.0% and 73.5%, respectively. Compared with the object-oriented method, it was found that the proposed method was more reliable and accurate. This method developed in this study has the characteristics of simple operation, strong universality, and high precision and can be applied to soil and water conservation monitoring and land use surveys in the Loess Plateau. [ABSTRACT FROM AUTHOR]

The HJ-1A satellite was successfully launched on September 6, 2008. The inclusion of a HyperSpectral Imager (HSI) as one of the payloads of the HJ-1A Satellite is a major milestone in the field of the remote sensing in China. It is also the first Fourier transform imaging spectrometer routinely used to acquire scientific data from a satellite orbiting Earth. This paper briefly introduces the basic imaging theories of the spatially modulated Fourier transform imaging spectrometer, and then discusses the theoretical analysis and algorithms of spectrum reconstruction. Results of the operational spectrum reconstruction for the raw data of the HJ-1A satellite Fourier transform HSI are presented. At present, the algorithms and processing flow have been used successfully in the Ground Data Processing System (GDPS) built by the China Center for Resource Satellite Data and Applications (CRESDA). [ABSTRACT FROM AUTHOR]

Abstract: Crop identification is the basis of crop monitoring using remote sensing. Remote sensing the extent and distribution of individual crop types has proven useful to a wide range of users, including policy-makers, farmers, and scientists. Northern China is not merely the political, economic, and cultural centre of China, but also an important base for grain production. Its main grains are wheat, maize, and cotton. By employing the Fourier analysis method, we studied crop planting patterns in the Northern China plain. Then, using time-series EOS-MODIS NDVI data, we extracted the key parameters to discriminate crop types. The results showed that the estimated area and the statistics were correlated well at the county-level. Furthermore, there was little difference between the crop area estimated by the MODIS data and the statistics at province-level. Our study shows that the method we designed is promising for use in regional spatial scale crop mapping in Northern China using the MODIS NDVI time-series. [Copyright &y& Elsevier]