Near surface air temperature (NSAT) products are required for environment-related researches and applications. Existing NSAT products vary in spatial-temporal resolution and data quality. Thus, it is necessary to evaluate and investigate the difference of different NSAT products to provide an overall assessment to help researchers and users to choose and use among the many NSAT products. In this study, Tibetan Plateau was selected as our study area, and six released NSAT products were collected for comparison and evaluation. The NSAT products were compared with in situ NSAT from China Meteorological Administration stations (CMA) respectively. The evaluation process was conducted from daily and monthly scale and gave out the accuracy ranking of the six NSAT products.

Sea surface temperature (SST) is a vital parameter at the earth-atmosphere interface. Current method can derive an all-sky SST at resolution up to 6 km by integrating thermal infrared (TIR) and passive microwave (PMW) remote sensing. However, due to the swath gap of the polar-orbit PMW sensors, current TIR-PMW integrated SST are not spatial-seamless (i.e. all-sky available). By integrating GCOM-W1 AMSR2 and FengYun-3B MWRI observations, this paper fills the BT inside the swath gap to reconstruct a spatial-seamless PMW brightness temperature (BT) and then introduces Aqua MODIS SST to estimates a 4-km all-sky SST, the spatial resolution and coverage of which outperforms the current TIR-PMW integrated SST. Results show that the reconstructed BT highly agrees with the original BT and the estimated SST has accuracy of 0.67 K-0.72 K when validated against in-situ SST. This study would be beneficial for associated studies such as climate change on large scales.

A widely used approach for all-weather land surface temperature (LST) estimation is the integration of satellite passive microwave (MW) and thermal infrared (TIR) remote sensing observations. However, there are still few methods for estimating near-real time (NRT) all-weather (AW) (NRT-AW) LST. Besides, estimation of the LST within the swath gap of the satellite MW images is still greatly limited. This letter proposes a so-called NRT-AW method for the estimation of NRT-AW LST. NRT-AW firstly fills up the brightness temperatures (BT) inside the AMSR2 swath gap. Then, the NRT AW LST is estimated by learning the mapping between the time series of AMSR2 BT and MODIS LST on the annual scales. The results of the application of NRT-AW in the Heihe River Basin (HRB) show that the NRT-AW LST is spatially continuous and highly consistent with the original MODIS LST with a standard deviation (STD) of 1.27–1.77 K. Validation based onin situLST indicates that the NRT-AW LST estimate has a root mean square error (RMSE) of 2.46–4.62 K. This method is beneficial for rapid mapping of all-weather LST over large areas and, thus, can satisfy associated applications.

Unmanned aerial vehicle (UAV) thermal infrared (TIR) remote sensing is an important way to obtain land surface temperature (LST) with high spatial and temporal resolutions. Due to wide spectral response function (SRF) ranges of UAV thermal imagers, currently available LST retrieval methods suitable for satellite sensors may induce significant uncertainty when applied to UAV sensors. Despite that some methods have been proposed to retrieve LST from UAV remote sensing, studies considering the adverse effect caused by the SRF ranges are still rare. Here, we present a so-called Temperature Retrieval for UAV Broadband thermal imager data (TRUB) method to retrieve LST from UAV broadband thermal imager data. TRUB’s core includes two parts: 1) a simple lookup table (LUT) algorithm for reducing the uncertainty induced by the wide SRF ranges; and 2) models suitable for UAV remote sensing for estimating the atmospheric parameters. Validation from the Heihe River Basin shows that the LST retrieved by TRUB, of which the root mean square error (RMSE) and mean bias error (MBE) is 1.71 and −0.02 K, respectively, is highly consistent with thein situLST. TRUB is helpful to reduce the uncertainty caused by the wide SRF ranges of UAV thermal imagers and quantify the influence of atmosphere, thus can obtain UAV remote-sensing LST with better accuracy in large-area operating missions.

Bistatic forward-looking synthetic aperture radar (BFSAR) is a kind of bistatic SAR system that can image forward-looking terrain in the flight direction of the receiver. Current literature and reports about BFSAR mainly concentrate on the stationary scene and ground-moving target imaging. Unlike stationary and ground-moving targets, the translational and rotational movements of ship targets usually lead to complicated range cell migration (RCM) and Doppler frequency migration (DFM). Moreover, the characteristics of RCM and DFM for different scattering points of the ship target are significantly different, i.e., the characteristics of the RCM and DFM are 2-D spatial variation, ultimately leading to severe defocusing of ship target in the SAR image. To solve these problems, a kind of hybrid SAR-ISAR imaging formation is proposed for BFSAR ship target imaging. First, to solve the problem of the Doppler ambiguity caused by the forward-looking mode of the receiver, an efficient ambiguity estimation method based on the minimum entropy criterion is presented. Then, keystone transform and range alignment processing can be applied to correct the spatial variant range walk and higher order RCM, respectively. Moreover, in order to obtain a high-resolution and well-focused image after translational compensation, a new method based on the fractional Fourier transform (FrFT) and the Wigner–Ville distribution (WVD) is proposed, where FrFT is applied to separate the multiple main scattering points in each range cell, and WVD is applied to obtain the high-resolution time–frequency distribution of each scattering point. Compared with the conventional ISAR range-Doppler (RD) algorithm and time–frequency estimation-based imaging methods, this method not only has no cross terms but also has high processing accuracy and better antinoise performance.

The echo signal of slow-moving target is usually submerged in clutter signal. As a consequence, ground moving target (GMT) separation is challenging because of the decreasing of detection performance. To solve this problem, this paper proposes a moving target detection method with single-channel for bistatic forward-looking SAR (BFSAR) which mainly contains three steps. First, range cell migration (RCM) is corrected by Keystone transform. Next, Extended Nonlinear Chirp Signal (NLCS) algorithm is applied to equalize the spatial variant Doppler parameters and subpress clutter spectrum broadening. At last, according to the difference of Doppler FM rate between GMT and the stationary clutter, the Short Time Fourier Transform (STFT) is devoted to separate GMT from the stationary clutter. The effectiveness of the detection method proposed in this paper has been demonstrated and illustrated by numerical simulations.

Land surface temperature (LST) is an important parameter for series land surface processes, models and applications. The accuracy of LST directly influenced its application. Therefore, a reasonable validation method is meaningful to assess the accuracy of LST datasets. In this study, an in-situ observation representativeness assessment method was proposed. Based on this method, the JPSS VIIRS LST product was validated against in-situ LST at 7 ground sites over the Heihe River Basin during the HiWATER experiments period. Results show that about 70%, 28%, 43%, 68%, 42%, 35% and 25% of the FOV LST for ARS, DMS, DSL, EBO, HHL, JYL, and SDQ are able to well represent the corresponding LST of VIIRS pixels, respectively and determined the representativeness period of each site. The VIIRS LST has a high correlation with the in-situ LST with an accuracy of 2.30 K - 5.76 K at daytime and 1.26 K-2.68 K at nighttime, respectively.

Terrestrial laser scanning (TLS) is widely used in forest inventory surveys. Diameter at breast height (DBH) is one of the most important parameters in the forest inventory survey. There are many methods to estimate DBH. In this study, cylinder fitting algorithm, circle fitting algorithm and Hough transform algorithm are used to estimate DBH of two larches of different ages to find a better DBH extraction algorithm. Compared with the circle fitting algorithm and Hough transform algorithm, the cylinder fitting algorithm achieves the highest accuracy. In addition, it is worth noting that different structure of the trees may affect the accuracy of these methods greatly.

Land surface temperature (LST) is a key variable at the land-atmosphere boundary. For many research projects and applications an all-weather LST product at moderate spatial resolution (e.g., 1 km) would be highly useful, especially in frequently cloudy areas. Merging thermal infrared (TIR) and microwave (MW) observations is able to overcome shortcomings of single-source remote sensing to derive such an LST. However, in current merging methods, models adopted for downscaling MW LST fail to quantify the effect of temporal variation of LST. Thus, accuracy of the merged LST can be deteriorated and therefore remain a major impediment for these methods to be generalized over large areas. In this context, we propose a new practical method to merge TIR and MW observations from a perspective of decomposition of LST in temporal dimension. The physical basis of the method is decomposing LST into three temporal components: annual temperature cycle component, diurnal temperature cycle component prescribed by solar geometry, and weather temperature component driven by weather change. The method was applied to MODIS and AMSR-E/AMSR2 data to generate an 11-year record of 1-km all-weather LST over Northeast China: the resulting merged LST has an accuracy of 1.29-1.71 K when validated against in situ LST; besides, no obvious differences in accuracy of the merged LST were found between clear-sky and unclear-sky conditions. Furthermore, the proposed method outperforms the previous method in both accuracy and image quality, indicating its good capability to generate daily 1-km all-weather LST, which will benefit continuous monitoring of earth's surface temperature.

Images classification is an essential field in remote sensing community. However, a variety of target shapes, as well as changing conditions during multiple time periods and different areas usually result in shifts in classification. This problem can affect classification results seriously. Although the affection is significant in remote sensing classification, very few people have considered this issue, and have solved it. In this paper, we introduce the associative domain adaptation (ADA) method to address this challenge. We apply this algorithm to two public remote sensing datasets. One is famous UC Merced dataset; another is NWPU-RESISC45 dataset which has a much more variance within the class. We then build a classification model by using UC Merced training images and labels as well as using training images from NWPU-RESISC45. This semi-supervised classification performance achieves an impressive test accuracy on the NWPU-RESISC45 test dataset.

Land Surface Temperature (LST) is an important input parameter for many land surface models. The accuracy of satellite LST products directly affect its application; therefore, it is necessary to evaluate LST products. In this study, two satellite remotely sensed LST products, i.e. AMSR2 LST and MODIS LST, were evaluated against the in-situ LSTs at 17 ground sites in Heihe River Basin in 2014. Results show that both AMSR2 and MODIS LSTs have good correlations with the in-situ LST, with R2 from 0.80 to 0.98 except at AR2 site at daytime. However, both of these two products have large systematic errors compared with the in-situ LST. The possible main reason is the scale mismatch between the FOV of the longwave radiometer and the AMSR2 and MODIS pixels.

Traditional 3D map reconstruction methods based on unmanned aerial (UA) always relies on multi-camera optical equipment or additional space positioning equipment. All of these restrict the UA's application scenarios to some extent. Visual simultaneous localization and mapping (VSLAM), using the camera as the only external sensor, can construct a 3D map of its spatial environment while recording its own localization. This paper proposes a technique of fast 3D map reconstruction based on UAs by using VSLAM, with parallel computing based on CUDA, to achieve fast dense reconstruction based on a UA platform. This research belongs to one of the emerging but very exciting application areas using UAs. It helps to expand the application scope of UAs to some extent. From the experiments, it is demonstrated that the methodology presented in this paper works well, and could be applied to real world applications.

Fuel moisture content (FMC) is a critical factor in assessing wildfire risk and its behaviour. Traditional field measurement of this variable is time-consuming and is impossible to extend to large-scale and dynamic applications. The canopy water has strong absorption characteristic in near and shortwave infrared spectra, allowing the near-real-time, multi-temporal and -spatial estimation of the FMC from remotely sensed data available. During last decade, numerous statistic- or physical model-based studies were carried out for the estimation of this variable. As FMC is responsive to weather variations, diurnal determination of this variable is essential for wildfire early-warning. With the launch of Himawari-8 in 2014, 10 mins images are available from this satellite, making real-time retrieval of the FMC achievable. Thus, this is the first study to retrieve diurnal FMC from Himawari-8 images, with the purpose for real-time wildfire risk assessment in near future.

Land surface temperature (LST) immensely affects the energy balance and water cycle on the earth's surface. Merging thermal infrared (TIR) and passive microwave (MW) remote sensing provides the possibility to obtain all-weather LST with moderate resolutions. However, due to difficulties in downscaling MW LST, current methods merging TIR LST and MW LST into such an all-weather LST are limited over large areas with very complicated land surfaces (e.g. the Tibetan Plateau). By fully considering the influence of the topography on estimation of merged LSTs, this study revises the recently-developed physical method for generating the 1-km all-weather LST and applies it over the Tibetan Plateau to merge MODIS (1 km) and AMSR2 (10 km) observations. Results show that the merged LST has accuracy of 0.99 K-3.22 K when validated against insitu LSTs from five ground stations with various land cover types. This study would be beneficial for continuously monitoring LST and improving spatio-temporal resolutions for associated land surface process studies requiring high-quality all-weather LST over large scales.

Terrestrial laser scanning (TLS) can acquire high-precision point cloud data within a short time span and has received a lot of attention in the research on forest resource inventory. At present, most of the methods for estimating tree parameters are based on the point cloud of the single tree. This study obtains forest parameters based on the plot-level TLS data. Larch trees in the plot are detected and tree height, diameter at breast height (DBH) and crown projection area of single larch are estimated. The results show that the trees detection accuracy of each plot is relatively high in this study, DBH and tree height can be estimated with relatively higher accuracies with R2values of 0.949 and 0.77, respectively, and root mean squared error (RMSE) value of 2.98 cm and 1.5897 m, respectively. Our results also show that the estimation accuracies of the forest parameters based on the plot-level are similar to that based on the single-level. The proposed method in this study performs much better than the conventional single-level methods in workload and automation.

Land surface temperature (LST) is widely used in various applications, such as ecology, meteorology and climatology. Compared to satellite thermal infrared (TIR) remote sensing, passive microwave (PMW) remote sensing has ability to overcome the influence of atmosphere and thus has potential to estimate LST under cloudy conditions. However, the coarse spatial resolution significantly limits the application of PMW remote sensing in LST estimation. In this study, a simple multiple regression approach is proposed to downscale the LST from AMSR2 (10 km) to MODIS (1 km) scale in a different way from current downscaling methods. The method is applied to northeast China and the results shows an accuracy of 2-3 K for this method. This study is meaningful for generation of all-weather LST with high spatial resolution from PMW observations at regional and global scales.

Land surface temperature (LST) is an important parameter in many research fields. Compared to the thermal infrared (TIR) remote sensing, passive microwave (MW) remote sensing can better overcome the atmospheric influences and has advantages in LST estimation. However, there are still many problems in estimating LST by MW: traditional empirical methods mainly rely on the statistic relation; therefore, their accuracies are generally limited; physical methods are not suitable for wide applications because they need to be constructed based on complicated surface cases. Based on the optimal time-window fitting the MW radiation transfer (RT) equation, this paper facilitates the semi-empirical method to estimate LST over the Chinese landmass from the Advanced Microwave Scanning Radiometer-2 (AMSR2) data. The results show that the method has higher accuracy than the traditional semi-empirical method. The study is beneficial for estimating LSTs in cloudy conditions and merging the TIR and MW LST in difference spatial scales.

Satellite passive microwave (MW) remote sensing has a better ability to observe land surface temperature (LST) in cloudy conditions than thermal infrared (TIR) remote sensing. Due to the much greater thermal sampling depth (TSD) of MW, currently available MW LST do not represent the thermodynamic temperature of the land surface and, therefore, yield systematic differences from TIR LST. The TSD effect is particularly prominent over barren land and sparsely vegetated surfaces. Here, we present a novel TSD correction (TSDC) method to estimate the MW LST over barren land. The core of this method is a new formulation of the passive MW radiation balance equation, which allows linking MW effective physical temperature to the soil temperature at a specific depth. The TSDC method is applied to the 6.9-GHz channel of AMSR-E in northwestern China-western Mongolia and western Namibia (WN). Evaluation shows that LST estimated by the TSDC method agrees well with the MODIS LST. Validation based on in situ LSTs measured at the Gobabeb site in WN demonstrates the high accuracy of the TSDC method: it yields a root mean squared error of about 2-3 K and slight systematic error. In contrast, other methods without TSDC yield lower accuracies and significantly underestimate LST. Therefore, the TSDC method has the potential to generate MW LST with the same physical meaning and similar accuracy as TIR LST. This study provides implications for developing practical and accurate methods to estimate MW LST over other land surface types and at the global scale.

With the rapid development of new satellite thermal sensors and applications of land surface temperature (LST), research on finding effective algorithms to retrieve accurate LST from satellite thermal infrared (TIR) data is becoming more and more important. In this study, multiple algorithms for retrieving LST from Landsat-8 Thermal Infrared Sensor (TIRS) data are validated and intercompared in an extremely arid region, Northwest China. According to the validation and intercomparison, we find that the radiative transfer equation (RTE) based method with TIRS band 1 (10.60-11.19 μm) has the highest accuracy, while the single-channel (SC) method using TIRS band 2 (11.50-12.51 μm) yielded the lowest accuracy. The accuracies of split-window (SW) algorithms are slightly lower than the RTE based method. However, the SW algorithms have better applicability than the RTE based method. The most suitable SW algorithm for Landsat-8 TIRS data in the study area is recommended. This study will be beneficial for developing the LST product from Landsat-8 data for the study area.