In this review article we present an up-to-date progress report of the connection between long-duration (and their various sub-classes) gamma-ray bursts (GRBs) and their accompanying supernovae (SNe). The analysis presented here is from the point of view of an observer, with much of the emphasis placed on how observations, and the modelling of observations, have constrained what we known about GRB-SNe. We discuss their photometric and spectroscopic properties, their role as cosmological probes, including their measured luminosity−decline relationships, and how they can be used to measure the Hubble constant. We present a statistical analysis of their bolometric properties, and use this to determine the properties of the "average" GRB-SN: which has a kinetic energy of E K ≈ 2.5 × 10 52 erg (σ E K = 1.8 × 10 52 erg), an ejecta mass of M ej ≈ 6 M (σ M ej = 4 M ), a nickel mass of M Ni ≈ 0.4 M (σ M Ni = 0.2 M ), an ejecta velocity at peak light of v ≈ 20, 000 km s −1 (σ v ph = 8, 000 km s −1 ), a peak bolometric luminosity of L p ≈ 1×10 43 erg s −1 (σ Lp = 0.4×10 43 erg s −1 ), and it reaches peak bolometric light in t p ≈ 13 days (σ tp = 2.7 days). We discuss their geometry, as constrained from observations, and consider the various physical processes that are thought to power the luminosity of GRB-SNe, and whether differences exist between GRB-SNe and the SNe associated with ultra-long duration GRBs such as GRB 111209A/SN 2011kl. We discuss how observations of the environments of GRB-SNe further constrain the physical properties of their pre-explosion progenitor stars, and give a brief overview of the current theoretical paradigms of the central engines that produce the various types of GRB-SNe. Furthermore, we present an overview of the r-process, radioactively powered transients that have been photometrically associated with short-duration GRBs, and we conclude the review by discussing what additional research is needed to further our understanding of GRB-SNe, in particular the role of binary-formation channels and the connection of GRB-SNe with superluminous SNe. arXiv:1604.03549v2 [astro-ph.HE] 18 Jul 2016 1 A: Strong spectroscopic evidence. B: A clear light curve bump as well as some spectroscopic evidence resembling a GRB-SN. C: A clear bump consistent with other GRB-SNe at the spectroscopic redshift of the GRB. D: A bump, but the inferred SN properties are not fully consistent with other GRB-SNe or the bump was not well sampled or there is no spectroscopic redshift of the GRB. E: A bump, either of low significance or inconsistent with other GRB-SNe. * Denotes exact, K-corrected rest-frame filter observable. ‡ Values fixed during fit.k ands denote the filter-averaged luminosity (k) and stretch (s) factors relative to SN 1998bw.

The accurate assessment of rice yield is crucially important for China’s food security and sustainable development. Remote sensing (RS), as an emerging technology, is expected to be useful for rice yield estimation especially at regional scales. With the development of unmanned aerial vehicles (UAVs), a novel approach for RS has been provided, and it is possible to acquire high spatio-temporal resolution imagery on a regional scale. Previous reports have shown that the predictive ability of vegetation index (VI) decreased under the influence of panicle emergence during the later stages of rice growth. In this study, a new approach which integrated UAV-based VI and abundance information obtained from spectral mixture analysis (SMA) was established to improve the estimation accuracy of rice yield at heading stage. The six-band image of all studied rice plots was collected by a camera system mounted on an UAV at booting stage and heading stage respectively. And the corresponding ground measured data was also acquired at the same time. The relationship of several widely-used VIs and Rice Yield was tested at these two stages and a relatively weaker correlation between VI and yield was found at heading stage. In order to improve the estimation accuracy of rice yield at heading stage, the plot-level abundance of panicle, leaf and soil, indicating the fraction of different components within the plot, was derived from SMA on the six-band image and in situ endmember spectra collected for different components. The results showed that VI incorporated with abundance information exhibited a better predictive ability for yield than VI alone. And the product of VI and the difference of leaf abundance and panicle abundance was the most accurate index to reliably estimate yield for rice under different nitrogen treatments at heading stage with the coefficient of determination reaching 0.6 and estimation error below 10%.

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Broad-lined type Ic supernovae (SNe Ic-BL) are a subclass of rare core collapse SNe whose energy source is debated in the literature. Recently a series of investigations on SNe Ic-BL with the magnetar (plus 56 Ni) model were carried out. Evidence for magnetar formation was found for the well-observed SNe Ic-BL 1998bw and 2002ap. In this paper we systematically study a large sample of SNe Ic-BL not associated with gamma-ray bursts. We use photospheric velocity data determined in a homogeneous way. We find that the magnetar+ 56 Ni model provides a good description of the light curves and velocity evolution of our sample of SNe Ic-BL, although some SNe (not all) can also be described by the pure-magnetar model or by the two-component pure-56 Ni model (3 out of 12 are unlikely explained by two-component model). In the magnetar+ 56 Ni model, the amount of 56 Ni required to explain their luminosity is significantly reduced, and the derived initial explosion energy is, in general, in accordance with neutrino heating. Some correlations between different physical parameters are evaluated and their implications regarding magnetic field amplification and the total energy reservoir are discussed.

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In this paper, we investigate two hydrogen-poor superluminous supernovae (SLSNe) iPTF15esb and iPTF13dcc whose light curves (LCs) show significant deviation from the smooth rise and fall. The LC of iPTF15esb exhibits two peaks and a post-peak plateau, and furthermore the late-time spectrum of iPTF15esb shows a strong, broad Hα emission line. The early-time LC of iPTF13dcc shows a long duration bump followed by the second peak. Here we propose an ejectacircumstellar medium (CSM) interaction model involving multiple shells/winds and use it to explain the LCs of iPTF15esb and iPTF13dcc. We find that the theoretical LCs reproduced by this model can well match the observations of iPTF15esb and iPTF13dcc. Based on our results, we infer that the progenitors have undergone multiple violent mass-loss processes before the SN explosion. In addition, we find that the variation trend of our inferred densities of the shells is consistent with that predicted by the stellar mass-loss history before an SN explosion. Further investigations for other bumpy SLSNe/SNe would shed light on their nature and provide a probe for the mass-loss history of their progenitors.

High-precision maps of soil organic carbon (SOC) are beneficial for managing soil fertility and understanding the global carbon cycle. Digital soil mapping plays an important role in efficiently obtaining the spatial distribution of SOC, which contributes to precision agriculture. However, traditional soil-forming factors (i.e., terrain or climatic factors) have weak variability in low-relief areas, such as plains, and cannot reflect the spatial variation of soil attributes. Meanwhile, vegetation cover hinders the acquisition of the direct information of farmland soil. Thus, useful environmental variables should be utilized for SOC prediction and the digital mapping of such areas. SOC has an important effect on crop growth status, and remote sensing data can record the apparent spectral characteristics of crops. The normalized difference vegetation index (NDVI) is an important index reflecting crop growth and biomass. This study used NDVI time series data rather than traditional soil-forming factors to map SOC. Honghu City, located in the middle of the Jianghan Plain, was selected as the study region, and the NDVI time series data extracted from Landsat 8 were used as the auxiliary variables. SOC maps were estimated through stepwise linear regression (SLR), partial least squares regression (PLSR), support vector machine (SVM), and artificial neural network (ANN). Ordinary kriging (OK) was used as the reference model, while root mean square error of prediction (RMSE P ) and coefficient of determination of prediction (R 2 P ) were used to evaluate the model performance. Results showed that SOC had a significant positive correlation in July and August (0.17, 0.29) and a significant negative correlation in January, April, and December (−0.23, −0.27, and −0.23) with NDVI time series data. The best model for SOC prediction was generated by ANN, with the lowest RMSE P of 3.718 and highest R 2 P of 0.391, followed by SVM (RMSE P = 3.753, R 2 P = 0.361) and PLSR (RMSE P = 4.087, R 2 P = 0.283). The SLR model was the worst model, with the lowest R 2 P of 0.281 and highest RMSE P of 3.930. ANN and SVM were better than OK (RMSE P = 3.727, R 2 P = 0.372), whereas PLSR and SLR were worse than OK. Moreover, the prediction results using single-data NDVI or short time series NDVI showed low accuracy. The effect of the terrain factor on SOC prediction represented unsatisfactory results. All these results indicated that the NDVI time series data can be used for SOC mapping in plain areas and that the ANN model can maximally extract additional associated information between NDVI time series data and SOC. This study presented an effective method to overcome the selection of auxiliary variables for digital soil mapping in plain areas when the soil was covered with vegetation. This finding indicated that the time series characteristics of NDVI were conducive for predicting SOC in plains.