Carbon emissions and consequent climate change directly affect the sustainable development of ecological environment systems and human society, which is a pertinent issue of concern for all countries globally. The construction of a carbon emission inversion model has significant theoretical importance and practical significance for carbon emission accounting and control. Established carbon emission models usually adopt socio-economic parameters or energy statistics to calculate carbon emissions. However, high-precision estimates of carbon emissions in administrative regions lacking energy statistics are difficult. This problem is especially prominent in small-scale regions. Methods to accurately estimate carbon emissions in small-scale regions are needed. Based on nighttime light remote-sensing data and the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model, combined with the environmental Kuznets curve, this paper proposes an ISTIRPAT (Improved Stochastic Impacts by Regression on Population, Affluence, and Technology) model. Through the improved STIRPAT model (ISTIRPAT) and panel data regression, provincial carbon emission inventory data were downscaled to the municipal level, and municipal scale carbon emission inventories were obtained. This study took the 17 cities and prefectures of Hubei Province, China, as an example to verify the accuracy of the model. Carbon emissions for 17 cities and prefectures from 2012 to 2018 calculated from the original STIRPAT model and the ISTIRPAT model were compared with real values. The results show that using the ISTIRPAT model to downscale the provincial carbon emission inventory to the municipal level, the inversion accuracy reached 0.9, which was higher than that of the original model. Overall, carbon emissions in Hubei Province showed an upward trend. Regarding the spatial distribution, the main carbon emission area was formed in the central part of Hubei Province as a ring-shaped mountain peak. The lowest carbon emissions in the central area expanded outward, increased, and gradually decreased to the edge of the province. The overall composition of carbon emissions in eastern Hubei was higher than those in western Hubei.
Rice serves as the staple food for over 50% of the global population. Remotely-sensed based estimation of the gross primary production (GPP) and evapotranspiration (ET) of rice paddy fields is essential to assess global food security. In this study, we tested the application of a recently proposed remotely-sensed based water-carbon coupled model (PML-V2) in the lower reaches of the Poyang Lake plain, which is one of the nine production bases for crops in China. Evaluation using the eddy covariance measurements showed that, after parameter localization, the model reproduced the seasonal variations of GPP and ET for both the early rice and the late rice. The model performed reasonably well in the validation period because the key parameters (e.g., the quantum efficiency and the stomatal conductance coefficient) exhibited predictable seasonal variations. At the regional scale, the spatial distribution in multi-year GPP of rice (1365 ± 326 gCm−2year−1) can be explained by the vegetation cover fraction (R2 > 0.9); in comparison, the multi-year ET (1003 ± 65 mm/year) exhibits smaller spatial variations due to the high evaporation rate of the saturated soil surface of paddy fields. The water use efficiency of rice in this region varies around 1.35 gC/kgH2O with a standard deviation of 0.30. Our study shows that GPP and ET of rice can be estimated by remote sensing models without detailed crop management information, which is usually unavailable at regional scales.
Drought is a common and greatly influential natural disaster, yet its reliable estimation and prediction remain a challenge. The object of this paper is to investigate the spatiotemporal evolution of drought in the Yangtze River basin. The multi-time scale drought characteristics were analyzed based on 19 models and 3 emission scenarios of CMIP6. The results show that the CMIP6 model generally has moisture deviation in the Yangtze River basin, but the accuracy has been improved after correction and ensemble. The drought conditions in the near future (2030–2059) of the Yangtze River basin will be more severe than those in the historical period (1981–2010), with the drought intensity increasing by 7.47%, 18.24%, 18.34%, and 41.48% in the order of 1-month, 3-month, 6-month, and 12-month scales, but it will be alleviated in the far future (2070–2099) to 5.97%, 11.86%, −4.09%, and −8.97% of the historical period, respectively. The 1-month scale drought events are few, and the spatial heterogeneity is strong under different scenarios; areas of high frequency of the 3-month, 6-month, and 12-month scale drought events shift from the upper and middle reaches, middle and lower reaches in the historical period to the southwestern part of the entire basin in the future, and the harm of drought in these regions is also higher. The Yangtze River basin will get wetter, and the variability will increase in the future. The larger the time scale is, the more intense the change will be, with the 12-month scale varying about three times as much as the 1-month scale.
The effect of topographic correction (TOC) has a profound influence on the quantitative application of remote sensing image. With regard to the invalid evaluation of the TOC model with such a single topographic correction assessment (TCA) method, we have selected five TCA indexes from five different perspectives: the difference in mean radiance radiometry between sunlit and shaded slopes, the cosine empirical relationship, stability, heterogeneity, and outlier ratio. The entropy weight method was used to assign weight to each TCA indexes, and the comprehensive evaluation value (CEV) of TOC for each band of remote sensing image was obtained by weighted superposition. After that, the weight of each band of the remote sensing image is determined by the entropy weight method, and the CEV of the TOC of the remote sensing image is obtained by weighting and superposition, so as to realize the effect evaluation of the six TOC models of C, SCS + C, VECA, Teillet, Minnaert, and Minnaert + SCS. The results indicate that the proposed method can effectively evaluate the correction effect of the TOC model. Results indicate that the SCS + C model has the best correction effect, while the Minnaert model performs the worst. The results generated from the Minnaert + SCS, Teillet, and Minnaert models typically show inferior quality. The SCS + C, VECA, and C models are better suited for generating images with high spectral fidelity, and these three correction models are recommended for TOCs over mountainous areas.
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