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Rural development degree (RDD) evaluation is a very valuable guidance for rural sustainable development. Earlier studies of RDD evaluatons more focusd on establishing index system, analysing spatial–temporal evolving patterns and functional differentiations. Weighting assignment (WA) method selection is a vital step for rural development degree (RDD) evaluation, but impacts of different WA methods on indicator weight determination and RDD evaluation were not well clarified. This study therefore employed three dominant WA methods, covering equal weight method, entropy method and mean square error method, along with a developed RDD evaluation index system to compare the differences of indicator weights and uncertainties of RDD evaluation. The results indicated that the spatial patterns of three WA-based RDD maps had great differences although using the same evaluation indicators. The RDD types with the largest proportion generated from different WA methods were also spatially various. Spatial distributions of RDD generated by various WA methods regions performed largely differences in central, northeastern and southwestern China. Our analyses found that the differences from industrial prosperity dimension and ecology livability dimension owing to utilizing different WA methods were largely responsible for the RDD spatial distributions in this study. This study gave some potential suggestions for WA method selection in RDD evaluation according to the data characteristics, WA method principles and application requirements. Among them, entropy method was suitable for indicator data with great dispersion degree and mean square error method would be better describe indicator differences with large indicator number. Besides, this study also underlined that WA-derived uncertainties should be paid more attentions in rural development and rural revitalization evaluation.

Advances in the timing of spring green-up date are a typical response of vegetation global change. Long-term observations of plant phenology have been used to track vegetation response to climate change. Normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) have been the most commonly used indicators in reconstructing spring green-up dates from remote sensing over the past several decades. The Moderate Resolution Imaging Spectroradiometer (MODIS) based phenology time series from NDVI and EVI with an enhanced TIMESAT algorithm are the two operational phenology products developed recently with sun-sensor geometry corrected reflectance. However, a comprehensive intercomparison and evaluation of these two spring green-up datasets using intensive ground observations have not been conducted, limiting their applications in regional interpretation of land surface phenology. Therefore, we used 455 ground observations from USA National Phenology Network (NPN) and 106 observations from 21 AmeriFlux sites to assess and validate the spring green-up dates for United States for 2000-2013. Our results indicate that the spring green-up dates from NDVI and EVI showed a good agreement in eastern United States, while substantial differences (70 days) were found in western and southern areas. Ground observations generally had a significant correlation (p < 0.01) with the two MODIS spring green-up dates, especially for deciduous broadleaf forests with root mean square error (RMSE) of 12 days and 16 days when compared with USA-NPN and AmeriFlux observations, respectively. Spring green-up dates from EVI overall showed a better relationship and a lower RMSE in reference to both USA-NPN and AmeriFlux observations than NDVI-based spring green-up dates. Our results highlight the importance of a rigorous validation of remote sensing products to better understand their limitations in operational applications. [ABSTRACT FROM AUTHOR]