Based on the microdata of 705 wheat farmers in the Loess Plateau, this study empirically analyzes the impact of uncertainty on farmers’ adoption of innovative seeds using a field experiment.  The results indicate that farmers are generally ambiguity-averse and risk-averse.  In addition, farmers with higher ambiguity aversion and risk aversion are less likely to adopt innovative wheat seeds, where their risk aversion plays a dominant role.  Enhancing information access will alleviate the negative influence of ambiguity aversion on farmers’ adoption of innovative seeds, and interlinked insurance and credit contracts will be beneficial to ease the adverse effect of risk aversion on the adoption of innovative wheat seeds.  Meanwhile, heterogeneity analysis reveals that the inhibitory effects of ambiguity aversion and risk aversion on innovative seed adoption are more significant among farmers with lower education and household income.  The government can establish both ex-ante and ex-post relevant guarantee mechanisms to help farmers preferably cope with various uncertainties in the production process, remitting farmers’ ambiguity aversion and risk aversion to enhance new agricultural technology adoption rates.

An increase in crop intensity could improve crop yield but may also lead to a series of environmental problems, such as depletion of ground water and increased soil salinity.  The generation of high resolution (30 m) crop intensity maps is an important method used to monitor these changes, but this is challenging because the temporal resolution of the 30-m image time series is low due to the long satellite revisit period and high cloud coverage.  The recently launched Sentinel-2 satellite could provide optical images at 10–60 m resolution and thus improve the temporal resolution of the 30-m image time series.  This study used harmonized Landsat Sentinel-2 (HLS) data to identify crop intensity.  The sixth polynomial function was used to fit the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) curves.  Then, 15-day NDVI and EVI time series were then generated from the fitted curves and used to generate the extent of croplands.  Lastly, the first derivative of the fitted VI curves were used to calculate the VI peaks; spurious peaks were removed using artificially defined thresholds and crop intensity was generated by counting the number of remaining VI peaks.  The proposed methods were tested in four study regions, with results showing that 15-day time series generated from the fitted curves could accurately identify cropland extent.  Overall accuracy of cropland identification was higher than 95%.  In addition, both the harmonized NDVI and EVI time series identified crop intensity accurately as the overall accuracies, producer’s accuracies and user’s accuracies of non-cropland, single crop cycle and double crop cycle were higher than 85%.  NDVI outperformed EVI as identifying double crop cycle fields more accurately.

Accurate and reliable cropland surface information is of vital importance for agricultural planning and food security monitoring.  As several global land cover datasets have been independently released, an inter-comparison of these data products on the classification of cropland is highly needed.  This paper presents an assessment of cropland classifications in four global land cover datasets, i.e., moderate resolution imaging spectrometer (MODIS) land cover product, global land cover map of 2009 (GlobCover2009), finer resolution observation and monitoring of global cropland (FROM-GC) and 30-m global land cover dataset (GlobeLand30).  The temporal coverage of these four datasets are circa 2010.  One of the typical agricultural regions of China, Shaanxi Province, was selected as the study area.  The assessment proceeded from three aspects: accuracy, spatial agreement and absolute area.  In accuracy assessment, 506 validation samples, which consist of 168 cropland samples and 338 non-cropland ones, were automatically and systematically selected, and manually interpreted by referencing high-resolution images dated from 2009 to 2011 on Google Earth.  The results show that the overall accuracy (OA) of four datasets ranges from 61.26 to 80.63%.  GlobeLand30 dataset, with the highest accuracy, is the most accurate dataset for cropland classification.  The cropland spatial agreement (mainly located in the plain ecotope of Shaanxi) and the non-cropland spatial agreement (sparsely distributed in the south and middle of Shaanxi) of the four datasets only makes up 33.96% of the whole province.  FROM-GC and GlobeLand30, obtaining the highest spatial agreement index of 62.40%, have the highest degree of spatial consistency.  In terms of the absolute area, MODIS underestimates the cropland area, while GlobCover2009 significantly overestimates it.  These findings are of value in revealing to which extent and on which aspect that these global land cover datasets may agree with each other at small scale on each ecotope region.  The approaches taken in this study could be used to derive a fused cropland classification dataset.