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Artificial irrigation is critical for improving soil moisture conditions and ensuring crop growth. Its irrational deployment can lead to ecological and environmental issues. Mapping and understanding the changes in irrigated areas are vital to effectively managing limited water. However, most researchers map irrigated areas with a single data resource, which makes it hard to detect irrigated signals in complex situations. The case study area for this paper was China's winter wheat region, and an irrigated area map was generated by analyzing the effects of artificial irrigation on crop phenological characteristics and soil moisture time series. The mapping process involved three steps: (1) generating a basic irrigated map by employing the ISODATA classification method on the Kolmogorov–Smirnov test irrigation signals from the microwave remote sensing data and reanalysis data; (2) creating the other map with the maximum likelihood ratio classification and zoning scheme on the phenological parameters extracted from the NDVI time series; and (3) fusing these two maps at the decision level to obtain the final map with a higher spatial resolution of 1 km. The map was evaluated against existing irrigated area data and was highly compatible with GMIA 5.0. The overall accuracy (OA) was 73.49%. [ABSTRACT FROM AUTHOR]

Highlights • An improved interpolation method for the reconstruction of winter wheat yield series was proposed. • The improved method concerned the spatio-temporal heterogeneity of winter wheat yield data. • Spatio-temporal integration was adopted to the interpolation process by a back propagation neural network model. Abstract Crop growth conditions and meteorological environments are observed and recorded by agro-meteorological stations, which, however, may fail to record crop yield data in some specific years. In this context, incomplete yield series data constrain their application and result in inconvenience in information mining. Accordingly, this study improves the existing spatio-temporal interpolation method and succeeds in interpolating wheat yield data observed and recorded by 56 agro-meteorological stations on the Huang-Huai-Hai Plain of China. In this study, pre-interpolation is first implemented to improve the completion rate of interpolation and eliminate the effect of absent neighboring values on the positions to be interpolated. Then, data reconstruction is performed in spatial and temporal dimensions based on spatio-temporal heterogeneity. Finally, the reconstructed results are combined with the back propagation (BP) neural network model for spatio-temporal integration. Moreover, this study analyzes the settings of key parameters and compares them with traditional interpolation methods. Corresponding results demonstrate the superiority of the proposed method in this study over traditional interpolation methods in terms of interpolation precision and the completion rate. Meanwhile, individual interpolation precision in each step of the proposed method is effectively enhanced. [ABSTRACT FROM AUTHOR]

Simulations based on site-specific crop growth models have been widely used to obtain regional yield potential estimates for food security assessments at the regional scale. By dividing a region into nonoverlapping basic spatial units using appropriate zonation schemes, the data required to run a crop growth model can be reduced, thereby improving the simulation efficiency. In this study, we explored the impacts of different zonation schemes on estimating the regional yield potential of the Chinese winter wheat area to obtain the most appropriate spatial zonation scheme of weather sites therein. Our simulated results suggest that the upscaled site-specific yield potential is affected by the zonation scheme and by the spatial distribution of sites. As such, the distribution of a small number of sites significantly affected the simulated regional yield potential under different zonation schemes, and the zonation scheme based on sunshine duration clustering zones could effectively guarantee the simulation accuracy at the regional scale. Using the most influential environmental variable of crop growth models for clustering can get the better zonation scheme to upscale the site-specific simulation results. In contrast, a large number of sites had little effect on the regional yield potential simulation results under the different zonation schemes. [ABSTRACT FROM AUTHOR]