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The aim of this study is to assess the winter wheat planting (WWP) area in Henan Province and investigate its temporal and spatial variations by using remote sensing (RS) technology. A spectral angle mapper (SAM) was adopted to identify the WWP area of each district divided by the hierarchical grades of land surface drought index during 2001-2015. The results obtained show the expediency of monitoring the WWP areas at the regional scale via drought regionalization, which provides a goodness-of-fit R2 =0.933, a mean relative error MRE=49,118 ha, and an overall accuracy up to 90.24%. The major WWP areas in Henan Province were located in Zhoukou, Zhumadian, Shangqiu, Nanyang, and Xinxiang prefecture-level cities. Two representative sites are mountainous districts, with rich water resources or high urbanization rate, which have a low probability of WWP. Both sites exhibited a strongly manifested evolution of WWP areas, which could be attributed to extremely cold weather conditions, crop alternation, the popularization of new varieties, and fast expansion of built-up areas. The results of this study are instrumental in the analysis of crop planting variation characteristics, which should be taken into account in the further decision-making process related to the crop planting strategies.

Timely and accurate acquisition of cropland spatial distribution data is of great significance for agricultural production management, planting structure adjustment and food security. In this study, three northeastern provinces (Heilongjiang, Liaoning and Jilin) were selected as the research area. Based on massive Sentinel-2 data, a 12-month Normalized Difference Vegetation Index (NDVI) dataset of cropland and non-cropland samples in a specific year was established in each province. The Google Earth Engine remote sensing computing platform was used to conduct supervised classification according to the difference characteristics of NDVI between cropland and non-cropland samples, and the spatial distribution data set of cropland resources with 10 m spatial resolution in the three northeastern provinces during 2020-2022 was obtained. The data set is an update of the latest available cropland resource data set, which can provide data support and scientific services for the scientific protection of phaeozem in Northeast China, the implementation of the strategy of "storing grain in land, storing grain in technology" and the guarantee of food security. [ABSTRACT FROM AUTHOR]

Monitoring changes of paddy rice is challenging due to its diverse cropping patterns and spectral variation. To investigate the spatio-temporal changes of rice cropping, we used the 10-day composited Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series data with a spatial resolution of 250 m to map the sub-pixel rice spatial distributions in the Hunan Province, the top one region in rice planting area in southern of China. A method of improved phenology-based temporal mixture analysis (PTMA) was presented to identify early, middle, and late rice cropping patterns. The results show that the PTMA is effective to extract rice cropping. The nine rice cropping patterns were classified as early, middle, and late rice cropping, and fractional rice cropping within 250 m pixels was obtained to analyse the internal changes. Both the local planting conditions and different forms of rice cultivation were compared with statistical data. Overall, MODIS-estimated fractional rice agreed well with field samples at the pixel level and statistical data at the county level, which demonstrates the effectiveness of the PTMA method for mapping rice in these hilly regions with small-size paddy rice field. The changes show that single-cropping rice and double-cropping rice have been frequently transferred in space, which could be important information to support agricultural decision-making. [ABSTRACT FROM AUTHOR]