Accurate information about the spatiotemporal patterns of rice paddies is essential for the assessment of food security, management of agricultural resources, and sustainability of ecosystems. However, accurate spatial datasets of rice paddy fields and multi-cropping at fine resolution are still lacking. Landsat observation is the primary source of remote sensing data that has continuously mapped regional rice paddy fields at a 30-m spatial resolution since the 1980s. However, Landsat data used for rice paddy studies reveals some challenges, especially data quality issues (e.g., cloud cover). Here, we present an algorithm that integrates time-series Landsat and MODIS (Moderate-resolution Imaging Spectroradiometer) images with a phenology-based approach (ILMP) to map rice paddy planting fields and multi-cropping patterns. First, a fusion of MODIS and Landsat data was used to reduce the cloud contamination, which added more information to the Landsat time series data. Second, the unique biophysical features of rice paddies during the flooding and open-canopy periods (which can be captured by the dynamics of the vegetation indices) were used to identify rice paddy regions as well as those of multi-cropping. This algorithm was tested for 2015 in Nanchang County, which is located on the Poyang Lake plain in southern China. We evaluated the resultant map of the rice paddy and multi-cropping systems using ground-truth data and Google Earth images. The overall accuracy and kappa coefficient of the rice paddy planting areas were 93.66% and 0.85, respectively. The overall accuracy and kappa coefficient of the multi-cropping regions were 92.95% and 0.89, respectively. In addition, our algorithm was more capable of capturing detailed information about areas with fragmented cropland than that of the National Land Cover Dataset (NLCD) from 2015. These results demonstrated the great potential of our algorithm for mapping rice paddy fields and using the multi-cropping index in complex landscapes in southern China.

Payment for Ecosystem Services (PES), a market-based policy instrument for the conservation and environmental management that aims to coordinate the interests of upstream and downstream ecosystem service (ES) stakeholders, has been adopted worldwide. However, the success of PES depends on the desirability of programs targeting rural communities and smallholders. In this article, an agent-based sustainability perspective on PES was proposed and applied to examine a PES case study of the Converting-Orchard-to-Forest (COF) project in Dongjiang Headwater Watershed (DHW). We used household interview-based information and associated secondary data to quantitatively assess the environmental consequences and livelihood impacts of the COF project. The findings show that: (1) the COF participants at the upstream suffered from substantial income loss due to decreased orchard area; (2) the participants’ chemical fertilizer and compound fertilizer consumption was larger than their nonparticipating counterparts; and (3) the COF participants and nonparticipants increased the material assets and reduced their fuelwood use and increased the liquefied petroleum gas. Our findings suggest that, because of the significant income loss experienced by the upstream participants, the COF program is unsustainable with the participants very likely to cultivate the orchard again once the COF project ends. The research provides insightful information regarding PES implementation and sustainability of similar PES schemes.