Satellite data indicates multidimensional variation of agricultural production in land consolidation area

Land consolidation (LC) is an innovative way to improve agricultural production. Spatiotemporal pattern of agricultural production in land consolidation area (LCA) is difficult to quantify with limited field observations and survey data. Satellite data has advantages on recording vegetation status changes frequently, which is very supportive of estimating variation of agricultural production. In this paper, we used Net Primary Productivity (NPP), Normal Difference Vegetation Index (NDVI), and Multiple Band Drought Index (MBDI) from satellite data, to examine five attributes (irrigation capacity, multiple cropping index, crop phenology, farmland productivity, and production stability) of agricultural production after land consolidation (LC) at a site in China. Results show that there were no significant spatial differences in irrigation capacity for farmland in few years after LC due to consistent climatic conditions and uniform irrigation and drainage system. Multiple cropping index shows a pattern of “first reducing, then growing, last reducing”, which may result from the disturbed “water-soil” environment and weak farmers' intention. Interannual variation of spatial distribution of phenology for the second-season crop is larger than that for the first-season crop since LC implementation adjusts short-term land use and management. With the improvement of production conditions and balanced distribution of production elements, farmland productivity has been improved and its differences among various farmland patches imply a reducing trend. Production in LCA is slightly less stable than that in the control area (TCA) where LC is not carried out because of limited and short-term effect from LC. We concluded that satellite data presents variation of agricultural production in LCA from different dimensions of time, space and attributes. Multidimensional variation of agricultural production is decided by several factors, including climate condition, LC activity, and farmers' intention.