Classification and Estimation of Irrigation Waters Based on Remote Sensing Images: Case Study in Yucheng City (China)

Abstract

The downstream plain of the Yellow River is experiencing some of the most severe groundwater depletion in China. Although the Chinese government has issued policies to ensure that the Yellow River can provide enough irrigation waters for this region, groundwater levels continue to decrease. Yucheng City was selected as a case study. A new method was designed to classify the cropland into various irrigated cropland. Subsequently, we analyzed data regarding these irrigated-cropland categories, irrigation norms, and the minimum amount of irrigation water being applied to cropland. The results showed that 91.5% of farmland can be classified as double irrigated (by both canal/river and well water), while 8.5% of farmland can be classified as well irrigated. During the irrigation season, the sediments brought in by the river have blocked portions of the canals. This has led to 23% of the double-irrigated cropland being irrigated by groundwater, and it is thus a main factor causing reductions in groundwater supply. These blocked canals should be dredged by local governments to mitigate local groundwater depletion. The method for classifying irrigated cropland from high-resolution images is valid and it can be used in other irrigated areas with a declining groundwater table for the sustainable use of groundwater resources.