Using proximal sensor data for soil salinity management and mapping

Abstract

Over the past five decades, increased pressure caused by the rapidly growing population has resulted in a reclamation of agricultural and urban buffer zones along China's coastline. However, information about the spatio–temporal variation of soil salinity in these reclaimed regions is limited. As such, obtaining this information is crucial for mapping the variation in saline areas and to identify suitable salinity management strategies. In this study, we employed EM38 data to conduct digital soil mapping of spatio–temporal variation and map these variations of different site-specific zones. The results indicated that the distribution of soil salinity was heterogeneous in the middle of, and that the leaching of salts was significant at the edges of, the study field. Afterwards, fuzzy-k means algorithm was used to divide the site-specific management zones within the time series apparent soil electrical conductivity (ECa) data and the spatial correlations of variation. We concluded that two management zones are optimal to guide precision management. Zone A had an average salinity level of about 165 mS m−1, in which salt-tolerant crops, such as cotton and barley can grow normally, while crops such as soybean and cowpeas may be planted using leaching and increasing the mulching film methods to reduce the accumulation of salt in surface soil. In Zone B, there was a low salinity level with a mean of 89 mS m−1 for ECa, which allows for rice, wheat, and a wide range of vegetables to be grown normally. In such situations, measures such as an optimized combination of irrigation and drainage, as well as soil amendment can be taken to adjust and control the salt content. Particularly, flattening the land with a large-scale machine was used to improve the ability of micro-topography to influence salt migration; rice and other dry, land crops were planted in rotation in combination with utilizing salt-leaching multiple times to speed up desalinization.