Digital mapping of soil salinization based on Sentinel-1 and Sentinel-2 data combined with machine learning algorithms

Soil salinization is one of the most important causes of land degradation and desertification,especially in arid and semi-arid areas.The dynamic monitoring of soil salinization is of great significance to land management,agricultural activities,water quality,and sustainable development.The remote sensing images taken by the synthetic aperture radar(SAR)Sentinel-1 and the multispectral satellite Sentinel-2 with high resolution and short revisit period have the potential to monitor the spatial distribution of soil attribute information on a large area;however,there are limited studies on the combination of Sentinel-1 and Sentinel-2 for digital mapping of soil salinization.Therefore,in this study,we used topography indices derived from digital elevation model(DEM),SAR indices generated by Sentinel-1,and vegetation indices generated by Sentinel-2 to map soil salinization in the Ogan-Kuqa River Oasis located in the central and northern Tarim Basin in Xinjiang of China,and evaluated the potential of multi-source sensors to predict soil salinity.Using the soil electrical conductivity(EC)values of 70 ground sampling sites as the target variable and the optimal environmental factors as the predictive variable,we constructed three soil salinity inversion models based on classification and regression tree(CART),random forest(RF),and extreme gradient boosting(XGBoost).Then,we evaluated the prediction ability of different models through the five-fold cross validation.The prediction accuracy of XGBoost model is better than those of CART and RF,and soil salinity predicted by the three models has similar spatial distribution characteristics.Compared with the combination of topography indices and vegetation indices,the addition of SAR indices effectively improves the prediction accuracy of the model.In general,the method of soil salinity prediction based on multi-source sensor combination is better than that based on a single sensor.In addition,SAR indices,vegetation indices,and topography indices are all effective variables for soil salinity prediction.Weighted Difference Vegetation Index(WDVI)is designated as the most important variable in these variables,followed by DEM.The results showed that the high-resolution radar Sentinel-1 and multispectral Sentinel-2 have the potential to develop soil salinity prediction model.

渭干河-库车河绿洲土壤盐渍化时空变化及其影响因素分析

文章以新疆土壤盐渍化较为典型的渭干河-库车河绿洲为基础,运用RS/GIS技术和统计分析方法,结合野外调查,对该绿洲1989、2001、2006、2011和2016年5个时期的盐渍化土壤时空动态变化特征及其驱动力进行了分析。结果表明:(1)渭干河-库车河绿洲土壤盐渍化类型有很大的变化,总体呈逐渐减轻的趋势,其中,重度盐渍化地面积减少比较明显,累计减少了1420.715km2,年均减少了52.62km2,中轻度盐渍化地面积总体上变化不大,累计增多了71.81 km2,非盐渍化地面积呈一直增加的趋势,累计增加了1574.6km2,是研究区各景观类型中面积最大的类型;(2)经研究区的地物转移分析发现,各土地覆盖类型之间面积转换频繁,研究区非盐渍地面积增加了很多,但也存在部分非盐渍地转化为中轻度盐渍化地和重度盐渍化地的现象。整个研究时段内,重度盐渍地面积减少了很多,各时期重度盐渍地、中轻度盐渍化地及其它地类转化为非盐渍地的转移概率也比较大;(3)干旱的气候条件、丰富的盐分来源、较浅的地下水位埋深并且较高的地下水矿化度等自然因素决定了土壤盐渍化空间分布的大体趋势,大规模绿洲水土资源开发利用间接地影响土壤盐渍化的空间变化过程。这一研究为掌握渭干河-库车河绿洲盐渍化土壤时空变化趋势及土地资源的可持续利用提供科学依据。

塔里木盆地中北部绿洲生态安全评价

维护干旱区绿洲生态安全,是实现区域可持续发展的重要前提。以渭干河-库车河绿洲为研究区,从绿洲生态系统结构、功能、效益3方面构建评价指标体系;运用三角模型,对2005-2014年该绿洲生态安全状态和趋势进行测算分析;采用灰色关联分析法对影响该绿洲生态安全的因子进行分析。结果表明：（1）生态系统非结构性指数（NSI）总体呈逐年下降趋势;生态系统非功能性指数（NFI）,呈现出波动变化趋势;生态系统效益性指数（BI）总体上呈现持续上升趋势。（2）在2005-2014年期间,渭干河-库车河绿洲生态安全经历了不安全-弱安全两个状态,并呈现出逐渐向安全且稳定发展的趋势。（3）2005-2014年影响渭干河-库车河绿洲生态安全的主要因子是水土协调度和造林面积。研究结果将为维持及进一步改善该绿洲生态环境提供科学依据。

新疆渭干河-库车河三角洲绿洲生态需水研究

绿洲是干旱区独特的景观类型,是人类活动的主体区域,绿洲的开发规模取决于水资源的利用程度和管理水平。在干旱区生态用水的概念与分类基础上,依据部分试验资料及理论公式推导确定各类生态用水定额,并以各生态用水类型面积为依据,对渭干河-库车河三角洲绿洲生态用水进行了初步估算。结果表明:渭干河-库车河三角洲绿洲生态用水总量达24.158×108m3,其中天然系统用水量为21.991×108m3,占91.03%;人工系统用水量为2.167×108m3,占8.97%。在天然系统用水中低地草甸最大,达11.417×108m3,占47.25%;在人工系统用水中,园林生态用水最大,达1.005×108m3,占4.16%。由于天然植被的生态用水主要来源于地下水,故扣除平原地下水补给量19.603×108m3,实际生态用水量为4.555×108m3。