Urmia Lake, located in Iran, has a unique pedological feature as fresh soil materials are being exposed as the lake is shrinking. Overuse of its water resources has caused its water level to drop, particularly in the east shore of that lake. This consequence has caused a serious environmental risk, but created a rare opportunity to investigate the spatial distribution of six iron forms and features, i.e., total iron (Fe_t); dithionite-extractable iron (Fe_d); oxalate-extractable iron (Fe_o); crystalline iron (Fe_d-Fe_o); activity ratio (Fe_o/Fe_d) and crystallinity ratio (Fe_d-Fe_o/Fe_t). In total, 130 soil samples were taken from three strata based on their proximity to the lake. Spectral indices derived from Landsat-8 OLI imagery acquired in July 2017 were used as environmental covariates in spatial models using Random Forests (RF) technique. The calibrated RF model for 2017 allowed us to backcast data for 2013 and 2015. The results showed that the iron forms and features varied primarily as a function of distance from the shore. Soil ripening process was also demonstrated in the east shore of Urmia Lake. Overall, this study showed the possibility of a combination of spectral imageries and spatial models to predict the dynamic environment of iron forms.

伊朗的Urmia湖具有独特的生态学特征，因为随着湖的缩小，新鲜的土壤物质正逐渐暴露出来。水资源的过度使用导致其水位下降，特别是在该湖的东岸。这一结果造成了严重的环境风险，但为研究六种铁形态和特征（即总铁（Fe _t））的空间分布创造了难得的机会。连二亚硫酸盐萃取铁（Fe _d）；草酸盐可萃取铁（Fe _o）；结晶铁（Fe _d -Fe _o）；活度比（Fe _o / Fe _d）和结晶度比（Fe _d -Fe _o / Fe _t）。根据与湖的接近程度，从三个地层总共采集了130个土壤样品。使用随机森林（RF）技术，将从2017年7月获取的Landsat-8 OLI图像获得的光谱指数用作空​​间模型中的环境协变量。2017年经过校准的RF模型使我们可以对2013年和2015年的数据进行后向分析。结果表明，铁的形式和特征主要随距海岸的距离而变化。乌尔米亚湖东岸也表现出土壤成熟过程。总的来说，这项研究表明将光谱图像和空间模型相结合来预测铁形态动态环境的可能性。