Fine-scale soil mapping with Earth Observation data: a multiple geographic level comparison

ABSTRACT Multitemporal collections of satellite images and their products have recently been explored in digital soil mapping. This study aimed to produce a bare soil image (BSI) for the São Paulo State (Brazil) to perform a pedometric analysis for different geographical levels. First, we assessed the potential of the BSI for predicting the surface (0.00-0.20 m) and subsurface (0.80-1.00 m) clay, iron oxides (Fe 2 O 3 ), aluminum (m%) and bases saturation (V%) contents at the state level, which are important properties for soil classification. In this task, legacy soil samples, the BSI and terrain attributes were employed in machine learning. In a second moment, we evaluated the capacity of the BSI for clustering the landscape at the regional level, comparing the predicted patterns with a legacy semi-detailed soil map from a smaller reference site. In the final stage, the predicted soil maps from the state level were investigated at the farm level considering several sites distributed across the São Paulo state. Our results demonstrated that clay and Fe 2 O 3 reached the best prediction performance for both depths at the state level, reaching a RMSE of less than 10 %, RPIQ higher than 1.6 and R 2 of at least 0.41. Additionally, the predicted landscape clusters had a significant association with the main pedological classes, subsurface color, soil mineralogy and texture from the legacy semi-detailed soil map. Illustrative examples at the farm level indicated great capacity of BSI in detecting the variations of soils, which were linked to several soil properties, such as texture, iron content, drainage network, among others. Therefore, this study demonstrates that BSI is valuable information derived from optical Earth Observation data that can contribute to the future of soil survey and mapping in Brazil (PronaSolos).摘要 多时相卫星影像及其衍生产品数据集近期已被应用于数字土壤制图研究领域。本研究旨在构建巴西圣保罗州裸土影像（bare soil image, BSI），以开展不同地理尺度下的土壤计量学分析。首先，我们评估了裸土影像在州尺度下预测表层（0.00-0.20 m）与亚表层（0.80-1.00 m）黏粒、三氧化二铁（Fe₂O₃）、铝含量（质量百分比，m%）以及盐基饱和度（V%）的潜力，上述指标均为土壤分类的重要属性。本研究将遗留土壤样品、裸土影像与地形属性应用于机器学习模型。第二阶段，我们评估了裸土影像在区域尺度下开展景观聚类的能力，并将预测得到的景观格局与来自更小参考样区的遗留半详细土壤图进行对比。最终阶段，我们针对分布于圣保罗州全域的多个样点，在农场尺度下对州尺度预测得到的土壤图展开分析。我们的研究结果表明，在州尺度下，两个土层的黏粒与三氧化二铁含量均取得了最优预测性能：均方根误差（root mean square error, RMSE）小于10%，四分位距性能比（ratio of performance to interquartile range, RPIQ）高于1.6，决定系数（coefficient of determination, R²）至少为0.41。此外，预测得到的景观聚类结果与遗留半详细土壤图中的主要土壤学分类单元、亚表层颜色、土壤矿物学特征及土壤质地均存在显著关联。农场尺度的典型案例表明，裸土影像具备出色的土壤变化检测能力，其检测结果可与质地、铁含量、排水网络等多项土壤属性建立关联。综上，本研究证实裸土影像是由光学对地观测数据衍生得到的宝贵信息，可为巴西未来的土壤调查与制图工作（PronaSolos）提供有力支撑。