School of Natural Resources and Environment, University of Florida, 103 Black Hall, PO Box 116455, Gainesville, Florida, 32611, USA.
School of Forest Resources and Conservation, University of Florida, 136 Newins-Ziegler Hall, Gainesville, Florida, 32611, USA.
Ecol Appl. 2017 Jul;27(5):1646-1656. doi: 10.1002/eap.1557. Epub 2017 Jun 19.
Soil carbon sequestration in agroecosystems could play a key role in climate change mitigation but will require accurate predictions of soil organic carbon (SOC) stocks over spatial scales relevant to land management. Spatial variation in underlying drivers of SOC, such as plant productivity and soil mineralogy, complicates these predictions. Recent advances in the availability of remotely sensed data make it practical to generate multidecadal time series of vegetation indices with high spatial resolution and coverage. However, the utility of such data largely is unknown, only having been tested with shorter (e.g., 1-2 yr) data summaries. Across a 2,000 ha subtropical grassland, we found that a long time series (28 yr) of a vegetation index (Enhanced Vegetation Index; EVI) derived from the Landsat 5 satellite significantly enhanced prediction of spatially varying SOC pools, while a short summary (2 yr) was an ineffective predictor. EVI was the best predictor for surface SOC (0-5 cm depth) and total measured SOC stocks (0-15 cm). The optimum models for SOC in the upper soil layer combined EVI records with elevation and calcium concentration, while deeper SOC was more strongly associated with calcium availability. We demonstrate how data from the open access Landsat archive can predict SOC stocks, a key ecosystem metric, and illustrate the rich variety of analytical approaches that can be applied to long time series of remotely sensed greenness. Overall, our results showed that SOC pools were closely coupled to EVI in this ecosystem, demonstrating that maintenance of higher average green leaf area is correlated with higher SOC. The strong associations of vegetation greenness and calcium concentration with SOC suggest that the ability to sequester additional SOC likely will rely on strategic management of pasture vegetation and soil fertility.
农业生态系统中的土壤碳固存可以在气候变化缓解方面发挥关键作用，但需要准确预测与土地管理相关的空间尺度上的土壤有机碳 (SOC) 储量。SOC 潜在驱动因素（如植物生产力和土壤矿物学）的空间变化使这些预测变得复杂。遥感数据可用性的最新进展使得生成具有高空间分辨率和覆盖范围的植被指数多年时间序列成为可能。然而，这些数据的实用性在很大程度上是未知的，仅通过更短的（例如，1-2 年）数据摘要进行了测试。在一个 2000 公顷的亚热带草原上，我们发现，从陆地卫星 5 号获取的植被指数（增强型植被指数；EVI）的长时间序列（28 年）显著提高了空间变化 SOC 池的预测能力，而短期摘要（2 年）则是无效的预测因子。EVI 是预测表层 SOC（0-5 厘米深度）和总测量 SOC 储量（0-15 厘米）的最佳指标。用于上层土壤 SOC 的最佳模型将 EVI 记录与海拔和钙浓度相结合，而深层 SOC 与钙供应的关系更密切。我们展示了如何使用开放获取的陆地卫星档案中的数据来预测 SOC 储量，这是一个关键的生态系统指标，并说明了可以应用于遥感绿色度长时间序列的各种丰富的分析方法。总体而言，我们的结果表明，在这个生态系统中，SOC 池与 EVI 密切相关，这表明维持较高的平均绿叶面积与较高的 SOC 相关。植被绿色度和钙浓度与 SOC 的强烈关联表明，额外 SOC 的固存能力可能依赖于牧场植被和土壤肥力的战略管理。
