Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden.
Swiss Federal Institute for Forest, Snow and Landscape research (WSL), Birmensdorf, Switzerland.
PLoS One. 2018 Jul 11;13(7):e0200328. doi: 10.1371/journal.pone.0200328. eCollection 2018.
Satellite derived normalized difference vegetation index (NDVI) is a common data source for monitoring regional and global ecosystem properties. In dry lands it has contributed to estimation of inter-annual and seasonal vegetation dynamics and phenology. However, due to the spectral properties of NDVI it can be affected by clouds which can introduce missing data in the time series. Remotely sensed soil moisture has in contrast to NDVI the benefit of being unaffected by clouds due to the measurements being made in the microwave domain. There is therefore a potential in combining the remotely sensed NDVI with remotely sensed soil moisture to enhance the quality and estimate the missing data. We present a step towards the usage of remotely sensed soil moisture for estimation of savannah NDVI. This was done by evaluating the European Space Agency (ESA) Climate Change Initiative (CCI) soil moisture and three of its individual products with respect to their relative performance. The individual products are from the advance scatterometer (ASCAT), Soil Moisture and Ocean Salinity (SMOS), and the Land Parameter Retrieval Model-Advanced Microwave Scanning Radiometer-Earth Observing System (LPRM-AMSR-E). Each dataset was used to simulate NDVI, which was subsequently compared to remotely sensed NDVI from MODIS. Differences in their ability to estimate NDVI indicated that, on average, CCI soil moisture differs from its individual products by showing a higher average correlation with measured NDVI. Overall NDVI modelled from CCI soil moisture gave an average correlation of 0.81 to remotely sensed NDVI which indicates its potential to be used to estimate seasonal variations in savannah NDVI. Our result shows promise for further development in using CCI soil moisture to estimate NDVI. The modelled NDVI can potentially be used together with other remotely sensed vegetation datasets to enhance the phenological information that can be acquired, thereby, improving the estimates of savannah vegetation phenology.
卫星衍生归一化差异植被指数（NDVI）是监测区域和全球生态系统特性的常用数据源。在旱地中，它有助于估计年际和季节性植被动态和物候。然而，由于 NDVI 的光谱特性，它可能会受到云层的影响，从而导致时间序列中出现缺失数据。与 NDVI 相反，遥感土壤湿度由于测量是在微波域中进行的，因此不受云层影响。因此，将遥感 NDVI 与遥感土壤湿度相结合具有提高质量和估计缺失数据的潜力。我们提出了一种利用遥感土壤湿度来估算热带稀树草原 NDVI 的方法。通过评估欧洲航天局（ESA）气候变化倡议（CCI）土壤湿度及其三个单独产品的相对性能来实现这一点。这些单独的产品来自先进散射计（ASCAT）、土壤湿度和海洋盐度（SMOS）以及陆地参数反演模型-高级微波扫描辐射计-地球观测系统（LPRM-AMSR-E）。每个数据集都用于模拟 NDVI，随后将其与 MODIS 遥感 NDVI 进行比较。它们估计 NDVI 的能力差异表明，平均而言，CCI 土壤湿度与其各个产品不同，因为它与实测 NDVI 的平均相关性更高。总体而言，CCI 土壤湿度模拟的 NDVI 与遥感 NDVI 的平均相关性为 0.81，这表明其具有估算热带稀树草原 NDVI 季节性变化的潜力。我们的结果表明，进一步开发利用 CCI 土壤湿度来估算 NDVI 具有前景。模拟的 NDVI 可以与其他遥感植被数据集一起使用，以增强可以获取的物候信息，从而提高对热带稀树草原植被物候的估计。
