Salt marshes constitute an important terrestrial-aquatic interface that remains underrepresented in Earth System Models due to constraining biophysical controls and spatially limited land cover. One promising approach to improve representativeness is the application of proximal remote sensing to generate phenological information, yet we lack detailed knowledge on how proximal sensors and indices perform within these ecosystems. We use measurements of net ecosystem productivity (NEP) from eddy covariance (EC) and derive ecologically-relevant phenology parameters (i.e., phenoperiods) to use as carbon phenology benchmarks. These benchmarks are compared against vegetation indices and spectral bands derived from spaceborne (i.e., MODIS) or common proximal sensors (i.e., phenocam and spectral reflectance sensors; SRS).

Phenocam derived indices, which exclude infrared wavelengths (i.e., vegetation contrast index; VCI and greenness chromatic coordinate; GCC), aligned closely with NEP benchmarks and provided best predictions of carbon sink season length (within 1–6 days of benchmark). Although isolating infrared from vegetation (NIRv) offered improvements, other indices utilizing infrared bands (i.e., normalized difference vegetation index; NDVI and enhanced vegetation index; EVI) primarily underestimated season start dates (5–30 days prior to benchmark) while overestimating season end dates (7–47 days after benchmark). These discrepancies are greatest for indices derived from MODIS and SRS sensors, which have narrower full width half maximum spectral bandwidths and sharper orientation angles. The phenocam (VCI and GCC) provides the most accurate phenology parameters while offering near-infrared (NIR) response which can generate additional information on seasonal changes in canopy structure and function.

The distinctive characteristics of the salt marsh environment and vegetation properties including standing dead biomass can introduce interpretation challenges for commonly used vegetation indices (NDVI, EVI). Incorporating information from proximal sensors utilizing only visible wavelengths (VCI, GCC) or isolating the near-infrared reflectance of vegetation (NIRv) offers improvements for studying carbon phenology within salt marshes.

盐沼构成了一个重要的陆地 - 水生界面，由于生物物理控制和空间有限的土地覆盖，在地球系统模型中仍然代表性不足。提高代表性的一种有前景的方法是应用近端遥感来生成物候信息，但我们缺乏有关近端传感器和指数在这些生态系统中如何执行的详细知识。我们使用来自涡度协方差 (EC) 的净生态系统生产力 (NEP) 的测量值，并推导出与生态相关的物候参数（即，物候期）用作碳物候基准。将这些基准与源自星载（即 MODIS）或常见近端传感器（即 phenocam 和光谱反射传感器；SRS）的植被指数和光谱带进行比较。

Phenocam 衍生指数，不包括红外波长（即植被对比度指数；VCI 和绿色色度坐标；GCC），与 NEP 基准密切相关，并提供了碳汇季节长度的最佳预测（在基准的 1-6 天内）。尽管从植被中分离红外线 (NIRv) 提供了改进，但其他利用红外波段的指数（即归一化差异植被指数；NDVI 和增强型植被指数；EVI）主要低估了季节开始日期（基准前 5-30 天），同时高估了季节结束日期日期（基准后 7-47 天）。对于源自 MODIS 和 SRS 传感器的指数，这些差异最大，它们具有更窄的全宽半最大光谱带宽和更锐利的定向角。

盐沼环境的独特特征和植被特性，包括死生物量，会给常用植被指数（NDVI、EVI）的解释带来挑战。结合仅使用可见波长 (VCI、GCC) 或隔离植被的近红外反射率 (NIRv) 的近端传感器的信息，可以改进盐沼中碳物候学的研究。