Search NASA⌕ Search

Engineering topics

Jing M Chen

Publications and source records attributed to Jing M Chen.

Diverse Photosynthetic Capacity of Global Ecosystems Mapped by Satellite Chlorophyll Fluorescence Measurements

Photosynthetic capacity is often quantified by the Rubisco-limited photosynthetic capacity (i.e. maximum carboxylation rate, V(sub cmax)). It is a key plant functional trait that is widely used in Earth System Models for simulation of the global carbon and water cycles. Measuring V(sub cmax) is time-consuming and laborious; therefore, the spatiotemporal distribution of V(sub cmax) is still poorly understood due to limited measurements of V(sub cmax). In this study, we used a data assimilation approach to map the spatial variation of V(sub cmax) for global terrestrial ecosystems from a 11-year-long satellite-observed solar-induced chlorophyll fluorescence (SIF) record. In this SIF-derived V(sub cmax) map, the mean V(sub cmax) value for each plant function type (PFT) is found to be comparable to a widely used N-derived V(sub cmax) dataset by Kattge et al. (2009). The gradient of V(sub cmax) along PFTs is clearly revealed even without land cover information as an input. Large seasonal and spatial variations of V(sub cmax) are found within each PFT, especially for diverse crop rotation systems. The distribution of major crop belts, characterized with high V(sub cmax) values, is highlighted in this V(sub cmax) map. Legume plants are characterized with high V(sub cmax) values. This V(sub cmax) map also clearly illustrates the emerging soybean revolution in South America where V(sub cmax) is the highest among the world. The gradient of V(sub cmax) in Amazon is found to follow the transition of soil types with different soil N and P contents. This study suggests that satellite-observed SIF is powerful in deriving the important plant functional trait, i.e. V(sub cmax), for global climate change studies.

Liming He↗

A Method for Improving Hotspot Directional Signatures in BRDF Models Used for MODIS

The semi-empirical, kernel-driven, linear RossThick-LiSparseReciprocal (RTLSR) Bidirectional Reflectance Distribution Function (BRDF) model is used to generate the routine MODIS BRDFAlbedo product due to its global applicability and the underlying physics. A challenge of this model in regard to surface reflectance anisotropy effects comes from its underestimation of the directional reflectance signatures near the Sun illumination direction; also known as the hotspot effect. In this study, a method has been developed for improving the ability of the RTLSR model to simulate the magnitude and width of the hotspot effect. The method corrects the volumetric scattering component of the RTLSR model using an exponential approximation of a physical hotspot kernel, which recreates the hotspot magnitude and width using two free parameters (C(sub 1) and C(sub 2), respectively). The approach allows one to reconstruct, with reasonable accuracy, the hotspot effect by adjusting or using the prior values of these two hotspot variables. Our results demonstrate that: (1) significant improvements in capturing hotspot effect can be made to this method by using the inverted hotspot parameters; (2) the reciprocal nature allow this method to be more adaptive for simulating the hotspot height and width with high accuracy, especially in cases where hotspot signatures are available; and (3) while the new approach is consistent with the heritage RTLSR model inversion used to estimate intrinsic narrowband and broadband albedos, it presents some differences for vegetation clumping index (CI) retrievals. With the hotspot-related model parameters determined a priori, this method offers improved performance for various ecological remote sensing applications; including the estimation of canopy structure parameters.

airborne measurements↗