Removal of Chlorophyll-a Spectral Interference for Improved Phycocyanin Estimation from Remote Sensing Reflectance

Monitoring cyanobacteria is an essential step for the development of environmental and public health policies. While traditional monitoring methods rely on collection and analysis of water samples, remote sensing techniques have been used to capture their spatial and temporal dynamics. Remote detection of cyanobacteria is commonly based on the absorption of phycocyanin (PC), a unique pigment of freshwater cyanobacteria, at 620 nm. However, other photosynthetic pigments can contribute to absorption at 620 nm, interfering with the remote estimation of PC. To surpass this issue, we present a remote sensing algorithm in which the contribution of chlorophyll-a (chl-a) absorption at 620 nm is removed. To do this, we determine the PC contribution to the absorption at 665 nm and chl-a contribution to the absorption at 620 nm based on empirical relationships established using chl-a and PC standards. The proposed algorithm was compared with semi-empirical and semi-analytical remote sensing algorithms for proximal and simulated satellite sensor datasets from three central Indiana reservoirs (total of 544 sampling points). The proposed algorithm outperformed semi-empirical algorithms with root mean square error (RMSE) lower than 25 µg/L for the three analyzed reservoirs and showed similar performance to a semi-analytical algorithm. However, the proposed remote sensing algorithm has a simple mathematical structure, it can be applied at ease and make it possible to improve spectral estimation of phycocyanin from space. Additionally, the proposed showed little influence from the package effect of cyanobacteria cells.

Remote-sensing algorithm for surface evapotranspiration considering landscape and statistical effects on mixed-pixels

Evapotranspiration (ET) plays an important role in surface-atmosphere interactions. Remote sensing has long been identified as a technology that is capable of monitoring ET. However, spatial problems greatly affect the accuracy of ET retrievals by satellite. The objective of this paper is to reduce the spatial-scale uncertainty produced by surface heterogeneity using Chinese HJ-1B data. Two upscaling schemes with area-weighting aggregation for different steps and variables were applied. One scheme is input parameter upscaling (IPUS), which refers to parameter aggregation, and the other is temperature sharpening and flux aggregation (TSFA). Footprint validation results show that TSFA is more accurate and less uncertain than IPUS, and additional analysis shows that TSFA can capture land surface heterogeneities and integrate the effect of overlooked land types in the mixed pixel.