Water, Energy, and Carbon with Artificial Neural Networks (WECANN): a statistically based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence

Abstract

Abstract.A new global estimate of surface turbulent fluxes, latent heat flux (LE) and sensible heat flux (H ), and gross primary production (GPP) is developed using a machine learning approach informed by novel remotely sensed solarinduced fluorescence (SIF) and other radiative and meteorological variables. This is the first study to jointly retrieve LE, H , and GPP using SIF observations. The approach uses an artificial neural network (ANN) with a target dataset generated from three independent data sources, weighted based on a triple collocation (TC) algorithm. The new retrieval, named Water, Energy, and Carbon with Artificial Neural Networks (WECANN), provides estimates of LE, H , and GPP from 2007 to 2015 at 1 • × 1 • spatial resolution and at monthly time resolution. The quality of ANN training is assessed using the target data, and the WECANN retrievals are evaluated using eddy covariance tower estimates from the FLUXNET network across various climates and conditions. When compared to eddy covariance estimates, WECANN typically outperforms other products, particularly for sensible and latent heat fluxes. Analyzing WECANN retrievals across three extreme drought and heat wave events demonstrates the capability of the retrievals to capture the extent of these events.Uncertainty estimates of the retrievals are analyzed and the interannual variability in average global and regional fluxes shows the impact of distinct climatic events -such as the 2015 El Niño -on surface turbulent fluxes and GPP.

Reconstructed Solar‐Induced Fluorescence: A Machine Learning Vegetation Product Based on MODIS Surface Reflectance to Reproduce GOME‐2 Solar‐Induced Fluorescence

“…The highest values of annual average RSIF are observed in tropical forests, as expected, with a peak over the Amazon and Maritime Continent, similarly to more complex GPP products using more input sources, such as WECANN (Alemohammad et al, 2017). Dry to wet transitional regions are correctly characterized, such as in Portugal‐Spain, North Africa, the Sahel, the east‐west gradient in the continental United States, and in India.…”

“…A correctly fitted and nonoverfitting NN is able to generalize instead of just learning a functional relationship on existing data (Wan et al, 2013). There have been multiple successful applications of NN in the geosciences in recent years ranging from soil moisture retrievals (Jimenez et al, 2013; Kolassa et al, 2013, 2016; Kolassa, Gentine, et al, 2017; Kolassa, Reichle, & Draper, 2017) and surface temperature and emissivity retrievals (Aires et al, 2001) to surface flux retrievals (Alemohammad et al, 2017; Beer et al, 2010; Jimenez et al, 2009; Jung et al, 2009; Koirala et al, 2017). …”

“…The retrieval is available at 1° and monthly resolution, from January 2007 to present. This retrieval has been shown to outperform other retrievals, especially in terms of the representation of seasonal and interannual variability (Alemohammad et al, 2017). The WECANN data set is used as a baseline comparison for RSIF, as GOME‐2 SIF is used as input data of the neural network; hence, there is strong correlation between WECANN GPP and SIF.…”

“…Water, Energy, and Carbon with Artificial Neural Networks (WECANN) is a machine learning retrieval of GPP, which uses only remote sensing observations and in particular GOME‐2 SIF to estimate surface fluxes at the global scale (Alemohammad et al, 2017). The retrieval is available at 1° and monthly resolution, from January 2007 to present.…”

“…SIF has a small‐amplitude signal, so it was not possible to observe it until very recently. SIF observations have been shown to be directly pertinent to estimate crop photosynthesis (Guanter et al, 2014) and yield (Guan et al, 2016), GPP across ecosystems (Lee et al, 2015; Yang et al, 2015; Zhang, Xiao, Jin, et al, 2016), water stress (Guan et al, 2015; Konings et al, 2017; Sun et al, 2015; Zhang, Xiao, Guanter, et al, 2016), biosphere‐atmosphere interactions (Green et al, 2017), surface turbulent fluxes (Alemohammad et al, 2017), and phenology, especially in northern latitudes where vegetation indices and their seasonality are polluted by the snow albedo (Jeong et al, 2017). One other advantage of SIF is that it responds to only the PAR absorbed by chlorophyll of the canopy, whereas typical optical (absorbed photosynthetic active radiation) APAR or fPAR products reflect the PAR absorbed by the entire canopy (nonphotosynthetic and photosynthetic; Song et al, 2013).…”