Moisture availability mediates the relationship between terrestrial gross primary production and solar‐induced chlorophyll fluorescence: Insights from global‐scale variations

Effective use of solar‐induced chlorophyll fluorescence (SIF) to estimate and monitor gross primary production (GPP) in terrestrial ecosystems requires a comprehensive understanding and quantification of the relationship between SIF and GPP. To date, this understanding is incomplete and somewhat controversial in the literature. Here we derived the GPP/SIF ratio from multiple data sources as a diagnostic metric to explore its global‐scale patterns of spatial variation and potential climatic dependence. We found that the growing season GPP/SIF ratio varied substantially across global land surfaces, with the highest ratios consistently found in boreal regions. Spatial variation in GPP/SIF was strongly modulated by climate variables. The most striking pattern was a consistent decrease in GPP/SIF from cold‐and‐wet climates to hot‐and‐dry climates. We propose that the reduction in GPP/SIF with decreasing moisture availability may be related to stomatal responses to aridity. Furthermore, we show that GPP/SIF can be empirically modeled from climate variables using a machine learning (random forest) framework, which can improve the modeling of ecosystem production and quantify its uncertainty in global terrestrial biosphere models. Our results point to the need for targeted field and experimental studies to better understand the patterns observed and to improve the modeling of the relationship between SIF and GPP over broad scales

Global analysis of the relationship between reconstructed solar induced chlorophyll fluorescence (SIF) and gross primary production (GPP)

Solar-induced chlorophyll fluorescence (SIF) is increasingly known as an effective proxy for plant photosynthesis, and therefore, has great potential in monitoring gross primary production (GPP). However, the relationship between SIF and GPP remains highly uncertain across space and time. Here, we analyzed the SIF (reconstructed, SIFc)–GPP relationships and their spatiotemporal variability, using GPP estimates from FLUXNET2015 and two spatiotemporally contiguous SIFc datasets (CSIF and GOSIF). The results showed that SIFc had significant positive correlations with GPP at the spatiotemporal scales investigated (p p p > 0.05). Therefore, we propose a two-slope scheme to differentiate ENF from non-ENF biome and synopsize spatiotemporal variability of the GPP/SIFc slope. The relative biases were 7.14% and 11.06% in the estimated cumulative GPP across all EC towers, respectively, for GOSIF and CSIF using a two-slope scheme. The significantly higher GPP/SIFc slopes of the ENF biome in the two-slope scheme are intriguing and deserve further study. In addition, there was still considerable dispersion in the comparisons of CSIF/GOSIF and GPP at both site and biome levels, calling for discriminatory analysis backed by higher spatial resolution to systematically address issues related to landscape heterogeneity and mismatch between SIFc pixel and the footprints of flux towers and their impacts on the SIF–GPP relationship

Synergistic use of SMAP and OCO-2 data in assessing the responses of ecosystem productivity to the 2018 U.S. drought

Soil moisture and gross primary productivity (GPP) estimates from the Soil Moisture Active Passive (SMAP) and solar-induced chlorophyll fluorescence (SIF) from the Orbiting Carbon Observatory-2 (OCO-2) provide new opportunities for understanding the relationship between soil moisture and terrestrial photosynthesis over large regions. Here we explored the potential of the synergistic use of SMAP and OCO-2 based data for monitoring the responses of ecosystem productivity to drought. We used complementary observational information on root-zone soil moisture and GPP (9 km) from SMAP and fine-resolution SIF (0.05°; GOSIF) derived from OCO-2 SIF soundings. We compared the spatial pattern and temporal evolution of anomalies of these variables over the conterminous U.S. during the 2018 drought, and examined to what extent they could characterize the drought-induced variations of flux tower GPP and crop yield data. Our results showed that SMAP GPP and GOSIF, both freely available online, could well capture the spatial extent and dynamics of the impacts of drought indicated by the U.S. Drought Monitor maps and the SMAP root-zone soil moisture deficit. Over the U.S. Southwest, monthly anomalies of soil moisture showed significant positive correlations with those of SMAP GPP (R² = 0.44, p < 0.001) and GOSIF (R² = 0.76, p < 0.001), demonstrating strong water availability constraints on plant productivity across dryland ecosystems. We further found that SMAP GPP and GOSIF captured the impact of drought on tower GPP and crop yield. Our results suggest that synergistic use of SMAP and OCO-2 data products can reveal the drought evolution and its impact on ecosystem productivity and carbon uptake at multiple spatial and temporal scales, and demonstrate the value of SMAP and OCO-2 for studying ecosystem function, carbon cycling, and climate change

Synergistic use of SMAP and OCO-2 data in assessing the responses of ecosystem productivity to the 2018 U.S. drought

Soil moisture and gross primary productivity (GPP) estimates from the Soil Moisture Active Passive (SMAP) and solar-induced chlorophyll fluorescence (SIF) from the Orbiting Carbon Observatory-2 (OCO-2) provide new opportunities for understanding the relationship between soil moisture and terrestrial photosynthesis over large regions. Here we explored the potential of the synergistic use of SMAP and OCO-2 based data for monitoring the responses of ecosystem productivity to drought. We used complementary observational information on root-zone soil moisture and GPP (9 km) from SMAP and fine-resolution SIF (0.05°; GOSIF) derived from OCO-2 SIF soundings. We compared the spatial pattern and temporal evolution of anomalies of these variables over the conterminous U.S. during the 2018 drought, and examined to what extent they could characterize the drought-induced variations of flux tower GPP and crop yield data. Our results showed that SMAP GPP and GOSIF, both freely available online, could well capture the spatial extent and dynamics of the impacts of drought indicated by the U.S. Drought Monitor maps and the SMAP root-zone soil moisture deficit. Over the U.S. Southwest, monthly anomalies of soil moisture showed significant positive correlations with those of SMAP GPP (R² = 0.44, p < 0.001) and GOSIF (R² = 0.76, p < 0.001), demonstrating strong water availability constraints on plant productivity across dryland ecosystems. We further found that SMAP GPP and GOSIF captured the impact of drought on tower GPP and crop yield. Our results suggest that synergistic use of SMAP and OCO-2 data products can reveal the drought evolution and its impact on ecosystem productivity and carbon uptake at multiple spatial and temporal scales, and demonstrate the value of SMAP and OCO-2 for studying ecosystem function, carbon cycling, and climate change

A spatially downscaled sun-induced fluorescence global product for enhanced monitoring of vegetation productivity

Sun-induced chlorophyll fluorescence (SIF) retrieved from satellite spectrometers can be a highly valuable proxy for photosynthesis. The SIF signal is very small and notoriously difficult to measure, requiring sub-nanometre spectral-resolution measurements, which to date are only available from atmospheric spectrometers sampling at low spatial resolution. For example, the widely used SIF dataset derived from the GOME-2 mission is typically provided in 0.5∘ composites. This paper presents a new SIF dataset based on GOME-2 satellite observations with an enhanced spatial resolution of 0.05∘ and an 8 d time step covering the period 2007–2018. It leverages on a proven methodology that relies on using a light-use efficiency (LUE) modelling approach to establish a semi-empirical relationship between SIF and various explanatory variables derived from remote sensing at higher spatial resolution. An optimal set of explanatory variables is selected based on an independent validation with OCO-2 SIF observations, which are only sparsely available but have a high accuracy and spatial resolution. After bias correction, the resulting downscaled SIF data show high spatio-temporal agreement with the first SIF retrievals from the new TROPOMI mission, opening the path towards establishing a surrogate archive for this promising new dataset. We foresee this new SIF dataset becoming a valuable asset for Earth system science in general and for monitoring vegetation productivity in particular. The dataset is available at https://doi.org/10.2905/21935FFC-B797-4BEE-94DA-8FEC85B3F9E1 (Duveiller et al., 2019)

Higher absorbed solar radiation partly offset the negative effects of water stress on the photosynthesis of Amazon forests during the 2015 drought

Amazon forests play an important role in the global carbon cycle and Earth\u27s climate. The vulnerability of Amazon forests to drought remains highly controversial. Here we examine the impacts of the 2015 drought on the photosynthesis of Amazon forests to understand how solar radiation and precipitation jointly control forest photosynthesis during the severe drought. We use a variety of gridded vegetation and climate datasets, including solar-induced chlorophyll fluorescence (SIF), photosynthetic active radiation (PAR), the fraction of absorbed PAR (APAR), leaf area index (LAI), precipitation, soil moisture, cloud cover, and vapor pressure deficit (VPD) in our analysis. Satellite-derived SIF observations provide a direct diagnosis of plant photosynthesis from space. The decomposition of SIF to SIF yield (SIFyield) and APAR (the product of PAR and fPAR) reveals the relative effects of precipitation and solar radiation on photosynthesis. We found that the drought significantly reduced SIFyield, the emitted SIF per photon absorbed. The higher APAR resulting from lower cloud cover and higher LAI partly offset the negative effects of water stress on the photosynthesis of Amazon forests, leading to a smaller reduction in SIF than in SIFyield and precipitation. We further found that SIFyield anomalies were more sensitive to precipitation and VPD anomalies in the southern regions of the Amazon than in the central and northern regions. Our findings shed light on the relative and combined effects of precipitation and solar radiation on photosynthesis, and can improve our understanding of the responses of Amazon forests to drought

Assessing the benefit of satellite-based Solar-Induced Chlorophyll Fluorescence in crop yield prediction

Large-scale crop yield prediction is critical for early warning of food insecurity, agricultural supply chain management, and economic market. Satellite-based Solar-Induced Chlorophyll Fluorescence (SIF) products have revealed hot spots of photosynthesis over global croplands, such as in the U.S. Midwest. However, to what extent these satellite-based SIF products can enhance the performance of crop yield prediction when benchmarking against other existing satellite data remains unclear. Here we assessed the benefits of using three satellite-based SIF products in yield prediction for maize and soybean in the U.S. Midwest: gap-filled SIF from Orbiting Carbon Observatory 2 (OCO-2), new SIF retrievals from the TROPOspheric Monitoring Instrument (TROPOMI), and the coarse-resolution SIF retrievals from the Global Ozone Monitoring Experiment-2 (GOME-2). The yield prediction performances of using SIF data were benchmarked with those using satellite-based vegetation indices (VIs), including normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and near-infrared reflectance of vegetation (NIRv), and land surface temperature (LST). Five machine-learning algorithms were used to build yield prediction models with both remote-sensing-only and climate-remote-sensing-combined variables. We found that high-resolution SIF products from OCO-2 and TROPOMI outperformed coarse-resolution GOME-2 SIF product in crop yield prediction. Using high-resolution SIF products gave the best forward predictions for both maize and soybean yields in 2018, indicating the great potential of using satellite-based high-resolution SIF products for crop yield prediction. However, using currently available high-resolution SIF products did not guarantee consistently better yield prediction performances than using other satellite-based remote sensing variables in all the evaluated cases. The relative performances of using different remote sensing variables in yield prediction depended on crop types (maize or soybean), out-of-sample testing methods (five-fold-cross-validation or forward), and record length of training data. We also found that using NIRv could generally lead to better yield prediction performance than using NDVI, EVI, or LST, and using NIRv could achieve similar or even better yield prediction performance than using OCO-2 or TROPOMI SIF products. We concluded that satellite-based SIF products could be beneficial in crop yield prediction with more high-resolution and good-quality SIF products accumulated in the future

A convolutional neural network for spatial downscaling of satellite-based solar-induced chlorophyll fluorescence (SIFnet)

Gross primary productivity (GPP) is the sum of leaf photosynthesis and represents a crucial component of the global carbon cycle. Space-borne estimates of GPP typically rely on observable quantities that co-vary with GPP such as vegetation indices using reflectance measurements (e.g., NDVI, NIRv, and kNDVI). Recent work has also utilized measurements of solar-induced chlorophyll fluorescence (SIF) as a proxy for GPP. However, these SIF measurements are typically coarse resolution while many processes influencing GPP occur at fine spatial scales. Here, we develop a Convolutional Neural Network (CNN), named SIFnet, that increases the resolution of SIF from the TROPOspheric Monitoring Instrument (TROPOMI) on board of the satellite Sentinel-5P by a factor of 10 to a spatial resolution of 500 m. SIFnet utilizes coarse SIF observations together with high resolution auxiliary data. The auxiliary data used here may carry information related to GPP and SIF. We use training data from non-US regions between April 2018 until March 2021 and evaluate our CNN over the conterminous United States (CONUS). We show that SIFnet is able to increase the resolution of TROPOMI SIF by a factor of 10 with a r2 and RMSE metrics of 0.92 and 0.17 mW m&minus;2 sr&minus;1 nm&minus;1, respectively. We further compare SIFnet against a recently developed downscaling approach and evaluate both methods against independent SIF measurements from Orbiting Carbon Observatory 2 and 3 (OCO-2/3). SIFnet performs systematically better than the downscaling approach (r = 0.78 for SIFnet, r = 0.72 for downscaling), indicating that it is picking up on key features related to SIF and GPP. Examination of the feature importance in the neural network indicates a few key parameters and the spatial regions these parameters matter. Namely, the CNN finds low resolution SIF data to be the most significant parameter with the NIRv vegetation index as the second most important parameter. NIRv consistently outperforms the recently proposed kNDVI vegetation index. Advantages and limitations of SIFnet are investigated and presented through a series of case studies across the United States. SIFnet represents a robust method to infer continuous, high spatial resolution SIF data.</p