No abstract

Satellite remote sensing provides unmatched spatiotemporal information on vegetation gross primary productivity (GPP). Yet understanding of the relationship between GPP and remote sensing observations and how it changes with factors such as scale, biophysical constraint, and vegetation type remains limited. This knowledge gap is especially apparent for dryland ecosystems, which have characteristic high spatiotemporal variability and are under‐represented by long‐term field measurements. Here we utilize an eddy covariance (EC) data synthesis for southwestern North America in an assessment of how accurately satellite‐derived vegetation proxies capture seasonal to interannual GPP dynamics across dryland gradients. We evaluate the enhanced vegetation index, solar‐induced fluorescence (SIF), and the photochemical reflectivity index. We find evidence that SIF is more accurately capturing seasonal GPP dynamics particularly for evergreen‐dominated EC sites and more accurately estimating the full magnitude of interannual GPP dynamics for all dryland EC sites. These results suggest that incorporation of SIF could significantly improve satellite‐based GPP estimates.

Abstract. In recent years, China's terrestrial ecosystems have experienced frequent droughts. How these droughts have affected carbon sequestration by the terrestrial ecosystems is still unclear. In this study, the process-based Boreal Ecosystem Productivity Simulator (BEPS) model, driven by remotely sensed vegetation parameters, was employed to assess the effects of droughts on net ecosystem productivity (NEP) of terrestrial ecosystems in China from 2000 to 2011. Droughts of differing severity, as indicated by a standard precipitation index (SPI), hit terrestrial ecosystems in China extensively in 2001, 2006, 2009, and 2011. The national total annual NEP exhibited the slight decline of −11.3 Tg C yr−2 during the aforementioned years of extensive droughts. The NEP reduction ranged from 61.1 Tg C yr−1 to 168.8 Tg C yr−1. National and regional total NEP anomalies were correlated with the annual mean SPI, especially in Northwest China, North China, Central China, and Southwest China. The reductions in annual NEP in 2001 and 2011 might have been caused by a larger decrease in annual gross primary productivity (GPP) than in annual ecosystem respiration (ER). The reductions experienced in 2009 might be due to a decrease in annual GPP and an increase in annual ER, while reductions in 2006 could stem from a larger increase in ER than in GPP. The effects of droughts on NEP lagged up to 3–6 months, due to different responses of GPP and ER. In eastern China, where is humid and warm, droughts have predominant and short-term lagged influences on NEP. In western regions, cold and arid, the drought effects on NEP were relatively weaker but prone to lasting longer.

Accurate crop yield assessments using satellite remote sensing-based methods are of interest for regional monitoring and the design of policies that promote agricultural resiliency and food security. However, the application of current vegetation productivity algorithms derived from global satellite observations is generally too coarse to capture cropland heterogeneity. The fusion of data from different sensors can provide enhanced information and overcome many of the limitations of individual sensors. In thitables study, we estimate annual crop yields for seven important crop types across Montana in the continental USA from 2008-2015, including alfalfa, barley, maize, peas, durum wheat, spring wheat and winter wheat. We used a satellite data-driven light use efficiency (LUE) model to estimate gross primary productivity (GPP) over croplands at 30-m spatial resolution and eight-day time steps using a fused NDVI dataset constructed by blending Landsat (5 or 7) and Terra MODIS reflectance data. The fused 30-m NDVI record showed good consistency with the original Landsat and MODIS data, but provides better spatiotemporal delineations of cropland vegetation growth. Crop yields were estimated at 30-m resolution as the product of estimated GPP accumulated over the growing season and a crop-specific harvest index (HI GPP). The resulting GPP estimates capture characteristic cropland productivity patterns and seasonal variations, while the estimated annual crop production results correspond favorably with reported county-level crop production data (r = 0.96, relative RMSE = 37.0%, p < 0.05) from the U.S. Department of Agriculture (USDA). The performance of estimated crop yields at a finer (field) scale was generally lower, but still meaningful (r = 0.42, relative RMSE = 50.8%, p < 0.05). Our methods and results are suitable for operational applications of crop yield monitoring at regional scales, suggesting the potential of using global satellite observations to improve agricultural management, policy decisions and regional/global food security.

Satellite remote sensing provides continuous observations of vegetation properties that can be used to estimate global terrestrial ecosystem gross primary production (GPP). The Photochemical Reflectance Index (PRI) has been shown to be sensitive to vegetation photosynthetic light use efficiency (LUE), GPP and canopy water-stress. Here, we use the NASA EOS MODIS (Moderate Resolution Imaging Spectroradiometer) based PRI with eddy covariance CO 2 flux measurements and meteorological observations from 20 tower sites representing major plant functional type (PFT) classes within the continental USA (CONUS) to assess GPP sensitivity to soil moisture related water stress. The sPRI (scaled PRI) metric derived using MODIS band 13 as a reference channel (sPRI 13) shows generally higher correspondence with tower GPP observations than other potential MODIS reference bands. The sPRI 13 observations were used as a proxy for soil moisture related water supply constraints to LUE within a satellite data driven terrestrial carbon flux model to estimate GPP (GPP PRI). The GPP PRI calculations show generally favorable correspondence with tower GPP observations (0.457 ≤ R 2 ≤ 0.818), except for lower GPP PRI performance over evergreen needleleaf forest (ENF) sites. A regional model sensitivity analysis using the sPRI 13 as a water supply proxy indicated that water restrictions limit GPP over more than 21% of the CONUS domain, particularly in drier climate areas where atmospheric moisture (VPD) deficits alone are insufficient to represent both atmosphere demand and water supply controls affecting productivity. Our results indicate strong potential of the MODIS sPRI 13 to represent soil moisture related water supply controls influencing photosynthesis, with enhanced (1-km resolution) delineation of these processes closer to the scale of in situ tower observations. These observations may provide an effective tool for characterizing sub-grid spatial heterogeneity in soil moisture related controls that inform coarser scale observations and estimates determined from other satellite observations and global carbon, and climate models.

No abstract

Light use efficiency (LUE) models are widely used to estimate gross primary productivity (GPP), a dominant component of the terrestrial carbon cycle. Their outputs are very sensitive to LUE. Proper determination of this parameter is a prerequisite for LUE models to simulate GPP at regional and global scales. This study was devoted to investigating the ability of the photochemical reflectance index (PRI) to track LUE variations for a sub-tropical planted coniferous forest in southern China using tower-based PRI and GPP measurements over the period from day 101 to 275 in 2013. Both half-hourly PRI and LUE exhibited detectable diurnal and seasonal variations, and decreased with increases of vapor pressure deficit (VPD), air temperature (Ta), and photosynthetically active radiation (PAR). Generally, PRI is able to capture diurnal and seasonal changes in LUE. However, correlations of PRI with LUE varied dramatically throughout the growing season. The correlation was the strongest (R 2 = 0.6427, p < 0.001) in July and the poorest in May. Over the entire growing season, PRI relates better to LUE under clear or partially cloudy skies (clearness index, CI > 0.3) with moderate to high VPD (>20 hPa) and high temperatures (>31˝C). Overall, we found that PRI is most sensitive to variations in LUE under stressed conditions, and the sensitivity decreases as the growing conditions become favorable when atmosphere water vapor, temperature and soil moisture are near the optimum conditions.

Forests are crucial terrestrial ecosystems. Their leaf area index (LAI) is a key parameter determining the exchange of matter and energy between the atmosphere and the ground surface. In this study, MOD 09A1 and MCD 43A1 data were input into an inversion model based on the 4-scale geometric optical model to retrieve 8-d 500 m LAI products in China during the period 2000 to 2010. The resulting LAI product was validated using LAI measured in 6 typical areas. The spatial and temporal variations of forest LAI and its relationships with temperature and precipitation were analyzed. The results show that the accuracy of the 500 m LAI product was above 70% in the 6 typical areas, indicating the reliability of this product.