The response of vegetation physiology to drought at large spatial scales is poorly understood due to a lack of direct observations. Here, we study vegetation drought responses related to photosynthesis, evaporation, and vegetation water content using remotely sensed data, and we isolate physiological responses using a machine learning technique. We find that vegetation functional decreases are largely driven by the downregulation of vegetation physiology such as stomatal conductance and light use efficiency, with the strongest downregulation in water-limited regions. Vegetation physiological decreases in wet regions also result in a discrepancy between functional and structural changes under severe drought. We find similar patterns of physiological drought response using simulations from a soil–plant–atmosphere continuum model coupled with a radiative transfer model. Observation-derived vegetation physiological responses to drought across space are mainly controlled by aridity and additionally modulated by abnormal hydro-meteorological conditions and vegetation types. Hence, isolating and quantifying vegetation physiological responses to drought enables a better understanding of ecosystem biogeochemical and biophysical feedback in modulating climate change.

Remotely sensed solar‐induced fluorescence (SIF) has emerged as a novel and powerful approach for terrestrial vegetation monitoring. Continuous measurements of SIF in synergy with concurrent eddy covariance (EC) flux measurements can provide a new opportunity to advance terrestrial ecosystem science. Here, we introduce a network of ground‐based continuous SIF observations at flux tower sites across the mainland China referred to as ChinaSpec. The network consists of 16 tower sites until 2019 including six cropland sites, four grassland sites, four forest sites, and two wetland sites. An automated SIF system was deployed at each of these sites to collect continuous high‐resolution spectra for high‐frequency SIF retrievals in synergy with EC flux measurements. The goal of ChinaSpec is to provide long‐term ground‐based SIF measurements and promote the collaborations between optical remote sensing and EC flux observation communities in China. We present here the details of instrument specifications, data collection and processing procedures, data sharing and utilization protocols, and future plans. Furthermore, we show the examples how ground‐based SIF observations can be used to track vegetation photosynthesis from diurnal to seasonal scales, and to assist in the validation of fluorescence models and satellite SIF products (e.g., from OCO‐2 and TROPOMI) with the measurements from these sites since 2016. This network of SIF observations could improve our understanding of the controls on the biosphere‐atmosphere carbon exchange and enable the improvement of carbon flux predictions. It will also help integrate ground‐based SIF measurements with EC flux networks which will advance ecosystem and carbon cycle researches globally.

The PROSPECT model has been widely used to estimate leaf biochemical constituents, but retrieval of leaf mass per area (LMA) in fresh leaves has proved to be difficult due to the predominant water absorption in the infrared spectral region. At wavelengths where water absorption is low, both LMA absorption and light scattering are relatively high. Therefore, the uncertainty in scattering simulation at these wavelengths will lead to a relatively large error in LMA estimation. In this paper, we introduce a wavelength-independent factor to represent the first-order effect of anisotropic scattering in the elementary layer in the modified model PROSPECT-g, aiming at appropriately simulating leaf optical properties in spectral regions with high scattering and thus reducing the uncertainty in LMA estimation. In order to avoid introducing a new variable to be retrieved in model inversion, this factor is an intermediate variable derived from measured near infrared region spectral data and other existing model parameters. Results show that about 30%-40% of the tested samples are well simulated using PROSPECT-5, while for the rest of the samples simulation is greatly improved with PROSPECT-g. Leaf reflectance and transmittance reconstructions using PROSPECT-g are improved, especially at wavelengths with high scattering such as 750-1400 and 1500-1850 nm. LMA retrieval is significantly improved, with the average root-mean-square error decreasing from 38.7 (PROSPECT-5) to 16.6 g/m² (PROSPECT-g) for 628 leaves after considering anisotropic scattering in the elementary layer. Improvements are particularly noticeable for leaves with extremely high LMA contents.

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 (R2 = 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.

Topographic correction methods rarely consider the canopy parameter effects directly and explicitly for sloping canopies. In order to address this problem, the topographic correction method MFM-GOST2 was developed by implementing the second version of the Geometric-Optical model for Sloping Terrains (the GOST2 model) in the multiple forward mode (MFM) inversion framework. First, a look up table (LUT) was constructed by multiple forward modeling of the GOST2 model; second, the radiance of a remotely sensed image and its corresponding topographic data were used for searching potential canopy parameter combinations from the LUT; and third, the corrected radiance was determined by averaging potential radiances of horizontal canopies from the LUT according to the canopy parameter combinations. The MFM-GOST2 and twelve generally used topographic correction methods were evaluated via a case study by visual analysis, linear relationship analysis, and the rose diagram analysis. The result showed that the MFM-GOST2 method successfully removed most of the topographic effects of a subset image of the Landsat-8 image in a case study. The case study also illustrates that the rose diagram analysis is a good way to evaluate topographic corrections, but the linear relationship analysis cannot be used independently for the evaluations because the decorrelation is not a sufficient condition to determine a successful topographic correction.

During recent decades, solar-induced chlorophyll fluorescence (SIF) has shown to be a good proxy for gross primary production (GPP), promoting the development of ground-based SIF observation systems and supporting a greater understanding of the relationship between SIF and GPP. However, it is unclear whether such SIF-oriented observation systems built from different materials and of different configurations are able to acquire consistent SIF signals from the same target. In this study, we used four different observation systems to measure the same targets together in order to investigate whether SIF from different systems is comparable. Integration time (IT), reflectance, and SIF retrieved from different systems with hemispherical-conical (hemi-con) and bi-hemispherical (bi-hemi) configurations were also evaluated. A newly built prism system (SIFprism, using prism to collect both solar and target radiation) has the shortest IT and highest signal to noise ratio (SNR). Reflectance collected from the different systems showed small differences, and the diurnal patterns of both red and far-red SIF derived from different systems showed a marginal difference when measuring the homogeneous vegetation canopy (grassland). However, when the target is heterogeneous, e.g., the Epipremnum aureum canopy, the values and diurnal pattern of far-red SIF derived from systems with a bi-hemi configuration were obviously different with those derived from the system with hemi-con configuration. These results demonstrate that different SIF systems are able to acquire consistent SIF for landscapes with a homogeneous canopy. However, SIF retrieved from bi-hemi and hemi-con configurations may be distinctive when the target is a heterogeneous (or discontinuous) canopy due to the different fields of view and viewing geometries. Our findings suggest that the bi-hemi configuration has an advantage to measure heterogeneous canopies due to the large field of view for upwelling sensors being representative for the footprint of the eddy covariance flux measurements.