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A shifting phenology in deciduous broadleaf forests (DBFs) can indicate forest health, resilience, and changes in the face of a rapidly changing climate. The availability of satellite-based solar-induced fluorescence (SIF) from the Orbiting Carbon Observatory-2 (OCO-2) promises to add to the understanding of the regional-level DBF phenology that has been developed, for instance, using proxies of gross primary productivity (GPP) from the Moderate Imaging Spectroradiometer (MODIS). It is unclear how OCO-2 and MODIS metrics compare in terms of capturing intra-annual variations and benchmarking DBF seasonality, thus necessitating a comparison. In this study, spatiotemporally matched OCO-2 SIF metrics (at footprint level) and corresponding MODIS GPP, normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI) products within a temperate DBF were used to compare the phenology captured by the productivity metrics. Additionally, an estimate of the SIF yield (SIFy), derived from OCO-2 SIF measurements, and a MODIS fraction of photosynthetically active radiation (fPAR) were tested. An examination of the trends and correlations showed relatively few qualitative differences among productivity metrics and environmental variables, but it highlighted a lack of seasonal signal in the calculation of SIFy. However, a seasonality analysis quantitatively showed similar seasonal timings and levels of seasonal production in and out of the growing season between SIF and GPP. In contrast, NDVI seasonality was least comparable to that of SIF and GPP, with senescence occurring approximately one month apart. Taken together, we conclude that satellite-based SIF and GPP (and EVI to a smaller degree) provide the most similar measurements of forest function, while NDVI is not sensitive to the same changes. In this regard, phenological metrics calculated with satellite-based SIF, along with those calculated with GPP and EVI from MODIS, can enhance our current understanding of deciduous forest structures and functions and provide additional information over NDVI. We recommend that future studies consider metrics other than NDVI for phenology analyses.

This paper proposes a novel approach for hydrometeor classification using passive microwave observations. The use of passive measurements for this purpose has not been extensively explored, despite being available for over four decades. We utilize the Micro-Wave Humidity Sounder-2 (MWHS-2) to relate microwave brightness temperatures to hydrometeor types derived from the global precipitation measurement’s (GPM) dual-frequency precipitation radar (DPR), which are classified into liquid, mixed, and ice phases. To achieve this, we utilize a convolutional neural network model with an attention mechanism that learns feature representations of MWHS-2 observations from spatial and temporal dimensions. The proposed algorithm classified hydrometeors with 84.7% accuracy using testing data and captured the geographical characteristics of hydrometeor types well in most areas, especially for frozen precipitation. We then evaluated our results by comparing predictions from a different year against DPR retrievals seasonally and globally. Our global annual cycles of precipitation occurrences largely agreed with DPR retrievals with biases being 8.4%, −11.8%, and 3.4%, respectively. Our approach provides a promising direction for utilizing passive microwave observations and deep-learning techniques in hydrometeor classification, with potential applications in the time-resolved observations of precipitation structure and storm intensity with a constellation of smallsats (TROPICS) algorithm development.

Remote sensing offers a non-destructive method to detect plant physiological response to the environment by measuring chlorophyll fluorescence (CF). Most methods to estimate CF require relatively complex retrieval, spectral fitting, or modelling methods. An investigation was undertaken to evaluate measurements of CF using a relatively straightforward technique to detect and monitor plant stress with a spectroradiometer and blue-red light emitting diode (LED). CF spectral response of tomato plants treated with a photosystem inhibitor were assessed and compared to traditional reflectance-based indices: normalized difference vegetation index (NDVI) and photochemical reflectance index (PRI). The blue-red LEDs provided input irradiance and a “window” in the CF emission range of plants (~650 to 850 nm) sufficient to capture distinctive “two-peak” spectra and to distinguish plant health from day to day of the experiment, while within day differences were noisy. CF-based metrics calculated from CF spectra clearly captured signs of vegetation stress earlier than reflectance-based indices and by visual inspection. This CF monitoring technique is a flexible and scalable option for collecting plant function data, especially for indicating early signs of stress. The technique can be applied to a single plant or larger canopies using LED in dark conditions by an individual, or a manned or unmanned vehicle for agricultural or military purposes.

CloudSat is often the only measurement of snowfall rate available at high latitudes, making it a valuable tool for understanding snow climatology. The capability of CloudSat to provide information on seasonal and subseasonal time scales, however, has yet to be explored. In this study, we use subsampled reanalysis estimates to predict the uncertainties of CloudSat snow water equivalent (SWE) accumulation measurements at various space and time resolutions. An idealized/simulated subsampling model predicts that CloudSat may provide seasonal SWE estimates with median percent errors below 50% at spatial scales as small as 2° × 2°. By converting these predictions to percent differences, we can evaluate CloudSat snowfall accumulations against a blend of gridded SWE measurements during frozen time periods. Our predictions are in good agreement with results. The 25th, 50th, and 75th percentiles of the percent differences between the two measurements all match predicted values within eight percentage points. We interpret these results to suggest that CloudSat snowfall estimates are in sufficient agreement with other, thoroughly vetted, gridded SWE products. This implies that CloudSat may provide useful estimates of snow accumulation over remote regions within seasonal time scales.

The long-term stability of microwave radiometers (MWR) on-board altimetry missions is critical to reduce the uncertainty on the global mean sea level estimate. Harmonization and homogenization steps are applied to MWR observations in that perspective. The Sentinel-3 tandem phase provides a unique opportunity to quantify the uncertainties on the “zero-bias line” homogenization approach defined by Bennartz et al. (2020). Initially developed to improve the performance of the wet tropospheric correction retrieval, it is used here to provide a common reference for the inter-calibration between Sentinel-3A and Sentinel-3B MWR. A simplified version of the “zero-bias line” approach, a linear correction depending on brightness temperatures, allows to strongly reduce the bias between the two radiometers for both channels (about 0.5 K) and the standard deviation of the difference (0.3 K). The full version of the approach adding a dependency on wind speed has improved the quality of the WTC retrieval (Bennartz et al. 2020) but degrades the performance of the homogenization. It is thus recommended to apply the simplified version of this approach in the processing of fundamental data record. The quantification of the uncertainties on the homogenization approach is only possible due to the ideal configuration of the Sentinel-3 tandem phase. The same dataset and the same metrics could be used to assess other approaches.

Solar-induced fluorescence (SIF) has been empirically linked to gross primary productivity (GPP) in multiple ecosystems and is thus a promising tool to address the current uncertainties in carbon fluxes at ecosystem to continental scales. However, studies utilizing satellite-measured SIF in South America have concentrated on the Amazonian tropical forest, while SIF in other regions and vegetation classes remain uninvestigated. We examined three years of Orbiting Carbon Observatory-2 (OCO-2) SIF data for vegetation classes within and across the six Brazilian biomes (Amazon, Atlantic Forest, Caatinga, Cerrado, Pampa, and Pantanal) to answer the following: (1) how does satellite-measured SIF differ? (2) What is the relationship (strength and direction) of satellite-measured SIF with canopy temperature (Tcan), air temperature (Tair), and vapor pressure deficit (VPD)? (3) How does the phenology of satellite-measured SIF (duration and amplitude of seasonal integrated SIF) compare? Our analysis shows that OCO-2 captures a significantly higher mean SIF with lower variability in the Amazon and lower mean SIF with higher variability in the Caatinga compared to other biomes. OCO-2 also distinguishes the mean SIF of vegetation types within biomes, showing that evergreen broadleaf (EBF) mean SIF is significantly higher than other vegetation classes (deciduous broadleaf (DBF), grassland (GRA), savannas (SAV), and woody savannas (WSAV)) in all biomes. We show that the strengths and directions of correlations of OCO-2 mean SIF to Tcan, Tair, and VPD largely cluster by biome: negative in the Caatinga and Cerrado, positive in the Pampa, and no correlations were found in the Pantanal, while results were mixed for the Amazon and Atlantic Forest. We found mean SIF most strongly correlated with VPD in most vegetation classes in most biomes, followed by Tcan. Seasonality from time series analysis reveals that OCO-2 SIF measurements capture important differences in the seasonal timing of SIF for different classes, details masked when only examining mean SIF differences. We found that OCO-2 captured the highest base integrated SIF and lowest seasonal pulse integrated SIF in the Amazon for all vegetation classes, indicating continuous photosynthetic activity in the Amazon exceeds other biomes, but with small seasonal increases. Surprisingly, Pantanal EBF SIF had the highest total integrated SIF of all classes in all biomes due to a large seasonal pulse. Additionally, the length of seasons only accounts for about 30% of variability in total integrated SIF; thus, integrated SIF is likely captures differences in photosynthetic activity separate from structural differences. Our results show that satellite measurements of SIF can distinguish important functioning and phenological differences in vegetation classes and thus has the potential to improve our understanding of productivity and seasonality in the tropics.

Medium Spectral Resolution Imaging Spectrometer (MERIS) oxygen A band measurements were used as a proxy for the Earth Polychromatic Imaging Camera (EPIC),to be launched on NASA’s Deep Space Climate Observatory (DSCOVR). The high spatial resolution of MERIS (1 × 1 km2) is exploited to study the effects of subscale spatialheterogeneity of clouds on the cloud-top pressure retrieved at the coarser spatial resolutionof EPIC (10 × 10 km2). In general, for a sub-scale cloud fraction less than 1, a shift of cloud-top pressure toward the middle atmosphere is found, with a low-bias for highclouds and a high-bias for low clouds. In addition, the deviation is found to be a function of surface reflectance. The subscale variability of fully clouded EPIC pixels causes a weak underestimation of cloud-top pressure, when compared to averaged high-resolution retrievals.

To date, a large variety of water vapour data records from satellite and reanalysis are available. It is key to understand the quality and uncertainty of these data records in order to fully exploit these records and to avoid data being employed incorrectly or misinterpreted. Therefore, it is important to inform users on accuracy and limitations of these data records based on consistent inter-comparisons carried out in the framework of international assessments. Addressing this challenge is the major objective of the Global Water and Energy Exchanges (GEWEX) water vapor assessment (G-VAP) which was initiated by the GEWEX Data and Assessments Panel (GDAP). Here, an overview of G-VAP objectives and an introduction to the results from G-VAP’s first phase are given. After this overview, a summary of available data records on water vapour and closely related variables and a short introduction to the utilized methods are presented. The results from inter-comparisons, homogeneity testing and inter-comparison of trend estimates, achieved within G-VAP’s first phase are summarized. The conclusions on future research directions for the wider community and for G-VAP’s next phase are outlined and recommendations have been formulated. For instance, a key recommendation is the need for recalibration and improved inter-calibration of radiance data records and subsequent reprocessing in order to increase stability and to provide uncertainty estimates. This need became evident from a general disagreement in trend estimates (e.g., trends in TCWV ranging from −1.51 ± 0.17 kg/m2/decade to 1.22 ± 0.16 kg/m2/decade) and the presence of break points on global and regional scale. It will be a future activity of G-VAP to reassess the stability of updated or new data records and to assess consistency, i.e., the closeness of data records given their uncertainty estimates.