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Forest biomass estimation at large scale is challenging and generally entails large uncertainty in tropical regions. With their wall-to-wall coverage ability, passive remote sensing signals are frequently used to extrapolate field estimates of forest aboveground biomass (AGB). However, studies often use limited reference data and/or flawed validation schemes and thus report unreliable extrapolation error estimates. Here, we compared the ability of three medium- to high-resolution passive satellite sensors, Landsat-8 (L8), Sentinel-2B (S2) and Worldview-3 (WV3), to map AGB in a forest landscape of Thailand. We used a large airborne LiDAR-derived AGB dataset as a reference to train and validate a random forest algorithm and conducted robust error assessments and variable selection using spatialized cross-validations. Our results indicate that the selected predictors strongly varied among the three sensors and between analyses were restricted to low (≤200 Mg ha−1) and high (>200 Mg ha−1) AGB areas. WV3 and S2 data outperformed L8 data to extrapolate AGB (RMSE of 68 and 72 against 84 Mg ha−1, respectively) due to the inclusion of the red-edge band and, probably, to their higher spatial and spectral resolution. Sensitivity to large AGB values was higher for WV3 than for S2 and L8 with saturation point of 247 Mg ha−1 against 204 and 192 Mg ha−1. AGB values above these saturation points remained poorly predictable, especially for L8, indicating that several tropical forest AGB maps should be interpreted with extreme caution. However, predicted gradients of lower AGB values (≤200 Mg ha−1), i.e., in early forest successional stages, were fairly consistent among sensors (r > 0.70), even if the mean absolute difference between estimates was large when AGB predictions were extrapolated out of the calibration area at regional level (34%). We finally showed that calibrating the model only within the sensitivity AGB domain (e.g., <200 Mg ha−1) minimizes the risk of induced bias for estimating small AGB values. These results provide important benchmarks for interpreting previously published maps and to improve future validation schemes.

Garlic and winter wheat are major economic and grain crops in China, and their boundaries have increased substantially in recent decades. Updated and accurate garlic and winter wheat maps are critical for assessing their impacts on society and the environment. Remote sensing imagery can be used to monitor spatial and temporal changes in croplands such as winter wheat and maize. However, to our knowledge, few studies are focusing on garlic area mapping. Here, we proposed a method for coupling active and passive satellite imagery for the identification of both garlic and winter wheat in Northern China. First, we used passive satellite imagery (Sentinel-2 and Landsat-8 images) to extract winter crops (garlic and winter wheat) with high accuracy. Second, we applied active satellite imagery (Sentinel-1 images) to distinguish garlic from winter wheat. Third, we generated a map of the garlic and winter wheat by coupling the above two classification results. For the evaluation of classification, the overall accuracy was 95.97%, with a kappa coefficient of 0.94 by eighteen validation quadrats (3 km by 3 km). The user’s and producer’s accuracies of garlic are 95.83% and 95.85%, respectively; and for the winter wheat, these two accuracies are 97.20% and 97.45%, respectively. This study provides a practical exploration of targeted crop identification in mixed planting areas using multisource remote sensing data.

Active sensors capture cloud three-dimensional structures accurately, while their cloud profile products are restricted to narrow orbital trajectory. In contrast, passive sensors can capture the image structure of clouds over a large area, it is difficult to obtain features in the clouds’ vertical distribution. Therefore, in order to obtain the three-dimensional distributions of clouds, this study used the A-train Cloud Segment (ATCS) datasets, combining with active and multi-angle polarimetric measurements as input to generate a deep-learning-based cloud fraction retrieval model. We tested the retrieved vertical features of clouds, the results indicate that the new model is able to use passive sensor datasets to retrieve the three-dimensional vertical features of clouds. This work provides insights into the three-dimensional cloud feature retrieval using passive satellite data.

The use of multispectral satellite imagery for water monitoring is a fast and cost-effective method that can benefit from the growing availability of medium–high-resolution and free remote sensing data. Since the 1970s, multispectral satellite imagery has been exploited by adopting different techniques and spectral indices. The high number of available sensors and their differences in spectral and spatial characteristics led to a proliferation of outcomes that depicts a nice picture of the potential and limitations of each. This paper provides a review of satellite remote sensing applications for water extent delineation and flood monitoring, highlighting trends in research studies that adopted freely available optical imagery. The performances of the most common spectral indices for water segmentation are qualitatively analyzed and assessed according to different land cover types to provide guidance for targeted applications in specific contexts. The comparison is carried out by collecting evidence obtained from several applications identifying the overall accuracy (OA) obtained with each specific configuration. In addition, common issues faced when dealing with optical imagery are discussed, together with opportunities offered by new-generation passive satellites.

Diurnal variations in cloud cover and cloud vertical distribution are of great importance to Earth–atmosphere system radiative budgets and climate change. However, thus far these topics have received insufficient attention, especially on the Tibetan Plateau (TP). This study focuses on the diurnal variations in total cloud cover, cloud vertical distribution, and cirrus clouds and their relationship to meteorological factors over the TP based on active and passive satellite observations, reanalysis data, and CMIP6 outputs. Our results are consistent with previous studies but provide new insights. The results show that total cloud cover peaks at 06:00–09:00 UTC, especially over the eastern TP, but the spatial and temporal distributions of clouds from different datasets are inconsistent. This could to some extent be attributed to subvisible clouds missed by passive satellites and models. Compared with satellite observations, the amplitudes of the diurnal variations in total cloud cover obtained by the reanalysis and CMIP6 models are obviously smaller. CATS can capture the varying pattern of the vertical distribution of clouds and corresponding height of peak cloud cover at middle and high atmosphere levels, although it underestimates the cloud cover of low-level clouds, especially over the southern TP. Compared with CATS, ERA5 cannot capture the complete diurnal variations in vertical distribution of clouds and MERRA-2 has a poorer performance. We further find that cirrus clouds, which are widespread over the TP, show significant diurnal variations with averaged peak cloud cover over 0.35 at 15:00 UTC. Unlike in the tropics, where thin cirrus (0.03< optical depth <0.3) dominate, opaque cirrus clouds (0.3< optical depth <3) are the dominant cirrus clouds over the TP. The seasonal and regional averaged cloud cover of opaque cirrus reaches a daily maximum of 0.18 at 11:00 UTC, and its diurnal cycle is strong positive correlation with that of 250 hPa relative humidity and 250 hPa vertical velocity. Although subvisible clouds (optical depth <0.03), which have a potential impact on the radiation budget, are the fewest among cirrus clouds over the TP, the seasonal and regional averaged peak cloud cover can reach 0.09 at 22:00 UTC, and their diurnal cycle correlates with that of the 250 hPa relative humidity, 2 m temperature, and 250 hPa vertical velocity. Our results will be helpful to improve the simulation and retrieval of total cloud cover and cloud vertical distribution and further provide an observational constraint for simulations of the diurnal cycle of surface radiation budget and precipitation over the TP region.

Clouds exhibit a wide range of vertical morphologies that are regulated by distinct atmospheric dynamics and thermodynamics and are related to a diversity of microphysical properties and radiative effects. In this study, the new CERES-CloudSat-CALIPSO-MODIS (CCCM) RelD1 dataset is used to investigate the morphology and spatial distribution of different cloud vertical structure (CVS) types during 2007–2010. The combined active and passive satellites provide a more precise CVS than those only based on passive imagers or microwave radiometers. We group the clouds into 12 CVS classes based on how they are located or overlapping in three standard atmospheric layers with pressure thresholds of 440 and 680 hPa. For each of the 12 CVS types, the global average cloud radiative effects (CREs) at the top of the atmosphere, within the atmosphere and at the surface, as well as the cloud heating rate (CHR) profiles are examined. The observations are subsequently used to evaluate the variations in total, high-, middle- and low-level cloud fractions in CMIP6 models. The “historical” experiment during 1850–2014 and two scenarios (ssp245 and ssp585) during 2015–2100 are analyzed. The observational results show a substantial difference in the spatial pattern among different CVS types, with the greatest contrast between high and low clouds. Single-layer cloud fraction is almost 4 times larger on average than multi-layer cloud fraction, with significant geographic differences associated with clearly distinguishable regimes, showing that overlapping clouds are regionally confined. The global average CREs reveal that four types of CVSs warm the planet, while eight of them cool it. The longwave component drives the net CHR profile, and the CHR profiles of multi-layer clouds are more curved and intricate than those of single-layer clouds, resulting in complex thermal stratifications. According to the long-term analysis from CMIP6, the projected total cloud fraction decreases faster over land than over the ocean. The high clouds over the ocean increase significantly, but other types of clouds over land and the ocean continue to decrease, helping to offset the decrease in oceanic total cloud fraction. Moreover, it is concluded that the spatial pattern of CVS types may not be significantly altered by climate change, and only the cloud fraction is influenced. Our findings suggest that long-term observed CVS should be emphasized in the future to better understand CVS responses to anthropogenic forcing and climate change.

Since Kepler, Newton and Huygens in the seventeenth century, geodesy has been concerned with determining the figure, orientation and gravitational field of the Earth. With the beginning of the space age in 1957, a new branch of geodesy was created, satellite geodesy. Only with satellites did geodesy become truly global. Oceans were no longer obstacles and the Earth as a whole could be observed and measured in consistent series of measurements. Of particular interest is the determination of the spatial structures and finally the temporal changes of the Earth's gravitational field. The knowledge of the gravitational field represents the natural bridge to the study of the physics of the Earth's interior, the circulation of our oceans and, more recently, the climate. Today, key findings on climate change are derived from the temporal changes in the gravitational field: on ice mass loss in Greenland and Antarctica, sea level rise and generally on changes in the global water cycle. This has only become possible with dedicated gravity satellite missions opening a method known as satellite gravimetry. In the first forty years of space age, satellite gravimetry was based on the analysis of the orbital motion of satellites. Due to the uneven distribution of observatories over the globe, the initially inaccurate measuring methods and the inadequacies of the evaluation models, the reconstruction of global models of the Earth's gravitational field was a great challenge. The transition from passive satellites for gravity field determination to satellites equipped with special sensor technology, which was initiated in the last decade of the twentieth century, brought decisive progress. In the chronological sequence of the launch of such new satellites, the history, mission objectives and measuring principles of the missions CHAMP, GRACE and GOCE flown since 2000 are outlined and essential scientific results of the individual missions are highlighted. The special features of the GRACE Follow-On Mission, which was launched in 2018, and the plans for a next generation of gravity field missions are also discussed.

Saline wetlands experience large temporal fluctuations in water supply during the year and are recharged only or mainly through precipitation, meaning they are vulnerable to climate-change-induced aridification. Most passive satellite sensors are unsuitable for continuous wetland monitoring due to cloud cover and their relatively low temporal resolution. However, active satellite sensors such as the C-band synthetic aperture radar of Sentinel-1 satellites offer free, cloud-independent data. We examined surface water cover changes from October 2014 to November 2018 in the strictly protected area (13,000 ha) of the Upper-Kiskunság Alkaline Lakes region in the Danube–Tisza Interfluve in Hungary, with the aim of helping with nature protection planning. Changes and sensitivity can be defined based on the knowledge of variability. We developed a method for water cover detection based on automatic classification, applying the so-called WEKA K-Means clustering algorithm. For satellite data processing and analysis, we used the Google Earth Engine cloud processing platform. In terms of validation, we compared our results with the multispectral Modified Normalized Difference Water Index (MNDWI) derived from Landsat 8 and Sentinel-2 top-of-atmosphere reflectance images using a threshold-based binary classifier (receiver operator characteristics) for the MNDWI data. Using two completely distinct methods operating in distinct wavelength ranges, we obtained adequately matching results, with Spearman’s correlation coefficients (ρ) ranging from 0.54 to 0.80.

Because runoff production is more efficient over wetter soils, and because soil moisture has an intrinsic memory, soil moisture information can potentially contribute to the accuracy of streamflow predictions at seasonal leads. In this work, we use surface (0-5 cm) soil moisture retrievals obtained with the National Aeronautics and Space Administration’s Soil Moisture Active Passive satellite instrument in conjunction with streamflow measurements taken within 236 intermediate-scale (2,000 – 10,000 km2) unregulated river basins in the conterminous United States to show that late-fall satellite-based surface soil moisture estimates are indeed strongly connected to subsequent springtime streamflow. We thus show that the satellite-based soil moisture retrievals, all by themselves, have the potential to produce skillful seasonal streamflow predictions several months in advance. In poorly instrumented regions, they could perform better than reanalysis soil moisture products in this regard.

For passive satellite imagers, current retrievals of cloud optical thickness and effective particle size fail for convective clouds with 3D morphology. Indeed, being based on 1D radiative transfer (RT) theory, they work well only for horizontally homogeneous clouds. A promising approach for treating clouds as fully 3D objects is cloud tomography, which has been demonstrated for airborne observations. However, more efficient forward 3D RT solvers are required for cloud tomography from space. Here, we present a path forward by acknowledging that optically thick clouds have “veiled cores” (VCs). Sunlight scattered into and out of this deep region does not contribute significant information about the inner structure of the cloud to the spatially detailed imagery. We investigate the VC location for the MISR and MODIS imagers. While MISR provides multiangle imagery in the visible and near-infrared (IR), MODIS includes channels in the shortwave IR, albeit at a single view angle. This combination will enable future 3D retrievals to disentangle the cloud’s effective particle size and extinction fields. We find that, in practice, the VC is located at an optical distance of ~5, starting from the cloud boundary along the line of sight. For MODIS’s absorbing wavelengths the VC covers a larger volume, starting at smaller optical distances. This concept will not only lead to a reduction in the number of unknowns for the tomographic reconstruction but also significantly increase the speed and efficiency of the 3D RT solver at the heart of the algorithm by applying, say, the photon diffusion approximation inside the VC.