Dabin Ji

WCOM: The science scenario and objectives of a global water cycle observation mission

Earth observation satellites play a critical role in providing information for understanding the global water cycle, which dominates the Earth-climate system. However, limitations in observations will restrict our current ability to reduce the uncertainties in the information used to make decisions regarding to water use and management. Under the support of “Strategic Priority Research Program for Space Sciences” of the Chinese Academy of Sciences, a new satellite concept of global Water Cycle Observation Mission (WCOM) is proposed, aiming to provide higher accuracy and consistent measurements of key elements of water cycle from space, including soil moisture, ocean salinity, freeze-thaw, snow water equivalent and etc. The expected more consistent and accurate datasets would be used to refine existing long-time series of satellite measurements, to constrain hydrological model projections and to detect the trends necessary for global change studies.

Estimating Snow Water Equivalent with Backscattering at X and Ku Band Based on Absorption Loss

Snow water equivalent (SWE) is a key parameter in the Earth’s energy budget and water cycle. It has been demonstrated that SWE can be retrieved using active microwave remote sensing from space. This necessitates the development of forward models that are capable of simulating the interactions of microwaves and the snow medium. Several proposed models have described snow as a collection of sphere- or ellipsoid-shaped ice particles embedded in air, while the microstructure of snow is, in reality, more complex. Natural snow usually forms a sintered structure following mechanical and thermal metamorphism processes. In this research, the bi-continuous vector radiative transfer (bi-continuous-VRT) model, which firstly constructs snow microstructure more similar to real snow and then simulates the snow backscattering signal, is used as the forward model for SWE estimation. Based on this forward model, a parameterization scheme of snow volume backscattering is proposed. A relationship between snow optical thickness and single scattering albedo at X and Ku bands is established by analyzing the database generated from the bi-continuous-VRT model. A cost function with constraints is used to solve effective albedo and optical thickness, while the absorption part of optical thickness is obtained from these two parameters. SWE is estimated after a correction for physical temperature. The estimated SWE is correlated with the measured SWE with an acceptable accuracy. Validation against two-year measurements, using the SnowScat instrument from the Nordic Snow Radar Experiment (NoSREx), shows that the estimated SWE using the presented algorithm has a root mean square error (RMSE) of 16.59 mm for the winter of 2009–2010 and 19.70 mm for the winter of 2010–2011.

Water Vapor Retrieval Over Cloud Cover Area on Land Using AMSR-E and MODIS

This study mainly discusses atmospheric water vapor retrieval over cloud cover area on land with the help of a newly developed surface emissivity parameter estimation method in microwave bands. In the retrieval method, the atmospheric water vapor sensitivity parameter-ratio of brightness temperature polarization difference at frequencies 18.7 and 23.8 GHz (ΔTb 1.87 /ΔTb 23.8 )-is used to retrieve water vapor, and the surface emissivity parameter-ratio of surface emissivity polarization difference at frequencies 18.7 and 23.8 GHz (Δe 18.7 /Δe 23.8 ) that corresponds to ΔTb 18.7 /ΔTb 23.8 is a key parameter that affects the final precision of retrieved atmosphere water vapor. In order to estimate Δe 18.7 /Δe 23.8 in cloudy condition, we first estimated the value of Δe 18.7 /Δe 23.8 in clear condition using Advanced Microwave Scanning Radiometer for EOS (AMSR-E) brightness temperature and related MODIS atmospheric products. At the same time, it was found that gradient information derived separately from ΔTb 18.7 /ΔTb 23.8 and Δe 18.7 /Δe 23.8 and very good correlation with each other. Based on this good correlation, the Δe 18.7 /Δe 23.8 in cloudy condition was estimated using corresponding==and adjacent 8 days Δe 18.7 /Δe 23.8 in clear condition. With the estimated Δe 18.7 /Δe 23.8 , we retrieved atmospheric column water vapor using lookup table method in cloudy condition over land. As a validation source data, the SuomiNet GPS-retrieved precipitable water (PW) vapor is used to validate the retrieved water vapor in this study. According to validation, the correlation coefficient of the two is 0.94 and the root-mean-square-error (RMSE) is 4.85 mm. It is a great improvement in water vapor retrieval using microwave in cloud cover area on land.

The water cycle observation mission (WCOM): Overview

Earth observation satellites play a critical role in providing information for understanding the global water cycle, which dominates the Earth-climate system. However, limitations in observations will restrict our current ability to reduce the uncertainties in the information used to make decisions regarding to water use and management. Under the support of “Strategic Priority Research Program for Space Sciences” of the Chinese Academy of Sciences, a new satellite concept of global Water Cycle Observation Mission (WCOM) is proposed, aiming to provide higher accuracy and consistent measurements of key elements of water cycle from space, including soil moisture, ocean salinity, freeze-thaw, snow water equivalent and etc. The expected more consistent and accurate datasets would be used to refine existing long-time series of satellite measurements, to constrain hydrological model projections and to detect the trends necessary for global change studies. The WCOM is expected to be implemented during the 13th five-year-plan period (2016-2020).

Observing Earth's water cycle from space

The global water cycle is the continuous transformation and movement of water on, above, and below the surface of the Earth through the phases of liquid, solid (ice and snow), and gas (vapor). It is the most active and important of the planet’s cycles, defining Earth’s mass, energy transportation, and transitions, and is influenced by factors such as global climate and human activity. To measure the effects of these transformations, scientists examine spatial distribution and temporal variations in images of cycle processes. However, such studies are currently limited by shortfalls in knowledge and observational capabilities. Existing systems offer satellite monitoring of the cycle, but the images they produce would benefit from improved temporal resolution, for example. Here, we present an integrated satellite-based observation system for the key elements and corresponding processes of the global water cycle. Our approach enhances observing and retrieval capabilities, to improve Earth science and global change studies. The proposed system, the Water Cycle Observation Mission (WCOM; see Figure 1), monitors soil moisture, ocean salinity, snow water equivalent, soil freeze-thaw processes, atmospheric water vapor, and precipitation. Moreover, its optimized payload configuration and design enable the mission to provide observations of all the environmental parameters— dominant and auxiliary—required for accurate retrieval of water cycle information. We can use the resulting datasets to refine the long-term satellite observations made during recent decades, and to monitor changes in hydrological elements. The WCOM satellite contains a combination of active and passive microwave remote sensors with wide frequency coverage, and hosts three main payloads (see Table 1). The first is an L-S-C-band tri-frequency fully polarized Figure 1. An artist’s impression of the Water Cycle Observation Mission (WCOM) satellite for observing the Earth’s water cycle.

Combining XCO2 measurements derived from SCIAMACHY and GOSAT for potentially generating global CO2 maps with high spatiotemporal resolution.

Global warming induced by atmospheric CO2 has attracted increasing attention of researchers all over the world. Although space-based technology provides the ability to map atmospheric CO2 globally, the number of valid CO2 measurements is generally limited for certain instruments owing to the presence of clouds, which in turn constrain the studies of global CO2 sources and sinks. Thus, it is a potentially promising work to combine the currently available CO2 measurements. In this study, a strategy for fusing SCIAMACHY and GOSAT CO2 measurements is proposed by fully considering the CO2 global bias, averaging kernel, and spatiotemporal variations as well as the CO2 retrieval errors. Based on this method, a global CO2 map with certain UTC time can also be generated by employing the pattern of the CO2 daily cycle reflected by Carbon Tracker (CT) data. The results reveal that relative to GOSAT, the global spatial coverage of the combined CO2 map increased by 41.3% and 47.7% on a daily and monthly scale, respectively, and even higher when compared with that relative to SCIAMACHY. The findings in this paper prove the effectiveness of the combination method in supporting the generation of global full-coverage XCO2 maps with higher temporal and spatial sampling by jointly using these two space-based XCO2 datasets.

Diurnal cycle and seasonal variation of cloud cover over the Tibetan Plateau as determined from Himawari-8 new-generation geostationary satellite data

Analysis of cloud cover and its diurnal variation over the Tibetan Plateau (TP) is highly reliant on satellite data; however, the accuracy of cloud detection from both polar-orbiting and geostationary satellites over this area remains unclear. The new-generation geostationary Himawari-8 satellites provide high-resolution spatial and temporal information about clouds over the Tibetan Plateau. In this study, the cloud detection of MODIS and AHI is investigated and validated against CALIPSO measurements. For AHI and MODIS, the false alarm rate of AHI and MODIS in cloud identification over the TP was 7.51% and 1.94%, respectively, and the cloud hit rate was 73.55% and 80.15%, respectively. Using hourly cloud-cover data from the Himawari-8 satellites, we found that at the monthly scale, the diurnal cycle in cloud cover over the TP tends to increase throughout the day, with the minimum and maximum cloud fractions occurring at 10:00 a.m. and 18:00 p.m. local time. Due to the limited time resolution of polar-orbiting satellites, the underestimation of MODIS daytime average cloud cover is approximately 4.00% at the annual scale, with larger biases during the spring (5.40%) and winter (5.90%).

A New Hybrid Snow Light Scattering Model Based on Geometric Optics Theory and Vector Radiative Transfer Theory

Light scattering models of snow are very important for the remote sensing of snow. Many previous models have used unrealistic assumptions about the snow particle shape and microstructure. In this paper, a new model is proposed, wherein a bicontinuous medium is used to simulate the snow microstructure, and geometric optics theory is used in combination with the Monte Carlo method to simulate the scattering properties of snow. Then, using the radiative transfer equation, the snow reflectance, including the polarized reflectance, can be simulated. Unlike other models that use Monte Carlo ray tracing, the new model is computationally efficient and can be used for massive simulations and practical applications. The simulation results of the new model are compared with the ground measurements and simulation results of a traditional model based on the Mie theory. Through validations and comparisons, the new model is shown to demonstrate a significantly improved capability in simulating the bidirectional reflectance of snow. The importance of the grain shape and microstructure modeling in the light scattering models of snow is confirmed by the comparison of the simulation results.

Observation system simulation experiment for a L-band microwave radiometer over rough bare soil site: A first step towards brightness temperature assimilation

L-band radiometry is a promising pathway for soil moisture estimation at global scale. An observation system simulation experiment was conducted for LEWIS over the SMOSREX bare soil site in 2006 through coupling the Variable Infiltration Capacity(VIC) land surface model and a Multi-Option L-band Microwave Emission Model(MOLMEM) in this study. Impacts from different dielectric constant models and roughness correction schemes on brightness temperature simulation were analyzed.

Analysis and parameterization of L-band microwave emission from exponentially correlated rough surface

Current and future satellite missions with L-band passive microwave radiometers could provide useful information for monitoring the soil moisture and freeze/thaw state at a global scale. The soil surface roughness plays a significant role in microwave emission from land surfaces. In this study, a simple parameterized model from exponentially correlated surface was developed. Results indicated the model can be very useful in understanding the effects of surface roughness on microwave emission.