Guosheng Zhong

A Modified Aerosol Free Vegetation Index Algorithm for Aerosol Optical Depth Retrieval Using GOSAT TANSO-CAI Data

In this paper, we introduced a new algorithm for retrieving aerosol optical depth (AOD) over land, from the Cloud and Aerosol Imager (CAI), which is one of the instruments on the Greenhouse Gases Observing Satellite (GOSAT) for detecting and correcting cloud and aerosol interference. We used the GOSAT and AErosol RObotic NETwork (AERONET) collocated data from different regions over the globe to analyze the relationship between the top-of-atmosphere (TOA) reflectance in the shortwave infrared (1.6 μm) band and the surface reflectance in the red (0.67 μm) band. Our results confirmed that the relationships between the surface reflectance at 0.67 μm and TOA reflectance at 1.6 μm are not constant for different surface conditions. Under low AOD conditions (AOD at 0.55 μm < 0.1), a Normalized Difference Vegetation Index (NDVI) based regression function for estimating the surface reflectance of 0.67 μm band from the 1.6 μm band was summarized, and it achieved good performance, proving that the reflectance relations of the 0.67 μm and 1.6 μm bands are typically vegetation dependent. Since the NDVI itself is easily affected by aerosols, we combined the advantages of the Aerosol Free Vegetation Index (AFRI), which is aerosol resistant and highly correlated with regular NDVI, with our regression function, which can preserve the various correlations of 0.67 μm and 1.6 μm bands for different surface types, and developed a new surface reflectance and aerosol-free NDVI estimation algorithm, which we named the Modified AFRI1.6 algorithm. This algorithm was applied to AOD retrieval, and the validation results for our algorithm show that the retrieved AOD has a consistent relationship with AERONET measurements, with a correlation coefficient of 0.912, and approximately 67.7% of the AOD retrieved data were within the expected error range (± 0.1 ± 0.15AOD(AERONET)).

Analyzing CO2 concentration changes and their influencing factors in Indonesia by OCO-2 and other multi-sensor remote-sensing data

ABSTRACT We used OCO-2 products and considered three factors that potentially affect CO2 concentration in Indonesia: sea surface temperature (SST), forest fires and vegetation. From 2014 to 2016, CO2 concentration in Indonesia showed a trend of increase, which is consistent with the global increase reported by the Greenhouse Gases Observing Satellite (GOSAT) Project. As an archipelago country, the results indicate that SST has a direct effect on the CO2 concentration in Indonesia. Their changing exhibits similar fluctuations; meanwhile, CO2 concentration and SST also presented positive correlation. In 2015, the number of fire hotspots suddenly increased to 140,699, because of occurrence of the worst forest fire. Due to special geographic conditions, forest fires did not induce CO2 concentration changes in Indonesia, but CO2 concentration in the corresponding islands showed a trend of increase. CO2 concentration increased in Kalimantan during the occurrence of forest fire in September–October 2014, and CO2 concentration increased in Kalimantan and Sumatra during the occurrence of forest fire in September–October 2015. Vegetation indices were stable and presented no correlation with CO2 concentration. This study demonstrated that OCO-2 is capable of monitoring CO2 concentration at a regional scale; additionally, an effective method for using OCO-2 Level 2 products is proposed.

A Dark Target Algorithm for the GOSAT TANSO-CAI Sensor in Aerosol Optical Depth Retrieval over Land

Cloud and Aerosol Imager (CAI) onboard the Greenhouse Gases Observing Satellite (GOSAT) is a multi-band sensor designed to observe and acquire information on clouds and aerosols. In order to retrieve aerosol optical depth (AOD) over land from the CAI sensor, a Dark Target (DT) algorithm for GOSAT CAI was developed based on the strategy of the Moderate Resolution Imaging Spectroradiometer (MODIS) DT algorithm. When retrieving AOD from satellite platforms, determining surface contributions is a major challenge. In the MODIS DT algorithm, surface signals in the visible wavelengths are estimated based on the relationships between visible channels and shortwave infrared (SWIR) near the 2.1 µm channel. However, the CAI only has a 1.6 µm band to cover the SWIR wavelengths. To resolve the difficulties in determining surface reflectance caused by the lack of 2.1 μm band data, we attempted to analyze the relationship between reflectance at 1.6 µm and at 2.1 µm. We did this using the MODIS surface reflectance product and then connecting the reflectances at 1.6 µm and the visible bands based on the empirical relationship between reflectances at 2.1 µm and the visible bands. We found that the reflectance relationship between 1.6 µm and 2.1 µm is typically dependent on the vegetation conditions, and that reflectances at 2.1 µm can be parameterized as a function of 1.6 µm reflectance and the Vegetation Index (VI). Based on our experimental results, an Aerosol Free Vegetation Index (AFRI2.1)-based regression function connecting the 1.6 µm and 2.1 µm bands was summarized. Under light aerosol loading (AOD at 0.55 µm < 0.1), the 2.1 µm reflectance derived by our method has an extremely high correlation with the true 2.1 µm reflectance (r-value = 0.928). Similar to the MODIS DT algorithms (Collection 5 and Collection 6), a CAI-applicable approach that uses AFRI2.1 and the scattering angle to account for the visible surface signals was proposed. It was then applied to the CAI sensor for AOD retrieval; the retrievals were validated by comparisons with ground-level measurements from Aerosol Robotic Network (AERONET) sites. Validations show that retrievals from the CAI have high agreement with the AERONET measurements, with an r-value of 0.922, and 69.2% of the AOD retrieved data falling within the expected error envelope of ± (0.1 + 15% AODAERONET).

Extraction of rice-planting area and identification of chilling damage by remote sensing technology: a case study of the emerging rice production region in high latitude

Rice is the second largest staple crop in the world and therefore plays an important role in food security. As a thermophilic crop, rice is sensitive to temperature changes. Thus, research on the chilling damage of rice is essential. The Sanjiang Plain is an emerging rice production area and is located at high latitudes in China, the world’s largest rice-producing country. Landsat data were used to extract rice-planting area from 1985 to 2015. MODIS 13Q1, which was uniformly distributed during the growing period of rice, was used to obtain NDVI values of paddies during 2002–2015. Dynamic Identification Index of sterile-type chilling damage and monitoring standard of delayed-type chilling damage were the proposed methods used in this paper, which were used to judge the chilling damage of rice. The results show that in the study region, the rice-planting area in 2015 is nearly 12 times larger than that in 1985. Delayed-type chilling damage occurred in 2002 and 2009, while sterile-type chilling damage occurred in 2005, 2006, 2009, 2010, 2014, and 2015. Comparing with the prevalent meteorological standards, the results indicate that the index and standards proposed in this paper are precise, applicable, and more sensitive than them. The method is a macroscopic and accurate method to identify chilling damage in rice and can also provide a scientific basis to ensuring the stability of rice yield.

Spatial pattern of GPP variations in terrestrial ecosystems and its drivers: Climatic factors, CO 2 concentration and land-cover change, 1982–2015

Abstract Quantitative estimation of spatial pattern of gross primary production (GPP) trends and its drivers plays a crucial role in global change research. This study applied C-Fix model to estimate the net effect of each factor on GPP trends of 1982–2015, used an unsupervised classifier to group similar GPP trend behaviors, and analyzed the responses of GPP to changes in climatic, atmospheric and environmental drivers. According to the features of monthly GPP trends and the patterns of growing season, we presented nine categories as aids in interpreting large-scale behavior. Land-cover change (LCC), rising CO2, temperature and water conditions changes have the positive overall effect on GPP over the entire world, contrary to radiation change effects. The global average contributions of LCC, CO2, temperature, radiation and water on GPP trend are 4.57%, 65.73%, 13.07%, −7.24 and 11.74%, respectively. LCC and climatic factors changes have had a greater impact on GPP in terms of a specific location or regional rather than globally, and the interactions between factors are positive on GPP. The effects of climatic factors trends on GPP in different locations can be opposite, in general: regionally, GPP changes at middle and high latitudes are likely dominated by rises in radiation and temperature; at lower latitudes, GPP changes are likely to be driven by shifts in water conditions; at high altitudes, GPP changes are probably caused by changes in temperature and water conditions. These results will increase the understanding of the variations of carbon flux under future CO2, LCC and climate conditions.

Spatio-temporal distribution of forest fires and vegetation recovery in the Northeast of China

Post-fire vegetation can be monitored and analyzed over large areas in a time- and cost-effective manner by using satellite sensor imagery in combination with spatial analysis as provided by Geographical Information Systems (GIS). In this study, spatio-temporal distribution dynamics of burned area in the Northeast of China were analyzed by using a time series of MODIS Burned Area Product (MCD45) data from 2000 to 2010. The forest area damage caused by a large fire which occurred in the northeast of china, in May 1987 was also analyzed as a case study using Landsat TM/ETM+ images. Digital image processing methods, such as spectral profile analysis, vegetation indices and burn severity classification, were applied to the satellite images acquired before and after the forest fire. The inter-annual vegetation dynamic before, after fire events shows that vegetation recovery is a slow process, even after 23 years, some low-severely burned areas still exist in the maps.