Conventional methods for Aerosol Optical Depth (AOD) retrieval are limited to areas with low reflectance such as water or vegetated areas because the satellite signals from the aerosols in these areas are more obvious than those in areas with higher reflectance such as urban and sandy areas. Land Surface Reflectance (LSR) is the key parameter that must be estimated accurately. Most current methods used to estimate AOD are applicable only in areas with low reflectance. It has historically been difficult to estimate the LSR for bright surfaces because of their complex structure and high reflectance. This paper provides a method for estimating LSR for AOD retrieval in bright areas, and the method is applied to AOD retrieval for Landsat 8 Operational Land Imager (OLI) images at 500 m spatial resolution. A LSR database was constructed with the MODerate-resolution Imaging Spectroradiometer (MODIS) surface reflectance product (MOD09A1), and this database was also used to estimate the LSR of Landsat 8 OLI images. The AOD retrieved from the Landsat 8 OLI images was validated using the AOD measurements from four AErosol RObotic NETwork (AERONET) stations located in areas with bright surfaces. The MODIS AOD product (MOD04) was also compared with the retrieved AOD. The results demonstrate that the AOD retrieved with the new algorithm is highly consistent with the AOD derived from ground measurements, and its precision is better than that of MOD04 AOD products over bright areas.

The operational Moderate Resolution Imaging Spectroradiometer (MODIS) Aerosol Products (APs) have provided long-term and wide-spatial-coverage aerosol optical properties across the globe, such as aerosol optical depth (AOD). However, the performance of the latest Collection 6.1 (C6.1) of MODIS APs is still unclear over urban areas that feature complex surface characteristics and aerosol models. The aim of this study was to validate and compare the performance of the MODIS C6.1 and C6 APs (MxD04, x = O for Terra, x = Y for Aqua) over Beijing, China. The results of the Dark Target (DT) and Deep Blue (DB) algorithms were validated against Aerosol Robotic Network (AERONET) ground-based observations at local sites. The retrieval uncertainties and accuracies were evaluated using the expected error (EE: ±0.05 + 15%) and the root-mean-square error (RMSE). It was found that the MODIS C6.1 DT products performed better than the C6 DT products, with a greater percentage (by about 13%–14%) of the retrievals falling within the EE. However, the DT retrievals collected from two collections were significantly overestimated in the Beijing region, with more than 64% and 48% of the samples falling above the EE for the Terra and Aqua satellites, respectively. The MODIS C6.1 DB products performed similarly to the C6 DB products, with 70%–73% of the retrievals matching within the EE and estimation uncertainties. Moreover, the DB algorithm performed much better than DT algorithm over urban areas, especially in winter where abundant missing pixels were found in DT products. To investigate the effects of factors on AOD retrievals, the variability in the assumed surface reflectance and the main optical properties applied in DT and DB algorithms are also analyzed.

This letter presents a new algorithm that allows the retrieval of the aerosol optical depth (AOD) at a high (500 m) spatial resolution from Landsat 8 Operational Land Imager (OLI) data over urban areas. Because of the complex structure over urban surfaces, the bidirectional reflectance characteristic is obvious; however, most of the current aerosol retrieval algorithms over land do not account for the anisotropic effect of the surface. This letter improves the quality of AOD retrieval by providing the surface reflectance based on the multiyear MODIS bidirectional reflectance distribution function (BRDF)/Albedo model parameters product (MCD43A1) and the RossThick-LiSparse reciprocal kernel-driven BRDF model. The ground-based Aerosol Robotic Network (AERONET) AOD measurements from five sites located in urban and suburban areas are used to validate the AOD retrievals, and the MODIS Terra Collection 6 (C6) dark target/deep blue AOD products (MOD04) at 10-km spatial resolution are obtained for comparison. The validation results show that the AOD retrievals from the OLI images are well correlated with the AERONET AOD measurements (R = 0.987), with a low root-mean-square error of 0.07, a mean absolute error of 0.036, and a relative mean bias of 1.029; approximately 95.3% of the collocations fall within the expected error. The analysis indicates that the BRDF is essential in ensuring the accuracy of AOD retrieval. Compared with the MOD04 AOD retrievals, the OLI AOD retrievals have better spatial continuity and higher accuracy. The new algorithm can provide continuous and detailed spatial distributions of the AOD over complex urban surfaces.

Understanding the mechanisms of the coastal wetland ecosystem carbon sink function is crucial for adapting to, mitigating, and predicting global climate change. Landscape metrics and Carnegie Ames Stanford Approach (CASA) model were used to quantify the spatiotemporal patterns of soil organic carbon (SOC) and vegetation carbon sequestration (VCS) in the Yellow River Delta (YRD). GeoDetector model was used to explore the effects of various factors influencing VCS. The results showed that: (1) From 1999 to 2020, the area of natural wetlands and non-wetlands decreased, while the area of artificial wetlands increased. (2) The SOC stock in the 0 ~ 100 cm depth in 1999 and 2020 were about 7.8871 Tg C and 7.0521 Tg C, respectively. The amount of VCS increased from 0.2309 Tg C in 2000 to 0.3681 Tg C in 2020, with a significant annual increase of 6532 Mg C. In the past 21 years, the total carbon sink in the YRD was 6.0952 Tg C. The amount of VCS was equivalent to 2.93% of SOC stock in 1999, rising to 5.22% by 2020. (3) Vegetation cover has the greatest influence on the carbon sink function in the YRD, followed by precipitation and biodiversity. From 2000 to 2020, the effect of biodiversity on the carbon sinks of natural wetlands and artificial wetlands increased. The synergistic effect of wetland type and vegetation cover on carbon sink effect was replaced by the synergistic effect of biodiversity and vegetation cover. The identification of the primary factors that influence VCS in the YRD is significant to the understanding of the spatiotemporal evolution of carbon sink effect in coastal wetland ecosystems, and to the guidance of the rational development and protection of coastal wetlands and promote sustainable ecosystem development.

Satellite remote sensing has been widely used to retrieve aerosol optical depth (AOD), which is an indicator of air quality as well as radiative forcing. The dark target (DT) algorithm is applied to low reflectance areas, such as dense vegetation, and the deep blue (DB) algorithm is adopted for bright-reflecting regions. However, both DT and DB algorithms ignore the effect of surface bidirectional reflectance. This paper provides a method for AOD retrieval in arid or semiarid areas, in which the key points are the accurate estimation of surface reflectance and reasonable assumptions of the aerosol model. To reduce the uncertainty in surface reflectance, a minimum land surface reflectance database at the spatial resolution of 500 m for each month was constructed based on the moderate-resolution imaging spectroradiometer (MODIS) surface reflectance product. Furthermore, a bidirectional reflectance distribution function (BRDF) correction model was adopted to compensate for the effect of surface reflectance anisotropy. The aerosol parameters, including AOD, single scattering albedo, asymmetric factor, Ångström exponent and complex refractive index, are determined based on the observation of two sunphotometers installed in northern Xinjiang from July to August 2014. The AOD retrieved from the MODIS images was validated with ground-based measurements and the Terra-MODIS aerosol product (MOD04). The 500 m AOD retrieved from the MODIS showed high consistency with ground-based AOD measurements, with an average correlation coefficient of ~0.928, root mean square error (RMSE) of ~0.042, mean absolute error (MAE) of ~0.032, and the percentage falling within the expected error (EE) of the collocations is higher than that for the MOD04 DB product. The results demonstrate that the new AOD algorithm is more suitable to represent aerosol conditions over Xinjiang than the DB standard product.

The aim of this study is to evaluate the accuracy of MODerate resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) products over heavy aerosol loading areas. For this analysis, the Terra-MODIS Collection 6.1 (C6.1) Dark Target (DT), Deep Blue (DB) and the combined DT/DB AOD products for the years 2000–2016 are used. These products are validated using AErosol RObotic NETwork (AERONET) data from twenty-three ground sites situated in high aerosol loading areas and with available measurements at least 500 days. The results show that the numbers of collections (N) of DB and DT/DB retrievals were much higher than that of DT, which was mainly caused by unavailable retrieval of DT in bright reflecting surface and heavy pollution conditions. The percentage falling within the expected error (PWE) of the DT retrievals (45.6%) is lower than that for the DB (53.4%) and DT/DB (53.1%) retrievals. The DB retrievals have 5.3% less average overestimation, and 25.7% higher match ratio than DT/DB retrievals. It is found that the current merged aerosol algorithm will miss some cases if it is determined only on the basis of normalized difference vegetation index. As the AOD increases, the value of PWE of the three products decreases significantly; the undervaluation is suppressed, and the overestimation is aggravated. The retrieval accuracy shows distinct seasonality: the PWE is largest in autumn or winter, and smallest in summer. The most severe overestimation and underestimation occurred in the summer. Moreover, the DT, DB and DT/DB products over different land cover types still exhibit obvious deviations. In urban areas, the PWE of DB product (52.6%) is higher than for the DT/DB (46.3%) and DT (25.2%) products. The DT retrievals perform poorly over the barren or sparsely vegetated area (N = 52). However, the performance of three products is similar over vegetated area. On the whole, the DB product performs better than the DT product over the heavy aerosol loading area.