Integration of Surface Reflectance and Aerosol Retrieval Algorithms for Multi-Resolution Aerosol Optical Depth Retrievals over Urban Areas

The SEMARA approach, an integration of the Simplified and Robust Surface Reflectance Estimation (SREM) and Simplified Aerosol Retrieval Algorithm (SARA) methods, was used to retrieve aerosol optical depth (AOD) at 550 nm from a Landsat 8 Operational Land Imager (OLI) at 30 m spatial resolution, a Terra-Moderate Resolution Imaging Spectroradiometer (MODIS) at 500 m resolution, and a Visible Infrared Imaging Radiometer Suite (VIIRS) at 750 m resolution over bright urban surfaces in Beijing. The SEMARA approach coupled (1) the SREM method that is used to estimate the surface reflectance, which does not require information about water vapor, ozone, and aerosol, and (2) the SARA algorithm, which uses the surface reflectance estimated by SREM and AOD measurements obtained from the Aerosol Robotic NETwork (AERONET) site (or other high-quality AOD) as the input to estimate AOD without prior information on the aerosol optical and microphysical properties usually obtained from a look-up table constructed from long-term AERONET data. In the present study, AOD measurements were obtained from the Beijing AERONET site. The SEMARA AOD retrievals were validated against AOD measurements obtained from two other AERONET sites located at urban locations in Beijing, i.e., Beijing_RADI and Beijing_CAMS, over bright surfaces. The accuracy and uncertainties/errors in the AOD retrievals were assessed using Pearson’s correlation coefficient (r), root mean squared error (RMSE), relative mean bias (RMB), and expected error (EE = ± 0.05 ± 20%). EE is the envelope encompassing both absolute and relative errors and contains 68% (±1σ) of the good quality retrievals based on global validation. Here, the EE of the MODIS Dark Target algorithm at 3 km resolution is used to report the good quality SEMARA AOD retrievals. The validation results show that AOD from SEMARA correlates well with AERONET AOD measurements with high correlation coefficients (r) of 0.988, 0.980, and 0.981; small RMSE of 0.08, 0.09, and 0.08; and small RMB of 4.33%, 1.28%, and -0.54%. High percentages of retrievals, i.e., 85.71%, 91.53%, and 90.16%, were within the EE for Landsat 8 OLI, MODIS, and VIIRS, respectively. The results suggest that the SEMARA approach is capable of retrieving AOD over urban areas with high accuracy and small errors using high to medium spatial resolution satellite remote sensing data. This approach can be used for aerosol monitoring over bright urban surfaces such as in Beijing, which is frequently affected by severe dust storms and haze pollution, to evaluate their effects on public health.

Retrieval and Uncertainty Analysis of Land Surface Reflectance Using a Geostationary Ocean Color Imager

Land surface reflectance (LSR) is well known as an essential variable to understand land surface properties. The Geostationary Ocean Color Imager (GOCI) be able to observe not only the ocean but also the land with the high temporal and spatial resolution thanks to its channel specification. In this study, we describe the land atmospheric correction algorithm and present the quality of results through comparison with Moderate Resolution Imaging Spectroradiometer (MODIS) and in-situ data for GOCI-II. The GOCI LSR shows similar spatial distribution and quantity with MODIS LSR for both healthy and unhealthy vegetation cover. Our results agreed well with in-situ-based reference LSR with a high correlation coefficient (>0.9) and low root mean square error (<0.02) in all 8 GOCI channels. In addition, seasonal variation according to the solar zenith angle and phenological dynamics in time-series was well presented in both reference and GOCI LSR. As the results of uncertainty analysis, the estimated uncertainty in GOCI LSR shows a reasonable range (<0.04) even under a high solar zenith angle over 70°. The proposed method in this study can be applied to GOCI-II and can provide continuous satellite-based LSR products having a high temporal and spatial resolution for analyzing land surface properties.

Multi-Temporal Surface Water Classification for Four Major Rivers from the Peruvian Amazon

We describe a new minimum extent, persistent surface water classification for reaches of four major rivers in the Peruvian Amazon (i.e., Amazon, Napo, Pastaza, Ucayali). These data were generated by the Peruvian Amazon Rural Livelihoods and Poverty (PARLAP) Project which aims to better understand the nexus between livelihoods (e.g., fishing, agriculture, forest use, trade), poverty, and conservation in the Peruvian Amazon over a 35,000 km river network. Previous surface water datasets do not adequately capture the temporal changes in the course of the rivers, nor discriminate between primary main channel and non-main channel (e.g., oxbow lakes) water. We generated the surface water classifications in Google Earth Engine from Landsat TM 5, 7 ETM+, and 8 OLI satellite imagery for time periods from circa 1989, 2000, and 2015 using a hierarchical logical binary classification predominantly based on a modified Normalized Difference Water Index (mNDWI) and shortwave infrared surface reflectance. We included surface reflectance in the blue band and brightness temperature to minimize misclassification. High accuracies were achieved for all time periods (>90%).

Modelos da Produtividade Primária Bruta em área de floresta tropical em sazonalmente seca, usando dados reflectância da vegetação de caatinga

R E S U M OArtigo recebido em XX/XX/2021 e aceito em XX/XX/2021 Os estudos da troca de energia nos ecossistemas fornecem informações importantes para a compreensão da Produtividade nos ecossistemas. A vegetação é um dos principais elementos da biosfera terrestre sendo responsável pela avaliação e funcionamento da atividade fotossintética bem como para as trocas de carbono entre os ecossistemas e a atmosfera. Neste contexto, a PPB é utilizada para avaliar, planejar e gerenciar os recursos ambientais frente as mudanças climáticas globais. Esse estudo tem por objetivo avaliar a Produção Primária Bruta no Bioma da Caatinga em Pernambuco. O estudo foi realizado na área de Floresta Tropical Sazonalmente Seca, a Caatinga no Estado de Pernambuco. Utilizou-se a refletância da superfície do produto (MOD09) a partir do MODIS/TERRA satélite do sensor, a refletância de superfície (SR) Landsat-8 e a reflectancia a superficie do fieldspec. Foram adquiridas nove cenas para o produto (MOD09), seis cenas para a refletância de superfície (SR) Landsat-8 e as mesmas datas das imagens foram utilizados os espectros de campo (filedspec). Foi realizada a seleção de amostras espectrais na imagem (espectros de referência), considerando o ponto espectral do local de coleta. Os modelos foram construídos a partir das combinações das bandas (ρ_350, ρ_351, ρ_352, ..., ρ_2500) suas transformações (ρ, 1/ρ, ln⁡(ρ), log_10⁡(ρ), √ρ, ρ^2, e^ρ). Os desempenhos dos modelos foram avaliados utilizando dois índices estatísticos, um de tendência (coeficiente de Pearson– r) e outro de desvio (Erro médio quadrático (RMSE– RMSE), e o PBIAS. Os resultados apontaram que os modelos calibrados demostraram bom desempenho na previsão com o uso das bandas do sensor OLI/Landsat 8 e do MODIS/Terra (MOD09GA). Models of Gross Primary Productivity in a seasonally dry tropical forest area using reflectance data from the Caatinga vegetationA B S T R A C TThe studies of energy exchange in ecosystems provide important information for the understanding of Productivity in ecosystems. Vegetation is one of the main elements of the terrestrial biosphere and is responsible for the evaluation and functioning of photosynthetic activity as well as for carbon exchanges between ecosystems and the atmosphere. In this context, a PPB is used to assess, plan and manage environmental resources in the face of global climate change. This study aims to evaluate a Gross Primary Production in the Caatinga Biome in Pernambuco. The study was carried out in the Seasonally Dry Tropical Forest, a Caatinga in the State of Pernambuco. Use the product's surface reflectance (MOD09) from the sensor's MODIS / TERRA satellite and the Landsat-8 surface reflectance (SR), nine scenes for the product (MOD09), six scenes for surface reflectance (SR) Landsat-8 and similar data with fieldspec. A selection of spectral members in the image (reference spectra) was carried out, considering the spectral point of the collection site. The models were built from the combinations of the bands (ρ_350, ρ_351, ρ_352, ..., ρ_2500) their transformations (ρ, 1/ρ, ln⁡(ρ), log_10⁡(ρ), √ρ, ρ^2, e^ρ). The performances of the models were taken using two statistical indices, one of trend (Pearson's coefficient - r) and another of deviation (Mean square error (RMSE - RMSE), and PBIAS. The results showed that the calibrated models showed good performance in prediction using the OLI / Landsat 8 and MODIS / Terra (MOD09GA) bands.Keyword: Remote sensing, FieldSpec®3, Caatinga

Evaluation of Surface Reflectance Products Based on Optimized 6S Model Using Synchronous In Situ Measurements

Surface reflectance (SR) estimation is the most essential preprocessing step for multi-sensor remote sensing inversion of geophysical parameters. Therefore, accurate and stable atmospheric correction is particularly important, which is the premise and basis of the quantitative application of remote sensing. It can also be used to directly compare different images and sensors. The Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multi-Spectral Instrument (MSI) surface reflectance products are publicly available and demonstrate high accuracy. However, there is not enough validation using synchronous spectral measurements over China’s land surface. In this study, we utilized Moderate Resolution Imaging Spectroradiometer (MODIS) atmospheric products reconstructed by Categorical Boosting (CatBoost) and 30 m ASTER Global Digital Elevation Model (ASTER GDEM) data to adjust the relevant parameters to optimize the Second Simulation of Satellite Signal in the Solar Spectrum (6S) model. The accuracy of surface reflectance products obtained from the optimized 6S model was compared with that of the original 6S model and the most commonly used Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) model. Surface reflectance products were validated and evaluated with synchronous in situ measurements from 16 sites located in five provinces of China: Fujian, Gansu, Jiangxi, Hunan, and Guangdong. Through the indirect and direct validation across two sensors and three methods, it provides evidence that the synchronous measurements have the higher and more reliable validation accuracy. The results of the validation indicated that, for Landsat-8 OLI and Sentinel-2 MSI SR products, the overall root mean square error (RMSE) calculated results of optimized 6S, original 6S and FLAASH across all spectral bands were 0.0295, 0.0378, 0.0345, and 0.0313, 0.0450, 0.0380, respectively. R2 values reached 0.9513, 0.9254, 0.9316 and 0.9377, 0.8822, 0.9122 respectively. Compared with the original 6S model and FLAASH model, the mean percent absolute error (MPAE) of the optimized 6S model was reduced by 32.20% and 15.86% for Landsat-8 OLI, respectively. On the other, for the Sentinel-2 MSI SR product, the MPAE value was reduced by 33.56% and 33.32%. For the two kinds of data, the accuracy of each band was improved to varying extents by the optimized 6S model with the auxiliary data. These findings support the hypothesis that reliable auxiliary data are helpful in reducing the influence of the atmosphere on images and restoring reality as much as is feasible.