Advanced Very High Resolution Radiometer Data Research Articles

Application of advanced very high-resolution radiometer (AVHRR)-based vegetation health indices for modelling and predicting malaria in Northern Benin, West Africa

BackgroundVegetation health (VH) is a powerful characteristic for forecasting malaria incidence in regions where the disease is prevalent. This study aims to determine how vegetation health affects the prevalence of malaria and create seasonal weather forecasts using NOAA/AVHRR environmental satellite data that can be substituted for malaria epidemic forecasts.MethodsWeekly advanced very high-resolution radiometer (AVHRR) data were retrieved from the NOAA satellite website from 2009 to 2021. The monthly number of malaria cases was collected from the Ministry of Health of Benin from 2009 to 2021 and matched with AVHRR data. Pearson correlation was calculated to investigate the impact of vegetation health on malaria transmission. Ordinary least squares (OLS), support vector machine (SVM) and principal component regression (PCR) were applied to forecast the monthly number of cases of malaria in Northern Benin. A random sample of proposed models was used to assess accuracy and bias.ResultsEstimates place the annual percentage rise in malaria cases at 9.07% over 2009–2021 period. Moisture (VCI) for weeks 19–21 predicts 75% of the number of malaria cases in the month of the start of high mosquito activities. Soil temperature (TCI) and vegetation health index (VHI) predicted one month earlier than the start of mosquito activities through transmission, 78% of monthly malaria incidence.ConclusionsSVM model D is more effective than OLS model A in the prediction of malaria incidence in Northern Benin. These models are a very useful tool for stakeholders looking to lessen the impact of malaria in Benin.

Direct assimilation of AVHRR satellite radiance measurements in a reanalysis system

AbstractThe Advanced Very‐High‐Resolution Radiometer (AVHRR) is a broad‐band, five‐channel scanner sensing in the visible, near‐infrared and thermal infrared portions of the electromagnetic spectrum. AVHRR instruments onboard polar‐orbiting satellites have data records spanning 30 years. The radiances from the infrared channels of AVHRR have been directly assimilated over the ice‐free ocean in the NASA Goddard Earth Observing System (GEOS) since 2017 to constrain skin sea surface temperature (SST). The GEOS system already uses an advanced bulk SST and a skin SST model which incorporates diurnal warming and cool‐skin temperature on the surface of the ocean. The positive contribution of this effort to the Numerical Weather Prediction (NWP) makes it desirable to extend the skin SST data assimilation procedure to reanalysis applications. The AVHRR data from platforms that were previously unexplored for NWP applications are assimilated at the Global Modeling and Assimilation Office (GMAO) at NASA, for an upcoming reanalysis project. This study uses results from reanalysis experiments to assess the impact of assimilating AVHRR radiances over open ocean on the atmospheric state variables, focusing on the analyzed skin temperatures that include the active skin SST model. It is demonstrated that including the skin temperature variations in atmospheric analysis is generally beneficial. The addition of radiance measurements from AVHRR provides further improvements to the overall reanalysis system performance. by helping to correct the diurnal heating and reducing errors in the background departure of hyperspectral radiance observations, which are an essential component of atmospheric reanalyses.

Spatiotemporally consistent global dataset of the GIMMS Normalized Difference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2022

Abstract. Global products of remote sensing Normalized Difference Vegetation Index (NDVI) are critical to assessing the vegetation dynamic and its impacts and feedbacks on climate change from local to global scales. The previous versions of the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI product derived from the Advanced Very High Resolution Radiometer (AVHRR) provide global biweekly NDVI data starting from the 1980s, being a reliable long-term NDVI time series that has been widely applied in Earth and environmental sciences. However, the GIMMS NDVI products have several limitations (e.g., orbital drift and sensor degradation) and cannot provide continuous data for the future. In this study, we presented a machine learning model that employed massive high-quality global Landsat NDVI samples and a data consolidation method to generate a new version of the GIMMS NDVI product, i.e., PKU GIMMS NDVI (1982–2022), based on AVHRR and Moderate-Resolution Imaging Spectroradiometer (MODIS) data. A total of 3.6 million Landsat NDVI samples that were well spread across the globe were extracted for vegetation biomes in all seasons. The PKU GIMMS NDVI exhibits higher accuracy than its predecessor (GIMMS NDVI3g) in terms of R2 (0.97 over 0.94), root mean squared error (RMSE: 0.05 over 0.09), mean absolute error (MAE: 0.03 over 0.07), and mean absolute percentage error (MAPE: 9 % over 20 %). Notably, PKU GIMMS NDVI effectively eliminates the evident orbital drift and sensor degradation effects in tropical areas. The consolidated PKU GIMMS NDVI has a high consistency with MODIS NDVI in terms of pixel value (R2 = 0.956, RMSE = 0.048, MAE = 0.034, and MAPE = 6.0 %) and global vegetation trend (0.9×10-3 yr−1). The PKU GIMMS NDVI product can potentially provide a more solid data basis for global change studies. The theoretical framework that employs Landsat data samples can facilitate the generation of remote sensing products for other land surface parameters. The PKU GIMMS NDVI product is open access and available under a Creative Commons Attribution 4.0 License at https://doi.org/10.5281/zenodo.8253971 (Li et al., 2023).

Evaluation of Five Global Top-of-Atmosphere Outgoing Longwave Radiation Products

Five global monthly top-of-atmosphere (TOA) outgoing longwave radiation (OLR) products are evaluated in this study, including the products derived from the High-Resolution Infrared Radiation Sounder (HIRS), Clouds and the Earth’s Radiant Energy System (CERES), Advanced Very High Resolution Radiometer (AVHRR), the CM SAF cLoud, Albedo and surface RAdiation dataset from AVHRR data (CLARA), and the Global Energy and Water Cycle EXchanges (GEWEX) project. Results show that overall there is good consistency among these five products. Larger differences are found between GEWEX and CERES (HIRS) after (before) 2000 (RMSE ~ 5 W/m2), particularly in the tropical regions. In terms of global mean values, GEWEX shows large differences with the other products from the year 1992 to 2002, and CLARA shows large differences from the year 1979 to 1981, which are more obvious in the global ocean values. Large discrepancies among these products exist at low latitudinal bands, particularly before the year 2000. Australia and Asia (mid–low latitude part) are two typical regions in which larger differences are found.

Developing global annual land surface phenology datasets (1982–2018) from the AVHRR data using multiple phenology retrieval methods

Land surface phenology that reflects periodical changes in terrestrial vegetation plays an important role in influencing the functioning and services of ecosystems. Satellite-derived metrics of land surface phenology have been widely used to assess the patterns in land cover dynamics. There are still limited satellite-based datasets of global land surface phenology available to date due to uneven qualities of satellite data. Here, we developed a global annual land surface phenology dataset based on Advanced Very High Resolution Radiometer (AVHRR) data from 1982 to 2018. The number of global vegetation growing seasons for each individual pixel was counted for each year from 1982 to 2018. We implemented four phenology retrieval methods, including the amplitude threshold method, the second-order derivative method, the third-order derivative method, and the curvature change rate method, to retrieve four key phenological metrics for each vegetation growth cycle, including the start of the growing season (SOS), maturity, senescence, and the end of the growing season (EOS). The results show that the phenological metrics retrieved using different phenology retrieval methods have similar patterns of global spatial distribution. From 1982 to 2018, the timing of both maturity and senescence within a year advanced in the Northern Hemisphere and the timing of SOS delayed in the Southern Hemisphere. The phenological metrics extracted from in-situ data such as the USA National Phenology Network (USA-NPN), the Pan European Phenology Database (PEP725), and flux tower measurements were positively correlated with satellite-derived phenological metrics. Despite the effects of mismatching spatial scales, the retrieved phenological metrics based on 5 km AVHRR data were consistent with those based on 500 m Moderate-resolution Imaging Spectroradiometer (MODIS) data and 30 m fused satellite data at both site-scale and regional-scale. The developed dataset released for usages and applications on global land surface dynamics offers potential end users options for choosing desirable phenological metrics derived from different methods.

A Global 250-m Downscaled NDVI Product from 1982 to 2018

Satellite-based normalized difference vegetation index (NDVI) time series data are useful for monitoring the changes in vegetation ecosystems in the context of global climate change. However, most of the current NDVI products cannot effectively reconcile high spatial resolution and continuous observations in time. Here, to produce a global-scale, long-term, and high-resolution NDVI database, we developed a simple and new data downscaling approach. The downscaling algorithm considers the pixel-wise ratios of the coefficient of variation (CV) between the coarse- and fine-resolution NDVI data and relative changes in the NDVI against a baseline period. The algorithm successfully created a worldwide monthly NDVI database with 250 m resolution from 1982 to 2018 by translating the fine spatial information from MODIS (Moderate-resolution Imaging Spectroradiometer) data and the long-term temporal information from AVHRR (Advanced Very High Resolution Radiometer) data. We employed the evaluation indices of root mean square error (RMSE), mean absolute error (MAE), and Pearson’s correlation coefficient (Pearson’s R) to assess the accuracy of the downscaled data against the MODIS NDVI. Both the RMSE and MAE values at the regional and global scales are typically between 0 and 0.2, whereas the Pearson’s R values are mostly above 0.7, which implies that the downscaled NDVI product is similar to the MODIS NDVI product. We then used the downscaled data to monitor the NDVI changes in different plant types and places with significant vegetation heterogeneity, as well as to investigate global vegetation trends over the last four decades. The Google Earth Engine platform was used for all the data downscaling processes, and here we provide a code for users to easily acquire data corresponding to any part of the world. The downscaled global-scale NDVI time series has high potential for the monitoring of the long-term temporal and spatial dynamics of terrestrial ecosystems under changing environments.

Cloud-probability-based estimation of black-sky surface albedo from AVHRR data

Abstract. This paper describes a new method for cloud-correcting observations of black-sky surface albedo derived using the Advanced Very High Resolution Radiometer (AVHRR). Cloud cover constitutes a major challenge for surface albedo estimation using AVHRR data for all possible conditions of cloud fraction and cloud type with any land cover type and solar zenith angle. This study shows how the new cloud probability (CP) data to be provided as part of edition A3 of the CLARA (CM SAF cLoud, Albedo and surface Radiation dataset from AVHRR data) record from the Satellite Application Facility on Climate Monitoring (CM SAF) project of EUMETSAT can be used instead of traditional binary cloud masking to derive cloud-free monthly mean surface albedo estimates. Cloudy broadband albedo distributions were simulated first for theoretical cloud distributions and then using global cloud probability (CP) data for 1 month. A weighted mean approach based on the CP values was shown to produce very-high-accuracy black-sky surface albedo estimates for simulated data. The 90 % quantile for the error was 1.1 % (in absolute albedo percentage) and that for the relative error was 2.2 %. AVHRR-based and in situ albedo distributions were in line with each other and the monthly mean values were also consistent. Comparison with binary cloud masking indicated that the developed method improves cloud contamination removal.

Temporal Greenness Trends in Stable Natural Land Cover and Relationships with Climatic Variability across the Conterminous United States

Abstract Assessment of temporal trends in vegetation greenness and related influences aids understanding of recent changes in terrestrial ecosystems and feedbacks from weather, climate, and environment. We analyzed 1-km normalized difference vegetation index (NDVI) time series data (1989–2016) derived from the Advanced Very High Resolution Radiometer (AVHRR) and developed growing-season time-integrated NDVI (GS-TIN) for estimating seasonal vegetation activity across stable natural land cover in the conterminous United States (CONUS). After removing areas from analysis that had experienced land-cover conversion or modification, we conducted a monotonic trend analysis on the GS-TIN time series and found that significant positive temporal trends occurred over 35% of the area, whereas significant negative trends were observed over only 3.5%. Positive trends were prevalent in the forested lands of the eastern one-third of CONUS and far northwest, as well as in grasslands in the north-central plains. We observed negative and nonsignificant trends mainly in the shrublands and grasslands across the northwest, southwest, and west-central plains. To understand the relationship of climate variability with these temporal trends, we conducted partial and multiple correlation analyses on GS-TIN, growing-season temperature, and water-year precipitation time series. The GS-TIN trends in northern forests were positively correlated with temperature. The GS-TIN trends in the central and western shrublands and grasslands were negatively correlated with temperature and positively correlated with precipitation. Our results revealed spatial patterns in vegetation greenness trends for different stable natural vegetation types across CONUS, enhancing understanding gained from prior studies that were based on coarser 8-km AVHRR data. Significance Statement Assessing vegetation trends, cycles, and related influences is important for understanding the responses and feedbacks of terrestrial ecosystems to climatic and environmental changes. We analyzed vegetation greenness trends (1989–2016) for stable natural land cover across the conterminous United States, based on vegetation index time series derived from coarse-resolution optical satellite sensors. We found greening trends in the forests of the east and far northwest and the grasslands of the northern central plains that correlated with increasing temperature in the regions. We observed browning and no trends mainly in the shrublands and grasslands across the northwest, southwest, and western central plains, associated with increasing temperature and decreasing precipitation. Future research should focus on vegetation greenness analysis using finer-resolution satellite data.

Improved estimation of the global top-of-atmosphere albedo from AVHRR data

The top-of-atmosphere (TOA) albedo, a key component of the earth's energy balance, can be monitored regularly by satellite observations. Compared to the previous study Song et al. (2018), this paper estimates TOA albedo by directly linking Advanced Very High Resolution Radiometer (AVHRR) narrowband reflectance with TOA broadband albedo determined by NASA's Clouds and the Earth's Radiant Energy System (CERES) instead of Moderate Resolution Imaging Spectroradiometer (MODIS). The TOA albedo product developed in this study has an increased spatial resolution, from 1° to 0.05°, and its starting year has been extended from 2000 to 1981, compared to the CERES TOA albedo product. Models of lands and oceans are established separately under different atmospheric and surface conditions using gradient boosting regression tree (GBRT) method instead of the linear regression models in the previous study. The root mean square errors (RMSEs) of the cloudy-sky, clear-sky and snow-cover models over land are 11.2%, 9.2% and 2.3%, respectively; over oceans they are 14.6%, 10.6% and 5.6%, respectively. Compared to Song et al. (2018), the improvements of the three models over land are 28.8%, 29.2% and 68.6%, respectively. Compared to the CERES product, the new product is much more accurate than that from our previous study. The global monthly mean differences of the TOA albedo obtained with the GBRT product and CERES from 2001 to 2014 are mostly less than 5%.

Validation of AVHRR Land Surface Temperature with MODIS and In Situ LST—A TIMELINE Thematic Processor

Land Surface Temperature (LST) is an important parameter for tracing the impact of changing climatic conditions on our environment. Describing the interface between long- and shortwave radiation fluxes, as well as between turbulent heat fluxes and the ground heat flux, LST plays a crucial role in the global heat balance. Satellite-derived LST is an indispensable tool for monitoring these changes consistently over large areas and for long time periods. Data from the AVHRR (Advanced Very High-Resolution Radiometer) sensors have been available since the early 1980s. In the TIMELINE project, LST is derived for the entire operating period of AVHRR sensors over Europe at a 1 km spatial resolution. In this study, we present the validation results for the TIMELINE AVHRR daytime LST. The validation approach consists of an assessment of the temporal consistency of the AVHRR LST time series, an inter-comparison between AVHRR LST and in situ LST, and a comparison of the AVHRR LST product with concurrent MODIS (Moderate Resolution Imaging Spectroradiometer) LST. The results indicate the successful derivation of stable LST time series from multi-decadal AVHRR data. The validation results were investigated regarding different LST, TCWV and VA, as well as land cover classes. The comparisons between the TIMELINE LST product and the reference datasets show seasonal and land cover-related patterns. The LST level was found to be the most determinative factor of the error. On average, an absolute deviation of the AVHRR LST by 1.83 K from in situ LST, as well as a difference of 2.34 K from the MODIS product, was observed.

Development of a global annual land surface phenology dataset for 1982–2018 from the AVHRR data by implementing multiple phenology retrieving methods

The land surface phenology (LSP) associated with vegetation dynamics plays an important role in influencing the land surface processes and land–atmosphere interactions. Satellite observations have been widely used in studies for the monitoring of LSP across large areas based on different phenology retrieval algorithms and the routine production of LSP from remote sensing data has yet come to fruition. Here we used six phenology retrieval methods, including the amplitude threshold (AT), first-order derivative (FOD), second-order derivative (SOD), relative changing rate (RCR), third-order derivative (TOD), and curvature change rate (CCR), to retrieve the start of the growing season (SOS) and the end of the growing season (EOS) from the Advanced Very High Resolution Radiometer (AVHRR) data. We improved the curve fitting method to reduce uncertainties owing to data preprocessing. The results indicated that both SOS and EOS retrieved by six different methods had similar spatial distribution and the retrieved dates could vary largely at the pixel level. In the Northern Hemisphere, from 1982 to 2018, the trends of SOS retrieved vary across methods and only the EOS extracted by the relative change curvature method had a significant advanced trend. In the Southern Hemisphere, from 1982 to 2018, SOS results derived from four methods (i.e., AT, SOD, TOD, and CCR) showed significantly delayed trends, EOS results extracted by all the methods demonstrated insignificant trends. The phenology retrieval methods were assessed using the field observation data from the Pan European Phenology Project (PEP725) and from time series of leaf area index (LAI) measured at flux towers. The satellite-retrieved dates of both SOS and EOS were positively correlated with field observation and the relationships are largely dependent on how field phenology metrics are defined. We presented longer time series (1982–2018) data of phenology metrics with fewer gaps and multiple phenology retrieving methods as compared to the MODIS land cover dynamics product. Based on our assessments, one might use the SOS generated by FOD and the EOS generated by RCR as they provide results the most consistent with field data among all the tested methods. If studies aim to use the earliest SOS (or latest EOS) in a year, one might use the data retrieved based on TOD or CCR. The global dataset is delivered for uses in studies and applications associated with LSP.

Assessment of the Advanced Very High-Resolution Radiometer (AVHRR) for Snowfall Retrieval in High Latitudes Using CloudSat and Machine Learning

AbstractPrecipitation retrieval is a challenging topic, especially in high latitudes (HL), and current precipitation products face ample challenges over these regions. This study investigates the potential of the Advanced Very High-Resolution Radiometer (AVHRR) for snowfall retrieval in HL using CloudSat radar information and machine learning (ML). With all the known limitations, AVHRR observations should be considered for HL snowfall retrieval because (1) AVHRR data have been continuously collected for about four decades on multiple platforms with global coverage, and similar observations will likely continue in the future; (2) current passive microwave satellite precipitation products have several issues over snow and ice surfaces; and (3) good coincident observations between AVHRR and CloudSat are available for training ML algorithms. Using ML, snowfall rate was retrieved from AVHRR’s brightness temperature and cloud probability, as well as auxiliary information provided by numerical reanalysis. The results indicate that the ML-based retrieval algorithm is capable of detection and estimation of snowfall with comparable or better statistical scores than those obtained from the Atmospheric Infrared Sounder (AIRS) and two passive microwave sensors contributing to the Global Precipitation Measurement (GPM) mission constellation. The outcomes also suggest that AVHRR-based snowfall retrievals are spatially and temporally reasonable and can be considered as a quantitatively useful input to the merged precipitation products that require frequent sampling or long-term records.

MODIS-Based AVHRR Cloud and Snow Separation Algorithm

The long-term data record (LTDR) has the goal of developing a quality and consistent Advanced Very High Resolution Radiometer (AVHRR) surface reflectance and albedo products dating back to 1982 at 0.05° spatial resolution. Distinguishing between cloud and snow is of critical importance when analyzing global albedo trends, for they influence the Earth’s energy balance. However, this task is specially challenging when working with AVHRR given its limited spectral bands. Therefore, the current version of the LTDR does not distinguish between snow and clouds. To this end, we propose the Moderate Resolution Imaging Spectroradiometer (MODIS)-based AVHRR Class Separation Algorithm (MACSSA), whose goal is to identify clear land and snow pixels using AVHRR data. We make use of a combination of optical and thermal information from satellite and reanalysis data, along with monthly climatology information. These are used as inputs for two different support vector machine (SVM) models, which are then applied to AVHRR data to retrieve the MACSSA predicted tags. These are compared first against reference tags retrieved from the MYD10C1 product over pixels with less than 2-min overpass time difference between MODIS Aqua and NOAA16–19, distributed all around the world, and second against the Climate Change Initiative Cloud (Cloud_cci AVHRR) project. We found the product to be highly accurate in identifying clear land pixels, with a probability of detection of clear pixels (PODclear) of 97%. The discrimination of snow and clouds shows a PODsnow of 89%, which is encouraging given the spectral limitations of the AVHRR sensor.

The Climate Monitoring SAF Outgoing Longwave Radiation from AVHRR

Data from the Advanced Very High Resolution Radiometer (AVHRR) have been used to create several long-duration data records of geophysical variables describing the atmosphere and land and water surfaces. In the Climate Monitoring Satellite Application Facility (CM SAF) project, AVHRR data are used to derive the Cloud, Albedo, and Radiation (CLARA) climate data records of radiation components (i.a., surface albedo) and cloud properties (i.a., cloud cover). This work describes the methodology implemented for the additional estimation of the Outgoing Longwave Radiation (OLR), an important Earth radiation budget component, that is consistent with the other CLARA variables. A first step is the estimation of the instantaneous OLR from the AVHRR observations. This is done by regressions on a large database of collocated observations between AVHRR Channel 4 (10.8 µm) and 5 (12 µm) and the OLR from the Clouds and Earth’s Radiant Energy System (CERES) instruments. We investigate the applicability of this method to the first generation of AVHRR instrument (AVHRR/1) for which no Channel 5 observation is available. A second step concerns the estimation of daily and monthly OLR from the instantaneous AVHRR overpasses. This step is especially important given the changes in the local time of the observations due to the orbital drift of the NOAA satellites. We investigate the use of OLR in the ERA5 reanalysis to estimate the diurnal variation. The developed approach proves to be valuable to model the diurnal change in OLR due to day/night time warming/cooling over clear land. Finally, the resulting monthly mean AVHRR OLR product is intercompared with the CERES monthly mean product. For a typical configuration with one morning and one afternoon AVHRR observation, the Root Mean Square (RMS) difference with CERES monthly mean OLR is about 2 Wm−2 at 1° × 1° resolution. We quantify the degradation of the OLR product when only one AVHRR instrument is available (as is the case for some periods in the 1980s) and also the improvement when more instruments are available (e.g., using METOP-A, NOAA-15, NOAA-18, and NOAA-19 in 2012). The degradation of the OLR product from AVHRR/1 instruments is also quantified, which is done by “masking” the Channel 5 observations.

Probabilistic Cloud Masking for the Generation of CM SAF Cloud Climate Data Records from AVHRR and SEVIRI Sensors

Cloud screening in satellite imagery is essential for enabling retrievals of atmospheric and surface properties. For climate data record (CDR) generation, cloud screening must be balanced, so both false cloud-free and false cloudy retrievals are minimized. Many methods used in recent CDRs show signs of clear-conservative cloud screening leading to overestimated cloudiness. This study presents a new cloud screening approach for Advanced Very-High-Resolution Radiometer (AVHRR) and Spinning Enhanced Visible and Infrared Imager (SEVIRI) imagery based on the Bayesian discrimination theory. The method is trained on high-quality cloud observations from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) lidar onboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite. The method delivers results designed for optimally balanced cloud screening expressed as cloud probabilities together with information on for which clouds (minimum cloud optical thickness) the probabilities are valid. Cloud screening characteristics over 28 different Earth surface categories were estimated. Using independent CALIOP observations (including all observed clouds) in 2010 for validation, the total global hit rates for AVHRR data and the SEVIRI full disk were 82% and 85%, respectively. High-latitude oceans had the best performance, with a hit rate of approximately 93%. The results were compared to the CM SAF cLoud, Albedo, and surface RAdiation dataset from AVHRR data–second edition (CLARA-A2) CDR and showed general improvements over most global regions. Notably, the Kuipers’ Skill Score improved, verifying a more balanced cloud screening. The new method will be used to prepare the new CLARA-A3 and CLAAS-3 (CLoud property dAtAset using SEVIRI, Edition 3) CDRs in the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF) project.

Retrieval of Global Orbit Drift Corrected Land Surface Temperature from Long-term AVHRR Data

Advanced Very High Resolution Radiometer (AVHRR) sensors provide a valuable data source for generating long-term global land surface temperature (LST). However, changes in the observation time that are caused by satellite orbit drift restrict their wide application. Here, a generalized split-window (GSW) algorithm was implemented to retrieve the LST from the time series AVHRR data. Afterwards, a novel orbit drift correction (ODC) algorithm, which was based on the diurnal temperature cycle (DTC) model and Bayesian optimization algorithm, was also proposed for normalizing the estimated LST to the same local time. This ODC algorithm is pixel-based and it only needs one observation every day. The resulting LSTs from the six-year National Oceanic and Atmospheric Administration (NOAA)-14 satellite data were validated while using Surface Radiation Budget Network (SURFRAD) in-situ measurements. The average accuracies for LST retrieval varied from −0.4 K to 2.0 K over six stations and they also depended on the viewing zenith angle and season. The simulated data illustrate that the proposed ODC method can improve the LST estimate at a similar magnitude to the accuracy of the LST retrieval, i.e., the root-mean-square errors (RMSEs) of the corrected LSTs were 1.3 K, 2.2 K, and 3.1 K for the LST with a retrieval RMSE of 1 K, 2 K, and 3 K, respectively. This method was less sensitive to the fractional vegetation cover (FVC), including the FVC retrieval error, size, and degree of change within a neighboring area, which suggested that it could be easily updated by applying other LST expression models. In addition, ground validation also showed an encouraging correction effect. The RMSE variations of LST estimation that were introduced by ODC were within ±0.5 K, and the correlation coefficients between the corrected LST errors and original LST errors could approach 0.91.

Integration of terrain and AVHRR-derived multi-temporal snow cover data for statistical assessment of avalanches: case study of a part of NW Himalaya

In this paper, the data of Advanced Very High Resolution Radiometer (AVHRR) 1 km resolution of daily time series is used to extract the distribution of seasonal snow in the Chowkibal-Tangdhar (CT) area. Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM) at 90-m resolution has been used to delineate the terrain characteristics such as aspect, slope, and altitude. The study is focused on two major objectives, first is to assess the snow cover variation with a time series analysis and second one is the snow cover changes with respect to the changes in terrain parameters mainly at avalanche prone regions. Study area has 60 registered avalanche sites; most of these avalanches trigger during the winter months when snow deposition takes place in the steep slopes due to successive snowfall spells. Normalized Difference Snow Index (NDSI) method is used to monitor the snow cover of the study area from the satellite image. The multi-temporal analysis of the snow cover derived from the AVHRR data shows that the binary snow cover in the study area increases with time from November to March due to successive snowfalls. After that, the snow cover starts to deplete from the mid-March onwards due to rise in temperature. Maximum snow cover area (SCA) has been found 85.67% and minimum is at 14.33% respectively. An attempt has been made to understand the influence of terrain parameters by extracting the major terrain and snow parameters for each of the avalanche site and correlating it with occurrences of the avalanches. The study suggests that the sites having terrain characteristics of slope 25–45°, aspect 26.45%, and altitude 2500–3500 m has more occurrences of avalanches.

Investigations into the development of a satellite-based aerosol climate data record using ATSR-2, AATSR and AVHRR data over north-eastern China from 1987 to 2012

Abstract. Satellites provide information on the temporal and spatial distributions of aerosols on regional and global scales. With the same method applied to a single sensor all over the world, a consistent data set is to be expected. However, the application of different retrieval algorithms to the same sensor and the use of a series of different sensors may lead to substantial differences, and no single sensor or algorithm is better than any other everywhere and at all times. For the production of long-term climate data records, the use of multiple sensors cannot be avoided. The Along Track Scanning Radiometer (ATSR-2) and the Advanced ATSR (AATSR) aerosol optical depth (AOD) data sets have been used to provide a global AOD data record over land and ocean of 17 years (1995–2012), which is planned to be extended with AOD retrieved from a similar sensor. To investigate the possibility of extending the ATSR data record to earlier years, the use of an AOD data set from the Advanced Very High Resolution Radiometer (AVHRR) is investigated. AOD data sets used in this study were retrieved from the ATSR sensors using the ATSR Dual View algorithm ADV version 2.31, developed by Finnish Meteorological Institute (FMI), and from the AVHRR sensors using the aerosol optical depth over land (ADL) algorithm developed by RADI/CAS. Together, these data sets cover a multi-decadal period (1987–2012). The study area includes two contrasting areas, both in regards to aerosol content and composition and surface properties, i.e. a region over north-eastern China, encompassing a highly populated urban/industrialized area (Beijing–Tianjin–Hebei) and a sparsely populated mountainous area. Ground-based AOD observations available from ground-based sun photometer AOD data in AERONET and CARSNET are used as a reference, together with broadband extinction method (BEM) data at Beijing to cover the time before sun photometer observations became available in the early 2000s. In addition, MODIS-Terra C6.1 AOD data are used as a reference data set over the wide area where no ground-based data are available. All satellite data over the study area were validated against the reference data, showing the qualification of MODIS for comparison with ATSR and AVHRR. The comparison with MODIS shows that AVHRR performs better than ATSR in the north of the study area (40∘ N), whereas further south ATSR provides better results. The validation against sun photometer AOD shows that both AVHRR and ATSR underestimate the AOD, with ATSR failing to provide reliable results in the wintertime. This is likely due to the highly reflecting surface in the dry season, when AVHRR-retrieved AOD traces both MODIS and reference AOD data well. However, AVHRR does not provide AOD larger than about 0.6 and hence is not reliable when high AOD values have been observed over the last decade. In these cases, ATSR performs much better for AOD up to about 1.3. AVHRR-retrieved AOD compares favourably with BEM AOD, except for AOD higher than about 0.6. These comparisons lead to the conclusion that AVHRR and ATSR AOD data records each have their strengths and weaknesses that need to be accounted for when combining them in a single multi-decadal climate data record.

Trends of land surface phenology derived from passive microwave and optical remote sensing systems and associated drivers across the dry tropics 1992–2012

Changes in vegetation phenology are among the most sensitive biological responses to global change. While land surface phenological changes in the Northern Hemisphere have been extensively studied from the widely used long-term AVHRR (Advanced Very High Resolution Radiometer) data, current knowledge on land surface phenological trends and the associated drivers remains uncertain for the tropics. This uncertainty is partly due to the well-known challenges of applying satellite-derived vegetation indices from the optical domain in areas prone to frequent cloud cover. The long-term vegetation optical depth (VOD) product from satellite passive microwaves features less sensitivity to atmospheric perturbations and measures different vegetation traits and functioning as compared to optical sensors. VOD thereby provides an independent and complementary data source for studying land surface phenology and here we performed a combined analysis of the VOD and AVHRR NDVI (Normalized Difference Vegetation Index) datasets for the dry tropics (25°N to 25°S) during 1992–2012. We find a general delay in the VOD derived start of season (SOS) and end of season (EOS) as compared to NDVI derived metrics, however with clear differences among land cover and continents. Pixels characterized by significant phenological trends (P < 0.05) account for up to 20% of the study area for each phenological metric of NDVI and VOD, with large spatial difference between the two sensor systems. About 50% of the pixels studied show significant phenological changes in either VOD or NDVI metrics. Drivers of phenological changes were assessed for pixels of high agreement between VOD and NDVI phenological metrics (serving as a means of reducing noise-related uncertainty). We find rainfall variability and woody vegetation change to be the main forcing variables of phenological trends for most of the dry tropical biomes, while fire events and land cover change are recognized as second-order drivers. Taken together, our study provides new insights on land surface phenological changes and the associated drivers in the dry tropics, as based on the complementary long-term data sources of VOD and NDVI, sensitive to changes in vegetation water content and greenness, respectively.

A Long-Term Historical Aerosol Optical Depth Data Record (1982–2011) Over China From AVHRR

A long-term historical aerosol optical depth (AOD) data set from 1982 to 2011 over China (15–45° N; 75–135° E) with 0.1 spatial resolution has been produced from Advanced Very High Resolution Radiometer (AVHRR) Pathfinder Atmospheres—Extended level-2B data. The spatial distribution pattern shows that high AOD values are found in central and eastern China over the entire period with AODs larger in summer and spring than in autumn and winter. As the high-quality products from AERONET were absent for this period over mainland China, AOD data obtained using the broadband extinction method from solar radiation stations have been used to verify the quality of the AVHRR AOD data set over China. The intercomparison results show that the interannual variation of AOD has been well captured in the variation curve of the AOD monthly mean and the variation trend is also consistent over the whole period. The correlation coefficient of the monthly mean is mostly larger than 0.55, the agreement index is larger than 0.57, and the relative error is less than 21%. Both AVHRR and visibility data sets show high values in regions with rapid economic development. Using Moderate Resolution Imaging Spectroradiometer AOD data as references, it is found that AVHRR AOD from this paper has better accuracy in general than that from Deep Blue (DB) algorithm over China, especially over eastern and southern China, while DB provides more coverage especially over bright surface such as northwest China. This long-term historic AOD data set can be used together with other AOD data sets to study the climate and environmental changes, especially in the 1980s and 1990s.