First Provisional Land Surface Reflectance Product from Geostationary Satellite Himawari-8 AHI

A provisional surface reflectance (SR) product from the Advanced Himawari Imager (AHI) on-board the new generation geostationary satellite (Himawari-8) covering the period between July 2015 and December 2018 is made available to the scientific community. The Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm is used in conjunction with time series Himawari-8 AHI observations to generate 1-km gridded and tiled land SR every 10 minutes during day time. This Himawari-8 AHI SR product includes retrieved atmospheric properties (e.g., aerosol optical depth at 0.47µm and 0.51µm), spectral surface reflectance (AHI bands 1–6), parameters of the RTLS BRDF model, and quality assurance flags. Product evaluation shows that Himawari-8 AHI data on average yielded 35% more cloud-free, valid pixels in a single day when compared to available data from the low earth orbit (LEO) satellites Terra/Aqua with MODIS sensor. Comparisons of Himawari-8 AHI SR against corresponding MODIS SR products (MCD19A1) over a variety of land cover types with the similar viewing geometry show high consistency between them, with correlation coefficients (r) being 0.94 and 0.99 for red and NIR bands, respectively. The high-frequency geostationary data are expected to facilitate studies of ecosystems on daily to diurnal time scales, complementing observations from networks such as the FLUXNET

An Introduction to the Geostationary-NASA Earth Exchange (GeoNEX) Products: 1. Top-of-Atmosphere Reflectance and Brightness Temperature

GeoNEX is a collaborative project led by scientists from NASA, NOAA, and many other institutes around the world to generate Earth monitoring products using data streams from the latest Geostationary (GEO) sensors including the GOES-16/17 Advanced Baseline Imager (ABI), the Himawari-8/9 Advanced Himawari Imager (AHI), and more. An accurate and consistent product of the Top-Of-Atmosphere (TOA) reflectance and brightness temperature is the starting point in the scientific processing pipeline and has significant influences on the downstream products. This paper describes the main steps and the algorithms in generating the GeoNEX TOA products, starting from the conversion of digital numbers to physical quantities with the latest radiometric calibration information. We implement algorithms to detect and remove residual georegistration uncertainties automatically in both GOES and Himawari L1bdata, adjust the data for topographic relief, estimate the pixelwise data-acquisition time, and accurately calculate the solar illumination angles for each pixel in the domain at every time step. Finally, we reproject the TOA products to a globally tiled common grid in geographic coordinates in order to facilitate intercomparisons and/or synergies between the GeoNEX products and existing Earth observation datasets from polar-orbiting satellites

Seasonal comparisons of Himawari-8 AHI and MODIS vegetation indices over latitudinal australian grassland sites

© 2020 by the authors. The Advanced Himawari Imager (AHI) on board the Himawari-8 geostationary (GEO) satellite offers comparable spectral and spatial resolutions as low earth orbiting (LEO) sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) sensors, but with hypertemporal image acquisition capability. This raises the possibility of improved monitoring of highly dynamic ecosystems, such as grasslands, including fine-scale phenology retrievals from vegetation index (VI) time series. However, identifying and understanding how GEO VI temporal profiles would be different from traditional LEO VIs need to be evaluated, especially with the new generation of geostationary satellites, with unfamiliar observation geometries not experienced with MODIS, VIIRS, or Advanced Very High Resolution Radiometer (AVHRR) VI time series data. The objectives of this study were to investigate the variations in AHI reflectances and normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and two-band EVI (EVI2) in relation to diurnal phase angle variations, and to compare AHI VI seasonal datasets with MODIS VIs (standard and sun and view angle-adjusted VIs) over a functional range of dry grassland sites in eastern Australia. Strong NDVI diurnal variations and negative NDVI hotspot effects were found due to differential red and NIR band sensitivities to diurnal phase angle changes. In contrast, EVI and EVI2 were nearly insensitive to diurnal phase angle variations and displayed nearly flat diurnal profiles without noticeable hotspot influences. At seasonal time scales, AHI NDVI values were consistently lower than MODIS NDVI values, while AHI EVI and EVI2 values were significantly higher than MODIS EVI and EVI2 values, respectively. We attributed the cross-sensor differences in VI patterns to the year-round smaller phase angles and backscatter observations from AHI, in which the sunlit canopies induced a positive EVI/ EVI2 response and negative NDVI response. BRDF adjustments of MODIS VIs to solar noon and to the oblique view zenith angle of AHI resulted in strong cross-sensor convergence of VI values (R2 > 0.94, mean absolute difference <0.02). These results highlight the importance of accounting for cross-sensor observation geometries for generating compatible AHI and MODIS annual VI time series. The strong agreement found in this study shows promise in cross-sensor applications and suggests that a denser time series can be formed through combined GEO and LEO measurement synergies

Sun-angle effects on remote-sensing phenology observed and modelled using himawari-8

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Satellite remote sensing of vegetation at regional to global scales is undertaken at considerable variations in solar zenith angle (SZA) across space and time, yet the extent to which these SZA variations matter for the retrieval of phenology remains largely unknown. Here we examined the effect of seasonal and spatial variations in SZA on retrieving vegetation phenology from time series of the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) across a study area in southeastern Australia encompassing forest, woodland, and grassland sites. The vegetation indices (VI) data span two years and are from the Advanced Himawari Imager (AHI), which is onboard the Japanese Himawari-8 geostationary satellite. The semi-empirical RossThick-LiSparse-Reciprocal (RTLSR) bidirectional reflectance distribution function (BRDF) model was inverted for each spectral band on a daily basis using 10-minute reflectances acquired by H-8 AHI at different sun-view geometries for each site. The inverted RTLSR model was then used to forward calculate surface reflectance at three constant SZAs (20°, 40°, 60°) and one seasonally varying SZA (local solar noon), all normalised to nadir view. Time series of NDVI and EVI adjusted to different SZAs at nadir view were then computed, from which phenological metrics such as start and end of growing season were retrieved. Results showed that NDVI sensitivity to SZA was on average nearly five times greater than EVI sensitivity. VI sensitivity to SZA also varied among sites (biome types) and phenological stages, with NDVI sensitivity being higher during the minimum greenness period than during the peak greenness period. Seasonal SZA variations altered the temporal profiles of both NDVI and EVI, with more pronounced differences in magnitude among NDVI time series normalised to different SZAs. When using VI time series that allowed SZA to vary at local solar noon, the uncertainties in estimating start, peak, end, and length of growing season introduced by local solar noon varying SZA VI time series, were 7.5, 3.7, 6.5, and 11.3 days for NDVI, and 10.4, 11.9, 6.5, and 8.4 days for EVI respectively, compared to VI time series normalised to a constant SZA. Furthermore, the stronger SZA dependency of NDVI compared with EVI, resulted in up to two times higher uncertainty in estimating annual integrated VI, a commonly used remote-sensing proxy for vegetation productivity. Since commonly used satellite products are not generally normalised to a constant sun-angle across space and time, future studies to assess the sun-angle effects on satellite applications in agriculture, ecology, environment, and carbon science are urgently needed. Measurements taken by new-generation geostationary (GEO) satellites offer an important opportunity to refine this assessment at finer temporal scales. In addition, studies are needed to evaluate the suitability of different BRDF models for normalising sun-angle across a broad spectrum of vegetation structure, phenological stages and geographic locations. Only through continuous investigations on how sun-angle variations affect spatiotemporal vegetation dynamics and what is the best strategy to deal with it, can we achieve a more quantitative remote sensing of true signals of vegetation change across the entire globe and through time

CIRA annual report FY 2016/2017

Reporting period April 1, 2016-March 31, 2017

Evaluation of atmospheric correction methods for low to high resolutions satellite remote sensing data

The objective of atmospheric correction is to retrieve surface reflectance from the top of atmosphere (TOA) reflectance. However, estimating surface reflectance from the TOA reflectance satellite data requires knowledge about the state of the atmosphere (e.g., water vapor and ozone) and the contribution of aerosols to the atmospheric path radiance. Thus, obtaining precise measurements of these parameters, which is very difficult, is crucial for accurate estimation of surface reflectance. The SREM (Simplified and Robust Surface Reflectance Estimation Method) is a physical-based atmospheric correction method based on the Radiative transfer (RT) equations of the second simulation of the Satellite Signal in Solar Spectrum (6SV). Essentially the SREM is a simplified version of 6SV which does not require Aerosol Optical Depth (AOD), aerosol type, water vapor, and ozone. An initial study showed accuracy comparable to the Landsat operational Surface Reflectance Products (SRProd) which is generated through different RT models using AOD, water vapor, and ozone data. To further validate the SREM under varying atmospheric conditions and at different spatial resolutions, an independent Reference Surface Reflectance (SRRef) dataset was generated using the AERONET (Aerosol Robotic Network) measurements as input to the 6SV RT model. The surface reflectances estimated by SREM (SRSREM) and SRProd from Planet Scope (PS, at 3 m spatial resolution), Sentinel-2 AB (S2AB) Multi-spectral Instrument (MSI, at 10 to 60 m spatial resolution), and Landsat-8 (L8) operational Land Imager (OLI, at 30 m spatial resolution) were validated against SRRef. Results showed that SRSREM performed similar to the SRProd of PS, S2AB MSI, and L8 OLI against SRRef. An inferior performance (R of 0.35 and 0.57) of L8 OLI's SRProd in the coastal blue (SB1) and blue (SB2) bands was observed, compared to SREM. The comparison of SRSREM with SRProd reveals the robustness of SREM, without using AOD, water vapor, and ozone data, for estimation of surface reflectance for all RT models tested. For some dates, SRRef and the SRProd under-corrected and produced higher values than the TOA reflectance, even when the atmosphere was clear but this was not the case for SREM. Analysis of surface reflectance estimation in shadowed areas revealed that the SRRef and SRProd had mainly negative values in coastal blue and blue bands for L8 OLI, while no negative SR value was observed for SREM in any band. These results recommend the utilization of SREM for the provision of surface reflectance products across a range of sensor

CIRA annual report FY 2017/2018

Reporting period April 1, 2017-March 31, 2018

Analysis and integration of regional scale temperature datasets into a seasonal crop monitoring system.

Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.Populations in excess of 20 million people in southern Africa annually face food insecurity. This number increases appreciably when detrimental seasonal climate conditions lead to widespread reductions in crop harvests. This situation has led to the development of regionalscale crop monitoring systems that incorporate crop-specific water balance (CSWB) models for early detection and warning of impending weather-related crop production shortfalls. Early warning of anticipated reductions in crop harvests facilitates early action in responding to potential crises. One such system, used by the Famine Early Warning Systems Network (FEWS NET) in southern Africa for crop monitoring, calculates the water requirements satisfaction index (WRSI) using a CSWB model. Operationally, CSWB models for calculating WRSI have used a static length of growing period (LGP) to bracket the period over which rainfall and evapotranspiration variations can affect crop yields. In the long term, concerns have been raised by some studies on the impact of rising air temperatures on crop production. There is therefore a need to incorporate the impacts of air temperature on crops directly into food security monitoring systems, in order to improve the accuracy of these monitoring systems in identifying and locating weather-related crop production shortfalls. This study sought to assess the potential improvements that can be introduced to the crop monitoring system in general and the WRSI in particular, by incorporating air temperature data into the CSWB model. To address this objective, daily maximum and minimum air temperature grids derived from a general circulation model reanalysis were used to generate thermal time estimates, expressed as growing degree day (GDD) grids for a maize crop. The GDDs were used to estimate the LGP of maize for each pixel of each summer season (which typically runs between around October and March) from 1982/1983 to 2016/2017 in southern Africa. The variable, temperature-driven LGP estimates compared favourably with LGP values obtained from literature for a few sample locations. The variable LGP was used to calculate the WRSI for 35 seasons, and the resultant WRSI showed improved correlation with historical yield estimates compared to the static-LGP WRSI, particularly after the farming practice of planting on multiple dates was taken into consideration. Various expressions of WRSI were considered in the analysis, including WRSI calculated assuming planting at the onset of rains, WRSI aggregated from varying number of separate planting dates, including three and six planting dates as test examples, and WRSI calculated using a modified soil water holding capacity to better capture local soil management practices. Historical maize yields for sub-national administrative units from seven southern African countries were correlated with the various WRSI expressions. Gridded GDD data that reflect the accumulated severity of extreme warm temperatures experienced during the crop growth period, referred to as extreme growing degree days, or eGDD, were also noted to have significant correlations with historical maize yields in several southern African countries. A number of variants of the eGDDs were tested, including eGDDs accumulated throughout the crop’s growth period, eGDDs that only occurred simultaneously with periods of crop water deficit, eGDDs that occurred during the crop flowering stage, and eGDDs scaled by the severity of crop water deficit. In several areas, the various eGDD expressions indicated higher correlations with yield than any of the WRSI variants indicated. The eGDD parameter showed strong correlations with WRSI, suggesting that the accumulated high temperatures were a reflection of the influence of low rainfall and low soil moisture during episodes of high temperature. More work is required to calibrate and refine the temperature-based monitoring parameters that were developed in this study, at local, sub-national scales. In particular, assumptions of the linearity of maize yield response for the various parameters should be tested. Potential improvements of the combined eGDD-water deficit parameter through the incorporation of prediction coefficients and constants should also be tested. A secondary aim of the study was to explore how readily available temperature-related datasets can be utilized to derive air-temperature metrics. To this end, satellite-derived thermal infrared (TIR) brightness temperature data were analysed, and a method was developed for identifying cloud cover, while simultaneously estimating cloud-free diurnal brightness temperature curves, using a single TIR satellite channel. The diurnal brightness temperature curves were developed using a sinusoidal and exponential model for daytime and nighttime respectively, utilizing modifications that enabled the curves to be estimated from two known temperatures at any two given times with cloud-free brightness temperature scenes. Comparison of the cloud mask developed in this study with an existing operational cloud mask based on a methodology developed by the EUMETSAT Satellite Applications Facility for Nowcasting gave an accuracy of 85.4%, when the operational method was considered as truth in a confusion matrix analysis. Situations were identified in which the different cloud detection methods showed superior performance, and could therefore complement each other. A statistical method was also developed for calibrating the cloud-free brightness temperatures to station-observed 2-m air temperatures using relationships between the means and diurnal temperature ranges of the two datasets. This enabled the identification of periods of occurrence of extreme warm air temperatures with a coefficient of determination of 0.91, and demonstrated the potential for the usage of TIR data for generating estimates of useful air temperature metrics. The efficiency of the algorithms that were used for simultaneous cloud masking and generation of cloud-free brightness temperature should be improved, in order to enable the methodology to be scaled up to a regional or global gridded level of analysis. Further work for improving operational gridded air temperature datasets by combining station-observed temperature data, modelled data from global circulation models, satellite-derived modelled cloud-free brightness temperature data and cloud masks is recommended

CIRA annual report FY 2014/2015

Reporting period July 1, 2014-March 31, 2015

The life cycle of anvil cirrus clouds from a combination of passive and active satellite remote sensing

Anvil cirrus clouds form in the upper troposphere from the outflow of ice crystals from deep convective cumulonimbus clouds. By reflecting incoming solar radiation as well as absorbing terrestrial thermal radiation, and re-emitting it at significantly lower temperatures, they play an important role for the Earth’s radiation budget. Nevertheless the processes that govern their life cycle are not well understood and, hence, they remain one of the largest uncertainties in atmospheric remote sensing and climate and weather modelling. In this thesis the temporal evolution of the anvil cirrus properties throughout their life cycle is investigated, as is their relationship with the meteorological conditions. For a comprehensive retrieval of the anvil cirrus properties, a new algorithm for the remote sensing of cirrus clouds called CiPS (Cirrus Properties from SEVIRI) is developed. Utilising a set of artificial neural networks, CiPS combines the large spatial coverage and high temporal resolution of the imaging radiometer SEVIRI aboard the geostationary satellites Meteosat Second Generation, with the high vertical resolution and sensitivity to thin cirrus clouds of the lidar CALIOP aboard the polar orbiting satellite CALIPSO. In comparison to CALIOP, CiPS detects 71 % and 95 % of all cirrus clouds with an ice optical thickness (IOT) of 0.1 and 1.0 respectively. Furthermore, CiPS retrieves the corresponding cloud top height, IOT, ice water path (IWP) and, by parameterisation, effective ice crystal radius. This way, macrophysical, microphysical and optical properties can be combined to interpret the temporal evolution of the anvil cirrus clouds. Together with a tool for identifying convective activity and a new cirrus tracking algorithm, CiPS is used to analyse the life cycle of 132 anvil cirrus clouds observed over southern Europe and northern Africa in July 2015. Although the anvil cirrus clouds grow optically thick during the convective phase, they become thinner at a rapid pace as convection ceases. Two hours after the last observed convective activity, 92±7 % of the anvil cirrus area has IOT_CiPS < 1 and IWP_CiPS < 30 g m−2 on average, with highest probability density around 0.1–0.2 and 1.5–3 g m−2 respectively. During the same time period, the cloud top height is observed to decrease. Since this is observed for both long-lived and short-lived anvil cirrus, it is deduced that in this life phase the amount of ice in the anvil is mainly controlled by sedimentation. This is in line with a corresponding decrease in the estimated effective radius. While the convective strength has no evident effect on the IOT and IWP, stronger vertical updraught is clearly correlated with higher cloud top height and larger effective radius. Larger ice crystals are, however, observed to be removed effectively within 2-3 h after convection has ceased, suggesting that the convective strength has no impact on the ice crystal sizes in ageing anvils. In this life stage, upper tropospheric relative humidity, as derived from ERA5 reanalysis data, is shown to have a larger impact on the anvil cirrus life cycle, where higher relative humidity govern larger and especially more long-lived anvil cirrus clouds