Spectral cross-calibration of VIIRS enhanced vegetation index with MODIS: A case study using year-long global data

© 2015 by the authors; licensee MDPI, Basel, Switzerland. In this study, the Visible Infrared Imaging Radiometer Suite (VIIRS) Enhanced Vegetation Index (EVI) was spectrally cross-calibrated with the Moderate Resolution Imaging Spectroradiometer (MODIS) EVI using a year-long, global VIIRS-MODIS dataset at the climate modeling grid (CMG) resolution of 0.05°-by-0.05°. Our cross-calibration approach was to utilize a MODIS-compatible VIIRS EVI equation derived in a previous study [Obata et al., J. Appl. Remote Sens., vol.7, 2013] and optimize the coefficients contained in this EVI equation for global conditions. The calibrated/optimized MODIS-compatible VIIRS EVI was evaluated using another global VIIRS-MODIS CMG dataset of which acquisition dates did not overlap with those used in the calibration. The calibrated VIIRS EVI showed much higher compatibility with the MODIS EVI than the original VIIRS EVI, where the mean error (MODIS minus VIIRS) and the root mean square error decreased from -0.021 to -0.003 EVI units and from 0.029 to 0.020 EVI units, respectively. Error reductions on the calibrated VIIRS EVI were observed across nearly all view zenith and relative azimuth angle ranges, EVI dynamic range, and land cover types. The performance of the MODIS-compatible VIIRS EVI calibration appeared limited for high EVI values (i.e., EVI > 0.5) due likely to the maturity of the VIIRS dataset used in calibration/optimization. The cross-calibration methodology introduced in this study is expected to be useful for other spectral indices such as the normalized difference vegetation index and two-band EVI

Generating global products of LAI and FPAR from SNPP-VIIRS data: theoretical background and implementation

Leaf area index (LAI) and fraction of photosynthetically active radiation (FPAR) absorbed by vegetation have been successfully generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) data since early 2000. As the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument onboard, the Suomi National Polar-orbiting Partnership (SNPP) has inherited the scientific role of MODIS, and the development of a continuous, consistent, and well-characterized VIIRS LAI/FPAR data set is critical to continue the MODIS time series. In this paper, we build the radiative transfer-based VIIRS-specific lookup tables by achieving minimal difference with the MODIS data set and maximal spatial coverage of retrievals from the main algorithm. The theory of spectral invariants provides the configurable physical parameters, i.e., single scattering albedos (SSAs) that are optimized for VIIRS-specific characteristics. The effort finds a set of smaller red-band SSA and larger near-infraredband SSA for VIIRS compared with the MODIS heritage. The VIIRS LAI/FPAR is evaluated through comparisons with one year of MODIS product in terms of both spatial and temporal patterns. Further validation efforts are still necessary to ensure the product quality. Current results, however, imbue confidence in the VIIRS data set and suggest that the efforts described here meet the goal of achieving the operationally consistent multisensor LAI/FPAR data sets. Moreover, the strategies of parametric adjustment and LAI/FPAR evaluation applied to SNPP-VIIRS can also be employed to the subsequent Joint Polar Satellite System VIIRS or other instruments.Accepted manuscrip

Comparison Between Fractional Vegetation Cover Retrievals from Vegetation Indices and Spectral Mixture Analysis: Case Study of PROBA/CHRIS Data Over an Agricultural Area

In this paper we compare two different methodologies for Fractional Vegetation Cover (FVC) retrieval from Compact High Resolution Imaging Spectrometer (CHRIS) data onboard the European Space Agency (ESA) Project for On-Board Autonomy (PROBA) platform. The first methodology is based on empirical approaches using Vegetation Indices (VIs), in particular the Normalized Difference Vegetation Index (NDVI) and the Variable Atmospherically Resistant Index (VARI). The second methodology is based on the Spectral Mixture Analysis (SMA) technique, in which a Linear Spectral Unmixing model has been considered in order to retrieve the abundance of the different constituent materials within pixel elements, called Endmembers (EMs). These EMs were extracted from the image using three different methods: i) manual extraction using a land cover map, ii) Pixel Purity Index (PPI) and iii) Automated Morphological Endmember Extraction (AMEE). The different methodologies for FVC retrieval were applied to one PROBA/CHRIS image acquired over an agricultural area in Spain, and they were calibrated and tested against in situ measurements of FVC estimated with hemispherical photographs. The results obtained from VIs show that VARI correlates better with FVC than NDVI does, with standard errors of estimation of less than 8% in the case of VARI and less than 13% in the case of NDVI when calibrated using the in situ measurements. The results obtained from the SMA-LSU technique show Root Mean Square Errors (RMSE) below 12% when EMs are extracted from the AMEE method and around 9% when extracted from the PPI method. A RMSE value below 9% was obtained for manual extraction of EMs using a land cover use map

Effects of Surface Heterogeneity Due to Drip Irrigation on Scintillometer Estimates of Sensible, Latent Heat Fluxes and Evapotranspiration over Vineyards

Accurate estimates of sensible (H) and latent (LE) heat fluxes and actual evapotranspiration (ET) are required for monitoring vegetation growth and improved agricultural water management. A large aperture scintillometer (LAS) was used to provide these estimates with the objective of quantifying the effects of surface heterogeneity due to soil moisture and vegetation growth variability. The study was conducted over drip-irrigated vineyards located in a semi-arid region in Albacete, Spain during summer 2007. Surface heterogeneity was characterized by integrating eddy covariance (EC) observations of H, LE and ET; land surface temperature (LST) and normalized difference vegetation index (NDVI) data from Landsat and MODIS sensors; LST from an infrared thermometer (IRT); a data fusion model; and a two-source surface energy balance model. The EC observations showed 16% lack of closure during unstable atmospheric conditions and was corrected using the residual method. The comparison between the LAS and EC measurements of H, LE, and ET showed root mean square difference (RMSD) of 25 W m−2, 19 W m−2, and 0.41 mm day−1, respectively. LAS overestimated H and underestimated both LE and ET by 24 W m−2, 34 W m−2, and 0.36 mm day−1, respectively. The effects of soil moisture on LAS measurement of H was evaluated using the Bowen ratio, β. Discrepancies between HLAS and HEC were higher at β ≤ 0.5 but improved at 1 ≥ β \u3e 0.5 and β \u3e 1.0 with R2 of 0.76, 0.78, and 0.82, respectively. Variable vineyard growth affected LAS performance as its footprints saw lower NDVILAS compared to that of the EC (NDVIEC) by ~0.022. Surface heterogeneity increased during wetter periods, as characterized by the LST–NDVI space and temperature vegetation dryness index (TVDI). As TVDI increased (decreased) during drier (wetter) conditions, the discrepancies between HLAS and HEC, as well as LELAS and LEEC Re decreased (increased). Thresholds of TVDI of 0.3, 0.25, and 0.5 were identified, above which better agreements between LAS and EC estimates of H, LE, and ET, respectively, were obtained. These findings highlight the effectiveness and ability of LAS in monitoring vegetation growth over heterogonous areas with variable soil moisture, its potential use in supporting irrigation scheduling and agricultural water management over large regions

Assessing the Potential of LAPAN-A3 Data for Landuse/landcover Mapping

LAPAN-A3 / LAPAN-IPB is the third generation of micro-satellite developed by Indonesian National Institute of Aeronautics and Space (LAPAN). The satellite carries a multispectral push-broom sensor that can record the earth's surface at the visible and near-infrared spectrum. Being launched in June 2016, there has no been many publications related to the use of LAPAN-A3 multispectral data for landuse/landcover (LULC) mapping. This paper aims to provide information regarding the use of LAPAN-A3 data for the LULC extraction maximum likelihood algorithm as well as neural network and then evaluate the results. The LAPAN-A3 image was geometrically corrected by using Landsat-8 OLI image as reference data. Three test areas with a size of 1200x945 pixels are then selected for pixel-based classification with the two aforementioned algorithms. For comparison, both LAPAN-A3 and Landsat-8 data were classified for 3 test areas. Accuracy assessment was performed on both datasets using manually interpreted SPOT-6 Pansharpened image as reference data. Preliminary results showed that LAPAN-A3 were able to extract  10 different LULC classes, comprises of built-up area, forest, rivers, fishponds, shrubs, wetland forests, rice fields, sea, agricultural land, and bare soil. The overall accuracy of LAPAN-A3 data is generally lower than Landsat-8, which ranges from 49.76% to 71.74%. These results illustrate the potential of LAPAN-A3 data to derive LULC information. The lack of necessary parameters to perform radiometric correction and blurring effect are several issues that need to be solved to improve the accuracy LULC.

THE USE OF NARROW SPECTRAL BANDS FOR IMPROVING REMOTE SENSING ESTIMATIONS OF FRACTIONALLY ABSORBED PHOTOSYNTHETICALLY ACTIVE RADIATION

Most remote sensing estimations of vegetation variables such as leaf area index (LAI), absorbed photosynthetically active radiation (Apar,), and primary production are made using broad band sensors with a bandwidth of approximately 100 nm. However, high resolution spectrometers are available and have not been fully exploited for the purpose of improving estimates of vegetation variables. The study was directed to investigate the use of high spectral resolution spectroscopy for remote sensing estimates of f apar in vegetation canopies in the presence of nonphotosynthetic background materials such as soil and leaf litter. A high spectral resolution measure defined as the chlorophyll absorption ratio index (CARI) was developed for minimizing the effects of nonphotosynthetic materials in the remote estimates of f apar CARI utilizes three bands at 550, 670, and 700 nm with bandwidth of 10 nm. Simulated canopy reflectance of a range of leaf area index (LAI) were generated with the SAIL model using measurements of 42 different soil types as canopy background. CARI calculated from the simulated canopy reflectance was compared with the broad band vegetation indices such as normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), and simple ratio (SR). CARI reduced the effect of nonphotosynthetic background materials in the assessment of vegetation canopy f apar more effectively than broad band vegetation indices

Effect of Relative Spectral Response on Multi-Spectral Measurements and NDVI from Different Remote Sensing Systems

Spectrally derived metrics from remotely sensed data measurements have been developed to improve understanding of land cover and its dynamics. Today there are an increasing number of remote sensing systems with varying characteristics that provide a wide range of data that can be synthesized for Earth system science. A more detailed understanding is needed on how to correlate measurements between sensors. One factor that is often overlooked is the effect of a sensor's relative spectral response (RSR) on broadband spectral measurements. This study examined the variability in spectral measurements due to RSR differences between different remote sensing systems and the implications of these variations on the accuracy and consistency of the normalized difference vegetation index (NDVI). A theoretical model study and a sensor simulation study of laboratory and remotely sensed hyper-spectral data of known land cover types was developed to provide insight into the effect on NDVI due to differences in RSR measurements of various land cover signatures. This research has shown that the convolution of RSR, signature reflectance and solar irradiance in land cover measurements leads to complex interactions and generally small differences between sensor measurements. Error associated with cross-senor calibration of signature measurements and the method of band radiance conversion to reflectance also contributed to measurement discrepancies. The effect of measurement discrepancies between sensors on the accuracy and consistency of NDVI measurements of vegetation was found to be dependent on the increasing sensitivity of NDVI to decreasing band measurements. A concept of isolines of NDVI error was developed as a construct for understanding and predicting the effect of differences in band measurements between sensors on NDVI. NDVI difference of less than 0.05 can be expected for many sensor comparisons of vegetation, however, some cases will lead to higher differences. For vegetation signatures used in this study, maximum effect on NDVI from measurement differences was 0.063 with an average of 0.023. For sensors with well aligned RSRs such as Landsat 7 ETM+ and MODIS, NDVI differences in the range of 0.01 are possible

Urban Overheating - Progress on Mitigation Science and Engineering Applications

The combination of global warming and urban sprawl is the origin of the most hazardous climate change effect detected at urban level: Urban Heat Island, representing the urban overheating respect to the countryside surrounding the city. This book includes 18 papers representing the state of the art of detection, assessment mitigation and adaption to urban overheating. Advanced methods, strategies and technologies are here analyzed including relevant issues as: the role of urban materials and fabrics on urban climate and their potential mitigation, the impact of greenery and vegetation to reduce urban temperatures and improve the thermal comfort, the role the urban geometry in the air temperature rise, the use of satellite and ground data to assess and quantify the urban overheating and develop mitigation solutions, calculation methods and application to predict and assess mitigation scenarios. The outcomes of the book are thus relevant for a wide multidisciplinary audience, including: environmental scientists and engineers, architect and urban planners, policy makers and students

Advances in Remote Sensing-based Disaster Monitoring and Assessment

Remote sensing data and techniques have been widely used for disaster monitoring and assessment. In particular, recent advances in sensor technologies and artificial intelligence-based modeling are very promising for disaster monitoring and readying responses aimed at reducing the damage caused by disasters. This book contains eleven scientific papers that have studied novel approaches applied to a range of natural disasters such as forest fire, urban land subsidence, flood, and tropical cyclones