Moderate Resolution Imaging Spectroradiometer Reflectance Research Articles

The Digital Change Detection for Low Resolution Satellite Images in Weathering Estimation

The detection and estimation of weathering conditions have become a very important daily necessity in our life. For this purpose, several satellites of low resolution imagery were launched by the weathering and environmental agencies. The important weather paremeters are temperuter, wind direction, velocity, clould and humidity, etc. The low resolution images often deal with large-scale phenomena and the interpretation and projection of the produced data requires continuous development of tools and criteria. In this paper, the low spatial resolution data generated by the moderate resolution imaging spectroradiometer (MODIS) were used to monitor the cloud density and direction above Iraq and its neighboring countries for the period from 14-12-2021 to 23-12-2021. The MODIS reflectance and surface temperature (HDF format) data were used and processed to achieve the purpose of this research. The reflectance data were projected according to a sinusoidal system due to the presence of three Earth zones distributions (37, 38, and 39, N). The cloud density was estimated from the digital values and the wind direction was estimated from the bands animation. The direction of wind for the cloudy condition was from the North-East. The digital data processing included data preparation and selection, georeference (adding a projection system), features extraction, and displaying the final results. All data were evaluated using ENVI (Environment for Visualizing Images, ver. 4.5.)

Aboveground biomass of marshes in Northeast China: Spatial pattern and annual changes responding to climate change

Examining vegetation aboveground biomass (AGB) changes is important to understanding wetland carbon sequestration. Here, we combined the field-measured AGB data (458 samples) from 2009 to 2021, moderate resolution imaging spectroradiometer reflectance products, and climatic data to reveal the AGB variations of marshes in Northeast China by comparing various models driven by different indicators. The results indicated that random forest model driven by six vegetation indices, land surface temperature, and land surface water index achieved accurate marsh AGB estimation with R2 being 0.78 and relative error being 16.71%. The mean marsh AGB in Northeast China from 2000 to 2021 was 682.89 ± 31.69 g·m−2, which generally increased from north to south in space. Temporally, annual marsh AGB declined slowly at a rate of 3.45 g·m−2·year−1 during the past 21 years driven mainly by the decrease in summer mean temperature that was characterized by a significantly positive correlation between them. Nevertheless, we highlighted that the temporal changes of marsh AGB spatially varied in response to inconsistent climate change, thus place-based measures are required for sustainable management of marshes.

Nonlinear Detector Response of Aqua MODIS Land Imaging Bands

The detectors in the Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) reflective solar bands (RSBs) are calibrated assuming a linear relationship between the observed top-of-atmosphere radiance and the instrument output response. Any nonlinear behavior in the system response can contribute to errors in NASA's Level 1B MODIS reflectance and radiance products, which are used in a wide variety of Earth science applications. While most RSB detectors continue to have very linear behavior even after more than 18 years of operation, we present evidence that the detectors in Aqua MODIS bands 1 (645 nm) and 2 (858 nm) have deviations from gain linearity that change over the mission by up to 3%. We show a clear divergence between gain measurements made with the onboard solar diffuser (SD) at two different radiance levels, achieved by collecting data both with and without an attenuation screen. Radiance trends from lunar calibrations and typical desert and ocean scenes are also compared. We present a simple method to characterize the response nonlinearity using a quadratic function by combining the results of the two sets of SD calibrations. Applying this quadratic calibration algorithm to the desert and lunar data results in improved agreement in their long-term radiance trends. A clear understanding of the magnitude of the gain nonlinearity could be useful to help improve the quality of numerous MODIS science products, including the land imaging applications that rely heavily on these bands.

An Automatic Method to Detect Lake Ice Phenology Using MODIS Daily Temperature Imagery

Lake ice phenology is a climate-sensitive indicator. However, ground-based monitoring suffers from the limitations of human vision and the difficulty of its implementation in harsh environments. Remote sensing provides great potential to detect lake ice phenology. In this study, a new automated method was developed to extract lake ice phenology parameters by capturing the temporal pattern of the transitional water/ice phase using a parameterized time function. The method is based on Moderate-Resolution Imaging Spectroradiometer (MODIS) daily temperature products, which have unique potential for monitoring lake ice cover as a result of providing four observations per day at 1 km spatial resolution from 2002 to 2016. Three seasonally ice-covered lakes with different characteristics in different climate regions were selected to test the method during the period of 2002–2016. The temporal pattern of water/ice transition phase was determined on the basis of unfrozen water cover fraction extracted from the MODIS daily temperature data, and was compared with the MODIS snow and reflectance products and Landsat images. A good agreement with an R2 of above 0.8 was found when compared with the MODIS snow product. The annual variation of extracted ice phenology dates showed good consistency with the MODIS reflectance and AMSR-E/2 products. The approach was then applied to nine seasonally ice-covered lakes in northern China from 2002 to 2016. The strongest tendency towards a later freeze-up start date was revealed in Lake Qinghai (6.31 days/10 yr) among the lakes in Tibetan plateau, and the break-up start and end dates rapidly shifted towards earlier dates in Lake Hulun (−3.73 days/10 yr; −5.02 days/10 yr). The method is suitable for estimating and monitoring ice phenology on different types of lakes over large scales and has a strong potential to provide valuable information on the responses of ice processes to climate change.

Modelling Daily Gross Primary Productivity with Sentinel-2 Data in the Nordic Region–Comparison with Data from MODIS

The high-resolution Sentinel-2 data potentially enable the estimation of gross primary productivity (GPP) at finer spatial resolution by better capturing the spatial variation in a heterogeneous landscapes. This study investigates the potential of 10 m resolution reflectance from the Sentinel-2 Multispectral Instrument to improve the accuracy of GPP estimation across Nordic vegetation types, compared with the 250 m and 500 m resolution reflectance from the Moderate Resolution Imaging Spectroradiometer (MODIS). We applied linear regression models with inputs of two-band enhanced vegetation index (EVI2) derived from Sentinel-2 and MODIS reflectance, respectively, together with various environmental drivers to estimate daily GPP at eight Nordic eddy covariance (EC) flux tower sites. Compared with the GPP from EC measurements, the accuracies of modelled GPP were generally high (R2 = 0.84 for Sentinel-2; R2 = 0.83 for MODIS), and the differences between Sentinel-2 and MODIS were minimal. This demonstrates the general consistency in GPP estimates based on the two satellite sensor systems at the Nordic regional scale. On the other hand, the model accuracy did not improve by using the higher spatial-resolution Sentinel-2 data. More analyses of different model formulations, more tests of remotely sensed indices and biophysical parameters, and analyses across a wider range of geographical locations and times will be required to achieve improved GPP estimations from Sentinel-2 satellite data.

Modeling Snow Surface Spectral Reflectance in a Land Surface Model Targeting Satellite Remote Sensing Observations

Snow surface spectral reflectance is very important in the Earth’s climate system. Traditional land surface models with parameterized schemes can simulate broadband snow surface albedo but cannot accurately simulate snow surface spectral reflectance with continuous and fine spectral wavebands, which constitute the major observations of current satellite sensors; consequently, there is an obvious gap between land surface model simulations and remote sensing observations. Here, we suggest a new integrated scheme that couples a radiative transfer model with a land surface model to simulate high spectral resolution snow surface reflectance information specifically targeting multisource satellite remote sensing observations. Our results indicate that the new integrated model can accurately simulate snow surface reflectance information over a large spatial scale and continuous time series. The integrated model extends the range of snow spectral reflectance simulation to the whole shortwave band and can predict snow spectral reflectance changes in the solar spectrum region based on meteorological element data. The kappa coefficients (K) of both the narrowband snow albedo targeting Moderate Resolution Imaging Spectroradiometer (MODIS) data simulated by the new integrated model and the retrieved snow albedo based on MODIS reflectance data are 0.5, and both exhibit good spatial consistency. Our proposed narrowband snow albedo simulation scheme targeting satellite remote sensing observations is consistent with remote sensing satellite observations in time series and can predict narrowband snow albedo even during periods of missing remote sensing observations. This new integrated model is a significant improvement over traditional land surface models for the direct spectral observations of satellite remote sensing. The proposed model could contribute to the effective combination of snow surface reflectance information from multisource remote sensing observations with land surface models.

Satellite-based reflectances capture large fraction of variability in global gross primary production (GPP) at weekly time scales

A perception has emerged, based on several studies, that satellite-based reflectances are limited in terms of their ability to predict gross primary production (GPP) globally at weekly temporal scales. The basis for this inference is in part that reflectances, particularly expressed in the form of vegetation indices (VIs), convey information about potential rather than actual photosynthesis, and they are sensitive to non-green substances (e.g., soil, woody branches, and snow) as well as to chlorophyll. Previous works have suggested that processing and quality control of satellite-based reflectance data play an important role in their interpretation. In this study, we use high quality reflectance data from the MODerate-resolution Imaging Spectroradiometer (MODIS) data to train neural networks that are used to upscale GPP estimated from eddy covariance flux tower measurements globally. We quantify the ability of the machine learning approaches to capture GPP variability at daily to interannual time scales. Our results show that MODIS reflectances, when paired only with potential short-wave radiation, are able to capture a large fraction of GPP variability (approximately 77%) at daily to weekly time scales. Additional meteorological information (temperature, water vapor deficit, soil water content, ET, and incident radiation) captures only a few more percent of the GPP variability. The meteorological information is used most effectively when information about plant functional type and climate classification is included. We show that machine learning can be a useful tool for estimating GPP uncertainties as well as GPP itself from upscaling methods. Our estimated global annual mean GPP for 2007 is 142.5 ± 7.7 Pg C y−1, which is higher than some other satellite-based estimates but within the range of other reported observation-, model-, and hybrid-based values.

On the Generation of Gapless and Seamless Daily Surface Reflectance Data

The land surface reflectance data are indispensable to generate many other land products. Global land surface reflectance data have been routinely produced from remote sensing sensors aboard different satellite platforms. However, the original data, especially the daily data, suffer from a large number of spatial gaps, which result from atmospheric contamination and instrument deficiencies. This seriously limits their further applications. Many composite products with less spatial gaps have been generated to solve the above problem, but they easily sacrifice their temporal resolutions of original data. Even worse, they cannot be directly implemented in realistic applications because of the noise and composite seams. This paper proposes a temporal–spatial reconstruction method (TSRM) to generate daily gapless and seamless land surface reflectance data. The TSRM integrates both temporal and spatial information for recovering different land cover types using three processing steps. First, spatial gaps are coarsely filled with multiyear weighted average (Step1). After that, all the gaps that are not filled in the first step are interpolated by using harmonic analysis of time series with true value constraint (Step2). Finally, the reconstructed results in the last step are seamlessly processed using the Poisson image editing method, and the seamless daily reflectance data set is generated (Step3). The Moderate Resolution Imaging Spectroradiometer reflectance data set (MOD09GA and MYD09GA) on two testing areas is selected to verify the performance of the proposed TSRM. Experimental results show that the TSRM has good performance with regard to maintaining the temporal and spatial integrity of the daily land surface reflectance data. Results on different testing sites also demonstrate that the TSRM preserves spectral integrity with clear seasonal trends for each spectral band.

Modeling Anisotropic Reflectance Over Composite Sloping Terrain

Heterogeneous terrain significantly complicates signals received by airborne or satellite sensors. It has been demonstrated that both solar direct beam and diffuse skylight illumination conditions are significant factors influencing the anisotropy of reflectance over mountainous areas. Several models and methods have been developed to account for topographic effects on surface reflectance at the pixel level in remote sensing. However, subtopographic effects are generally neglected for low-spatial-resolution pixels due to the complex law of radiative transfer and the limitations of higher spatial resolution digital elevation models, which can lead to deviations in reflectance estimation. Accurately estimating the subtopographic effects on anisotropic reflectance over composite sloping terrain under different illumination conditions presents a challenge for remote sensing models and applications. In this paper, the diffused equivalent slope model (dESM) was developed, which is an anisotropic reflectance simulation model coupled with diffuse skylight over composite sloping terrain. The corresponding subtopographic impact factor was also proposed to exhibit how microslope topography affects reflectance over composite sloping terrain under different illumination conditions. Simulated reflectance data sets simulated by the radiosity method and Moderate Resolution Imaging Spectroradiometer reflectance data were used to evaluate the performance of the dESM model. The results reveal that the dESM model can accurately capture the reflectance anisotropy over composite sloping terrain under different illumination conditions, and the subtopographic impact factor can account for the effects of microslope topography, shadow, and illumination conditions.

Estimation of gross primary production in Moso bamboo forest based on light-use efficiency derived from MODIS reflectance data

ABSTRACTAssessing the contribution of Moso bamboo (Phyllostachys pubescens) forest to forest ecosystem carbon storage requires accurate estimation of gross primary production (GPP). Based on measurements of light-use efficiency (LUE), defined as the ratio of measured GPP to photosynthetically active radiation (PAR), from the eddy covariance flux tower, the linear regression model and partial least squares regression model were used for estimation of LUE using the Moderate-Resolution Imaging Spectroradiometer (MODIS) reflectance data. GPP estimates were then calculated by the product of LUE estimates and PAR (named the LUE-PAR model), which was compared with GPP from the GPP algorithm designed for the MODIS sensor aboard the Aqua and Terra platforms (MOD17A2 model) and the EC-LUE model. The results revealed the PLS model performed better than the linear regression model in LUE estimation but had lager uncertainties in high and low LUE values. GPP estimates driven by a MODIS-based radiation product with high spatial resolution was more accurate than those driven by Modern-Era Retrospective Analysis for Research and Applications (MERRA) radiation product from the NASA’s Global Modelling and Assimilation Office data set. The LUE-PAR model had the highest accuracy than the other two LUE models. The GPP values derived from the EC-LUE model driven by photosynthetically active radiation (PAR) from MERRA and maximum LUE from the EC data were overestimated due to the overestimation in MERRA radiation product. The GPP values derived from the MOD17A2 model driven by PAR from the MERRA and maximum LUE from the biome properties look-up table were underestimated due to underestimation in the maximum LUE of Moso bamboo forest. This study implied that the LUE-PAR model driven by LUE estimates from the PLS model and PAR from MERRA is a superior approach in improving GPP simulations, and PAR products with high spatial resolution and accurate species-specific maximum LUE are necessary for the LUE models in estimating GPP at regional scale.

Monitoring the On-Orbit Calibration of Terra MODIS Reflective Solar Bands Using Simultaneous Terra MISR Observations

On December 18, 2015, the Terra spacecraft completed 16 years of successful operation in space. Terra has five instruments designed to facilitate scientific measurements of the earth’s land, ocean, and atmosphere. The Moderate Resolution Imaging Spectroradiometer (MODIS) and the Multiangle Imaging Spectroradiometer (MISR) instruments provide information for the temporal studies of the globe. After providing over 16 years of complementary measurements, a synergistic use of the measurements obtained from these sensors is beneficial for various science products. The 20 reflective solar bands (RSBs) of MODIS are calibrated using a combination of solar diffuser and lunar measurements, supplemented by measurements from pseudoinvariant desert sites. MODIS views the on-board calibrators and the earth via a two-sided scan mirror at three spatial resolutions: 250 m using 40 detectors in bands 1 and 2, 500 m using 20 detectors in bands 3 and 4, and 1000 m using 10 detectors in bands 8–19 and 26. Simultaneous measurements of the earth’s surface are acquired in a push-broom fashion by MISR at nine view angles spreading out in the forward and backward directions along the flight path. While the swath width for MISR acquisitions is 360 km, MODIS scans a wider swath of 2330 km via its two-sided scan mirror. The reflectance of the MODIS scan mirror has an angle dependence characterized by the response versus scan angle (RVS). Its on-orbit change is derived using the gain from a combination of on-board and earth-view measurements. The on-orbit RVS for MODIS has experienced a significant change, especially for the short-wavelength bands. The on-orbit RVS change for the short-wavelength bands (bands 3, 8, and 9) at nadir is observed to be greater than 10% over the mission lifetime. Due to absence of a scanning mechanism, MISR can serve as an effective tool to evaluate and monitor the on-orbit performance of the MODIS RVS. Furthermore, it can also monitor the detector and scan-mirror differences for the MODIS bands using simultaneous measurements from earth-scene targets, e.g., North Atlantic Ocean and North African desert. Simultaneous measurements provide the benefit of minimizing the impact of earth-scene features while comparing the radiometric performance using vicarious techniques. Long-term observations of both instruments using select ground targets also provide an evaluation of the long-term calibration stability. The goal of this paper is to demonstrate the use of MISR to monitor and enhance the on-orbit calibration of the MODIS RSB. The radiometric calibration requirements for the MODIS RSB are ±2% in reflectance and ±5% in radiance at typical radiance levels within ±45° of nadir. The results show that the long-term changes in the MODIS reflectance at nadir frames are generally within 1%. The MODIS level 1B calibrated products, generated after correcting for the on-orbit changes in the gain and RVS, do not have any correction for changes in the instrument’s polarization sensitivity. The mirror-side-dependent polarization sensitivity exhibits an on-orbit change, primarily in the blue bands, that manifests in noticeable mirror side differences in the MODIS calibrated products. The mirror side differences for other RSB are observed to be less than 1%, therefore demonstrating an excellent on-orbit performance. The detector differences in the blue bands of MODIS exhibit divergence in recent years beyond 1%, and a calibration algorithm improvement has been identified to mitigate this effect. Short-term variations in the recent year caused by the forward updates were identified in bands 1 and 2 and are planned to be corrected in the next reprocess.

Multi-factor modeling of above-ground biomass in alpine grassland: A case study in the Three-River Headwaters Region, China

In this study, we evaluate various methods for estimating the above-ground biomass (AGB) of alpine grassland vegetation using Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices, in combination with long-term climate and grassland monitoring data collected at 15 site-specific stations, in the pastoral area of southern Qinghai Province (i.e., the Three-River Headwaters Region) of China. The results show that (1) over the past 12 years, there were considerable spatial variations in the grassland AGB and NDVI, with the average AGB in the peak period of grassland growth in the range of 329-3653 kg DW/ha, corresponding to an average NDVI of 0.25-0.72; (2) Grassland AGB is affected by various factors, such as geographic location, topography, climate, soil, and grass types. Single-factor AGB models only account for 15-49% of the variations in the grassland AGB during the peak period of grass growth, with NDVI-based AGB model to be the best (46%) among all linear remote sensing models we tested; and (3) although the multi-factor model (based on latitude, longitude, and grass cover and height) performs the best (70%) in estimating the AGB, it is not possible for operation due to the current difficulty of grass height modeling. The alternative and operational multi-factor model f(x,y,c) (latitude, longitude, and grass cover) can achieve reasonable estimation of AGB (63%), with the grass cover modeled from the MODIS reflectance, which would be further improved in conjunction with unmanned aerial vehicle technology in the future. Using this model f(x,y,c), the root-mean-square error (RMSE) of AGB estimation is reduced by 20% (i.e., 151 kg DW/ha) as compared with the best single-factor model based on the NDVI (RMSE of 887 kg DW/ha). (C) 2016 Elsevier Inc. All rights reserved.

How big is an OMI pixel?

Abstract. The Ozone Monitoring Instrument (OMI) is a push-broom imaging spectrometer, observing solar radiation backscattered by the Earth's atmosphere and surface. The incoming radiation is detected using a static imaging CCD (charge-coupled device) detector array with no moving parts, as opposed to most of the previous satellite spectrometers, which used a moving mirror to scan the Earth in the across-track direction. The field of view (FoV) of detector pixels is the solid angle from which radiation is observed, averaged over the integration time of a measurement. The OMI FoV is not quadrangular, which is common for scanning instruments, but rather super-Gaussian shaped and overlapping with the FoV of neighbouring pixels. This has consequences for pixel-area-dependent applications, like cloud fraction products, and visualisation.The shapes and sizes of OMI FoVs were determined pre-flight by theoretical and experimental tests but never verified after launch. In this paper the OMI FoV is characterised using collocated MODerate resolution Imaging Spectroradiometer (MODIS) reflectance measurements. MODIS measurements have a much higher spatial resolution than OMI measurements and spectrally overlap at 469 nm. The OMI FoV was verified by finding the highest correlation between MODIS and OMI reflectances in cloud-free scenes, assuming a 2-D super-Gaussian function with varying size and shape to represent the OMI FoV. Our results show that the OMPIXCOR product 75FoV corner coordinates are accurate as the full width at half maximum (FWHM) of a super-Gaussian FoV model when this function is assumed. The softness of the function edges, modelled by the super-Gaussian exponents, is different in both directions and is view angle dependent.The optimal overlap function between OMI and MODIS reflectances is scene dependent and highly dependent on time differences between overpasses, especially with clouds in the scene. For partially clouded scenes, the optimal overlap function was represented by super-Gaussian exponents around 1 or smaller, which indicates that this function is unsuitable to represent the overlap sensitivity function in these cases. This was especially true for scenes before 2008, when the time differences between Aqua and Aura overpasses was about 15 min, instead of 8 min after 2008. During the time between overpasses, clouds change the scene reflectance, reducing the correlation and influencing the shape of the optimal overlap function.

Simulating aerosols over Arabian Peninsula with CHIMERE: Sensitivity to soil, surface parameters and anthropogenic emission inventories

A three dimensional chemistry transport model, CHIMERE, was used to simulate the aerosol optical depths (AOD) over the Arabian Peninsula desert with an offline coupling of Weather Research and Forecasting (WRF) model. The simulations were undertaken with: (i) different horizontal and vertical configurations, (ii) new datasets derived for soil/surface properties, and (iii) EDGAR-HTAP anthropogenic emissions inventories. The model performance evaluations were assessed: (i) qualitatively using MODIS (Moderate-Resolution Imaging Spectroradiometer) deep blue (DB) AOD data for the two local dust events of August 6th and 23rd (2013), and (ii) quantitatively using AERONET (Aerosol Robotic Network) AOD observations, CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) aerosol extinction profiles, and AOD simulations from various forecast models. The model results were observed to be highly sensitive to erodibility and aerodynamic surface roughness length. The use of new datasets on soil erodibility, derived from the MODIS reflectance, and aerodynamic surface roughness length (z0), derived from the ERA-Interim datasets, significantly improved the simulation results. Simulations with the global EDGAR-HTAP anthropogenic emission inventories brought the simulated AOD values closer to the observations. Performance testing of the adapted model for the Arabian Peninsula domain with improved datasets showed good agreement between AERONET AOD measurements and CHIMERE simulations, where the correlation coefficient (R) is 0.6. Higher values of the correlation coefficients and slopes were observed for the dusty periods compared to the non-dusty periods.

Agricultural drought monitoring using MODIS-based drought indices over the USA Corn Belt

In 2012, the USA Corn Belt, an intensive agricultural region of the USA, was hit by a widespread severe drought, affecting states such as Illinois, Iowa, Nebraska, and Indiana. In this study, time series (2000–2012) of Moderate Resolution Imaging Spectroradiometer (MODIS) measurements were investigated to assess the 2012 drought conditions during the corn-growing season. Seven MODIS indices generated based on eight day MODIS reflectance and land surface temperature (LST) products were examined with standardized precipitation index (SPI) and Palmer-Z across the Corn Belt to evaluate the relative performance of each MODIS index to detect agricultural drought. The normalized difference infrared index (NDII6) anomaly shows the highest correlation coefficient (r) with SPI at three time scales and correlates best with Palmer-Z, which suggests good sensitivity of the NDII6 anomaly to precipitation and moisture deficiency in agricultural areas. The temporal and spatial features of drought provided by MODIS indices were compared with maps of the USA Drought Monitor (USDM), the current advanced tool for drought monitoring. The rapid intensification of drought across the Corn Belt in 2012 summer captured by MODIS index anomalies agreed with the changes of USDM maps quite well, especially in August and September when extreme drought occurred. Through comparison with the USDM drought map, the NDII6 anomaly demonstrated an advantage in monitoring drought condition over irrigated land and showed the potential to advance fine-scale agricultural drought monitoring by providing more detailed spatial characterization.

Mapping of soil properties and land degradation risk in Africa using MODIS reflectance

There is a need for up-to-date assessments and maps of soil properties and land health at scales relevant for decision-making and management, including for properties that are dynamic and hence change in response to management. Also, there is a need for approaches to soil mapping that capture the ever increasing effects that humans are having on the environment in general and specifically on soil properties worldwide. In this paper, we develop models for digital soil mapping based on remote sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS) platform for Africa. The article presents maps of soil organic carbon (SOC), pH, sand and sum of exchangeable bases, as well as prevalence of root-depth restrictions in the upper 50cm of the soil profile. Prediction models were developed based on spatially balanced field survey data, representing all major climate zones on the continent. The prediction models for soil property mapping performed well, with overall RMSEP values of 10.6, 0.34, 9.1, and 6.5 for SOC, pH, sand, and sum of bases, respectively. The accuracy of the prediction model for root-depth restrictions was 77%, with an AUC of 0.85 and Cohen's kappa value of 0.52 when averaged across predictions run on independent test data. The methods and maps developed can provide much improved identification of soil and land health constraints, and spatial targeting of land management interventions at various scales, informing both policy and practice.

Calculation of albedo on complex terrain using MODIS data: a case study in Taihang Mountain of China

Albedo is one of the import parameters for the routine applications of various remote sensing models, and most methods for estimating albedo are only applicable in plain areas with relatively flat topography. Due to the existence of the undulant terrain in mountain areas, the incoming radiance can be decomposed into beam, diffuse, and terrain radiance, which are affected by atmospheric transmittance, solar zenith and azimuth angles, terrain slope and aspect, etc. The existence of partial shadow in low-resolution pixel makes the calculation of albedo more complicated in the mountain areas. This paper presents a rapid and operational method for estimating albedo on complex terrain using high-resolution Digital Elevation Model (DEM) and MODerate-resolution Imaging Spectroradiometer (MODIS) data with sensor view angles less than 20. Shadow coverage ratio of the low-resolution map was retrieved from high-resolution shadow map, and was used for the correction of beam radiance. The parameters used for the atmospheric correction of narrowband beam radiance and weighting coefficients for the calculation of broadband albedo using MODIS with and without band 5 were developed based on the SMARTS2 simulation under different atmospheric conditions. Taken the Taihang Mountain in the North China as the study area, the calculated reflectance and albedo were compared with the MODIS reflectance and albedo products. The maximum deviations between estimated and blue-sky albedos were 0.0155 and +0.0151 in plain areas, and 0.00667 and 0.01747 in the mountain areas, respectively. Good agreements indicate that the procedure presented in this paper is applicable for the albedo calculation in the mountain areas using MODIS images and high-resolution DEM.

Characterizing the Pixel Footprint of Satellite Albedo Products Derived from MODIS Reflectance in the Heihe River Basin, China

The adjacency effect and non-uniform responses complicate the precise delimitation of the surface support of remote sensing data and their derived products. Thus, modeling spatial response characteristics (SRCs) prior to using remote sensing information has become important. A point spread function (PSF) is typically used to describe the SRCs of the observation cells from remote sensors and is always estimated in a laboratory before the sensor is launched. However, research on the SRCs of high-order remote sensing products derived from the observations remains insufficient, which is an obstacle to converting between multi-scale remote sensing products and validating coarse-resolution products. This study proposed a method that combines simulation and validation to establish SRC models of coarse-resolution albedo products. Two series of commonly used 500-m/1-km resolution albedo products, which are derived from Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data, were investigated using 30-m albedo products that provide the required sub-pixel information. The analysis proves that the size of the surface support of each albedo pixel is larger than the nominal resolution of the pixel and that the response weight is non-uniformly distributed, with an elliptical Gaussian shape. The proposed methodology is generic and applicable for analyzing the SRCs of other advanced remote sensing products.

Development of a High Resolution BRDF/Albedo Product by Fusing Airborne CASI Reflectance with MODIS Daily Reflectance in the Oasis Area of the Heihe River Basin, China

A land-cover-based linear BRDF (bi-directional reflectance distribution function) unmixing (LLBU) algorithm based on the kernel-driven model is proposed to combine the compact airborne spectrographic imager (CASI) reflectance with the moderate resolution imaging spectroradiometer (MODIS) daily reflectance product to derive the BRDF/albedo of the two sensors simultaneously in the foci experimental area (FEA) of the Heihe Watershed Allied Telemetry Experimental Research (HiWATER), which was carried out in the Heihe River basin, China. For each land cover type, an archetypal BRDF, which characterizes the shape of its anisotropic reflectance, is extracted by linearly unmixing from the MODIS reflectance with the assistance of a high-resolution classification map. The isotropic coefficients accounting for the differences within a class are derived from the CASI reflectance. The BRDF is finally determined by the archetypal BRDF and the corresponding isotropic coefficients. Direct comparisons of the cropland archetypal BRDF and CASI albedo with in situ measurements show good agreement. An indirect validation which compares retrieved BRDF/albedo with that of the MCD43A1 standard product issued by NASA and aggregated CASI albedo also suggests reasonable reliability. LLBU has potential to retrieve the high spatial resolution BRDF/albedo product for airborne and spaceborne sensors which have inadequate angular samplings. In addition, it can shorten the timescale for coarse spatial resolution product like MODIS.

An Improved Endmember Selection Method Based on Vector Length for MODIS Reflectance Channels

Endmember selection is the basis for sub-pixel land cover classifications using multiple endmember spectral mixture analysis (MESMA) that adopts variant endmember matrices for each pixel to mitigate errors caused by endmember variability in SMA. A spectral library covering a large number of endmembers can account for endmember variability, but it also lowers the computational efficiency. Therefore, an efficient endmember selection scheme to optimize the library is crucial to implement MESMA. In this study, we present an endmember selection method based on vector length. The spectra of a land cover class were divided into subsets using vector length intervals of the spectra, and the representative endmembers were derived from these subsets. Compared with the available endmember average RMSE (EAR) method, our approach improved the computational efficiency in endmember selection. The method accuracy was further evaluated using spectral libraries derived from the ground reference polygon and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery respectively. Results using the different spectral libraries indicated that MESMA combined with the new approach performed slightly better than EAR method, with Kappa coefficient improved from 0.75 to 0.78. A MODIS image was used to test the mapping fraction, and the representative spectra based on vector length successfully modeled more than 90% spectra of the MODIS pixels by 2-endmember models.