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To improve our capacity to map long-term vegetation dynamics in heterogeneous landscapes, this study proposed a new prior knowledge-based spatiotemporal enhancement method, namely, PK-STEM, to fuse MODIS and Landsat FPAR products following the remote sensing trend surface framework. PK-STEM uses historical Landsat FPAR images as prior knowledge and fuses them with new satellite-derived FPAR data. PK-STEM can work in three modes: 1) using only MODIS data; 2) using only Landsat data; and 3) using both MODIS and Landsat data. This study retrieved FPAR from Landsat images using a scaling-based method and tested the performance of PK-STEM in a regional application. For the entire year of 2012, we compared the performance of PK-STEM in different modes and with that of two typical spatiotemporal fusion methods, the enhanced spatial and temporal adaptive reflectance model (ESTARFM) and unmixing-based linear mixing growth model (LMGM). Then, a long time series FPAR data set at 30-m resolution and eight-day intervals was generated for 13 years (2000–2012). Our results show that PK-STEM in mode III is the most robust and accurate (root mean squared error (RMSE) = 0.062; mean $R = 0.851$) among the three modes and more accurate than ESTARFM (mean RMSE = 0.065; mean $R = 0.776$) and LMGM (mean RMSE = 0.074; mean $R = 0.734$). For the 12 years (2000–2011), PK-STEM also achieves high accuracies with mean RMSE = 0.066 and $R = 0.938$. PK-STEM is very flexible with a continual update mechanism and is efficient for long time series applications. [ABSTRACT FROM AUTHOR]

Accurate estimation of the fine-resolution fraction of absorbed photosynthetically active radiation (FPAR) across broad spatial extents and long time periods requires efficient and applicable methods. The existing methods can hardly provide a balance between accuracy, simplicity, and transferability through space and time. Within the remote-sensing trend-surface conceptual framework, this article proposes a scaling-based method to efficiently retrieve FPAR from fine-resolution satellite data using coarse-resolution FPAR products as a reference. The method was particularly developed and applied to Moderate Resolution Imaging Spectroradiometer (MODIS) FPAR product and Landsat imagery. First, necessary prior knowledge related to FPAR retrieval and scaling theories was used to explicitly linearize the complex relationship between MODIS FPAR and Landsat surface reflectance. Second, the explicit linear model for FPAR estimation was trained through one-pair image learning for each date to estimate FPAR from Landsat imagery in real time. Both homogeneous and heterogeneous cases were considered. The method was validated at ten selected worldwide sites from the Validation of Land European Remote Sensing Instruments (VALERI) program and derived an overall root mean squared error (RMSE) of 0.133. A long time series of FPAR data set at the 30-m resolution was generated at the regional scale (approximately 2000 km2) for 13 years (2000–2012). The results were accurate (RMSE = 0.072) and MODIS-consistent, which were significantly better than those of the normalized difference vegetation index (NDVI) downscaling-based and regression tree methods. The scaling-based method provides accurate, MODIS-consistent and spatially consistent FPAR estimates in real time, is highly transferrable through space and time, and allows for future extension of FPAR estimates to the era of the Landsat series satellites. [ABSTRACT FROM AUTHOR]

The spatial scale variability of surface shortwave radiation (SSR) budget is dominated by clouds. Therefore, a prerequisite step for accurate estimation of the instantaneous SSR components under all-sky conditions, especially partly cloudy conditions, is to determine a proper spatial scale. In this study, a modified one-dimensional (1-D) radiative transfer (RT) model is developed to study the effect of cloud cover on SSR field, in which cloud fraction (CF) is introduced to improve the classic 1-D plane-parallel RT equations and a global sensitivity analysis (GSA) is performed to quantitatively understand the effect of CF on SSR under both optically thick and optically thin cloudy conditions. An artificial neural network approach is then employed to generate multiple SSR components based on extensive RT simulations using the modified 1-D RT model and the GSA results. The optimal spatial scale is quantitatively determined through eight cloudy scenarios over the Tibetan Plateau, with different amounts and spatial distribution patterns of clouds. The GSA results show that the top three vital parameters for the modified 1-D RT model are solar zenith angle, CF, and land surface albedo. The optimal spatial scale for applying the modified 1-D RT model is about 20 km, which is surprisingly consistent with some theoretically and technically complicated simulation studies. The finding of the current study adds new evidence to the growing body of knowledge about the spatial scale consideration for estimating all-sky instantaneous SSR with 1-D RT theory. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is an important parameter that directly affects the water and heat balance between the Earth surface and atmosphere. Mapping the LST distribution at continuous temporal and wide spatial scales is very helpful for researching many physical and biochemical processes. Remotely sensed instruments are the key players in these studies. Passive microwave remotely sensed data have the advantage of retrieving the land parameters under nearly all weather conditions because of the power to penetrate clouds. In this study, a practical two-stage algorithm, which uses single-frequency and double-polarization passive microwave brightness temperature observations, is presented to retrieve the LST over China. The vertically polarized land surface emissivity (LSE) at 18.7 GHz is first estimated by a parameterization relationship with the polarization ratio (PR), which is defined as the ratio of the horizontal to the vertical brightness temperatures at the same frequency. Subsequently, the LST is retrieved using the estimated LSE by ignoring the atmospheric effect. The evaluation of the simulated data shows an RMSE of 1.45 K, which is very encouraging. The cross validations by the satellite thermal infrared products are from daily to monthly time scales. The daily accuracy is 3.04 K and the bimonthly accuracy is 4.43 K. A high positive bias in arid and semiarid regions and a negative bias in frequently cloudy regions are noticeable in both comparisons. These biases do not reveal the uncertainties of this LST retrieval algorithm; on the contrary, the retrieved the Advanced Microwave Scanning Radiometer − EOS (AMSR-E) LST appears to be more reasonable compared to the thermal infrared LST under certain circumstances. [ABSTRACT FROM AUTHOR]

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-infrared-band 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. [ABSTRACT FROM AUTHOR]

Shortwave radiation (0.3–3.0 μm) is a dominant component of land surface all-wave radiation budget. Reliable estimation of shortwave radiation throughout the globe is particularly important for an in-depth understanding of global changes. Unfortunately, a large number of existing space-based algorithms ignore the effects of topography by simply assuming that the surface is ideally flat. As pointed out by many studies, such neglect toward topographic effects leads to significant errors in derived radiation. This study proposes a shortwave topographic radiation model (SWTRM) by quantifying solar direct radiation shielding, occlusion of sky radiation, reflected radiation from nearby terrain, and invisibility of certain targets. To drive the SWTRM, an artificial neural network (ANN) approach was employed to generate multiple components of the shortwave radiation budget. The new model was run over the Tibetan Plateau region and used MODIS products coupled with digital elevation data (DEM). Topographically corrected shortwave fluxes were derived by coupling SWTRM and ANN outputs. The results show that: (1) the proposed SWTRM works well over mountainous regions; (2) the ANN-based shortwave model provides reasonable retrieval accuracy (RMSE < 60 W/m 2 , bias < 13 W/m 2 for all shortwave radiation components). More importantly, the ANN model can simultaneously provide all radiation components that are indispensable for driving the SWTRM; (3) over mountainous areas, the induced error can exceed 600 W/m 2 for shortwave net flux. Hence, topographic effects cannot be neglected; and (4) topography and solar illumination angle are key modulators in deriving shortwave radiation from space, which control the magnitude and spatial distribution of shortwave radiation over mountainous regions. [ABSTRACT FROM AUTHOR]

Longwave radiation (5–100 μm) is a critical component of the Earth's radiation budget. Most of the existing satellite-based retrieval algorithms are valid only for flat surfaces without accounting for topographic effects. This causes significant errors. Meanwhile, the fixed spatial resolution of remote sensing data makes it difficult to link the satellite-derived longwave radiation to different land models running on various scales. These deficiencies result in an urgent need for topographic modeling and spatial scaling studies of longwave radiation. In this paper, a longwave topographic radiation model (LWTRM) is proposed that quantifies all possible radiation-affecting factors over rugged terrain. For driving the LWTRM, a hybrid method for simultaneously deriving multiple components of longwave radiation from MODIS data is suggested based on artificial neuron networks (ANN) and the radiative transfer simulation. Topographically corrected longwave radiation is then derived by coupling the ANN outputs and LWTRM. Based on this, a general upscaling strategy for longwave radiation is presented. The results demonstrate that: (1) both the proposed LWTRM and the upscaling strategy are rather effective and work well over rugged areas; (2) the ANN-based retrieval method can produce longwave radiation with better accuracy(RMSE < 23 W/m 2 , bias < 9 W/m 2 ). More importantly, it can simultaneously derive multiple components of longwave radiation in a consistent manner; (3) over mountainous areas, the radiation cannot be accurately characterized in terms of either spatial distribution or specific values if topographic effects are neglected, for instance, the induced error can reach up to 100 W/m 2 for the longwave net flux; and (4) the topographic effects cannot be ignored below spatial scale of approximately 5 km in the selected study area. [ABSTRACT FROM AUTHOR]

Surface upward longwave radiation (SULR) is one critical component of the surface energy balance and is closely related to the surface temperature fields. The SULR with the moderate spatial resolution and appropriate precision in the Tibetan Plateau, a large and complex cryosphere, can enhance our understanding of the land surface processes, atmospheric circulations and even global climate change. The purpose of this paper is to estimate SULR from top-of-atmosphere (TOA) radiances of Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS). A hybrid method is proposed that combines extensive radiative transfer simulation and the Artificial Neuron Network (ANN) statistical model. For the sake of ensuring the availability of the simulation database, the eligible profiles from MODIS atmospheric profiles product were screened according to five criteria. And then the analyses on the important parameters of the simulation dataset were performed to check the integrality of this dataset. Moreover, the variables' importance was analyzed, and TOA radiances of MODIS channels 29, 31, 32 and 33 (or VIIRS channels M14, M15 and M16) and sensor view zenith angle (VZA) were selected to retrieve SULR. The ANN-based retrieval model was created finally based on this simulation dataset. The evaluation showed good agreements for both MODIS and VIIRS training and testing datasets with R 2 greater than 0.98. The MODIS SULR was validated using two-year ground-measured data at seven sites in the Tibetan Plateau with R 2 of 0.886, root-mean-square error (RMSE) of 26.985 W m − 2 and mean bias error (MBE) of 10.812 W m − 2 as a whole. And the relevance was obvious from the overall trend and distribution though the discrepancy was relatively large when directly comparing SULR from VIIRS and MODIS data. The validation results showed that the ANN model is a good nonlinear model to retrieve SULR with the TOA radiances and VZA. [ABSTRACT FROM AUTHOR]

It is worth noting that the influences of dust aerosol type and different aerosol loadings were not considered in the development of the generalized split-window (GSW) algorithm. However, numerical simulations showed that the influence of dust aerosol could lead to a maximum land surface temperature (LST) retrieval error of 5.12 K when the aerosol optical depth (AOD) in the atmosphere is 1.0 and viewing zenith angle (VZA) is 60°. This paper focuses on reducing the influence of dust aerosol on the LST retrieval error of the GSW algorithm. A linear function was developed to reduce such influence with respect to the AOD. The slope could be expressed as a function of the difference between the MODIS channel brightness temperatures \bmT\bf{31} and \bmT\bf{32} measured at the top of the atmosphere (TOA) and difference and mean of the two-channel emissivities, and the offset could be used as a constant value for each VZA. The results showed that the retrieval accuracy could be improved by approximately 4 K for \bfAOD = \bf1.0 and \bfVZA = \bf60^\circ. Sensitivity analysis in terms of the uncertainties of the input parameters showed that the maximum LST retrieval error is 1.15 K for \bfVZA = \bf0^\circ. Some of the in situ measurements observed at the Yingke site in northwest China and Arvaikheer site in south Mongolia were used to test the proposed method, respectively. The results showed that the proposed method could improve the LST retrieval accuracy by at least 1 K for the GSW algorithm in atmospheres with heavy dust aerosol loading. [ABSTRACT FROM PUBLISHER]

This paper describes two new methods that were used to generate 26 years (1985–2010) of broadband emissivity (BBE) products with spatiotemporal continuity at the global scale from satellite data recorded by the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Very High Resolution Radiometer (AVHRR). On the basis of emissivity libraries, the study began with establishing relationships for converting channel emissivities of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and MODIS to BBEs for the 8–13.5-\mu\m spectral window and then developed two new algorithms from simultaneous ASTER emissivity products to estimate BBEs over vegetated surfaces using the MODIS and AVHRR data. The MODIS-data-based algorithm (MDBA) uses linear equations with MODIS normalized difference vegetation index (NDVI) and seven channels' albedo; the AVHRR-data-based algorithm uses nonlinear equations with AVHRR red and near-infrared reflectances. The proposed algorithms were first validated with ASTER emissivity products. Results indicated that the root-mean-square errors of both the proposed algorithms were less than 0.015 and their biases were less than 0.003. Comparison with MODIS emissivity products from the day/night algorithm showed that the estimated BBEs using the MDBA were generally smaller than the MODIS products. Cross-comparisons were also made between the proposed algorithms and the NDVI threshold method. Finally, strategies for mapping global BBE products from the MODIS and AVHRR data are presented, and some examples are discussed. The global BBE products are planned to be released throughout the network in the near future. [ABSTRACT FROM PUBLISHER]

Abstract: Shortwave (0.3–3μm) and longwave (3–50μm) surface radiative flux components have been widely used in numerical prediction, meteorology, hydrology, biomass estimation, surface energy circulation and climate change studies, etc. However, during past decades, these components were usually estimated independently using different methods, possibly causing inconsistent estimation biases due to different atmospheric parameters and algorithms, especially for net surface fluxes. Two methods have been proposed in this paper to simultaneously derive surface shortwave (or longwave) radiative flux components based on MODIS products using an artificial neural network (ANN). The validation results show that the maximum root-mean-square error for downward and net shortwave radiative fluxes is less than 45W/m2, about 60W/m2 for direct solar radiation and 25W/m2 for all longwave fluxes, which are comparable or even better than existing algorithms, thus demonstrating their feasibility and efficacy. The ANN-based models are then applied over the Tibetan Plateau region and the characteristics of the surface radiative flux components over such areas are analyzed. [Copyright &y& Elsevier]

Despite its wide applications, the spatiotemporal fusion of coarse- and fine-resolution satellite images is limited primarily to the availability of clear-sky fine-resolution images, which are commonly scarce due to unfavorable weather, and such a limitation might cause errors in spatiotemporal fusion. Thus, the effective use of limited fine-resolution images, while critical, remains challenging. To address this issue, in this paper we propose a new phenological similarity strategy (PSS) to select the optimal combination of image pairs for a prediction date. The PSS considers the temporal proximity and phenological similarity between the base and prediction images and computes a weight for identifying the optimal combination of image pairs. Using the PSS, we further evaluate the influence of input data on the fusion accuracy by varying the number and temporal distribution of input images. The results show that the PSS (mean R = 0.827 and 0.760) outperforms the nearest date (mean R = 0.786 and 0.742) and highest correlation (mean R = 0.821 and 0.727) strategies in both the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the linear mixing growth model (LMGM), respectively, for fusing Landsat 8 OLI and MODIS NDVI datasets. Furthermore, base images adequately covering different growth stages yield better predictability than simply increasing the number of base images. [ABSTRACT FROM AUTHOR]

The science teams of Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imager Radiometer Suite (VIIRS) have been supporting various global climate, biogeochemistry, and energy flux research efforts by producing valuable long-term Leaf Area Index (LAI) products. Although intensive LAI validation studies have been carried out globally, retrieval accuracy has been assessed only over short time spans and product quality is reported as a constant based on the assumption that it is stationary. However, a preliminary evaluation found a time-dependent signal degradation of the MODIS sensors which may cause the uncertainty of the Bidirectional Reflectance Factor (BRF) product to exceed the preset uncertainty buffer in the retrieval algorithm and result in MODIS LAI quality variation and time-series inconsistency with VIIRS LAI. Therefore, to ensure the reliability of trend-detection studies and to answer the critical question of whether the algorithm configuration is still suitable for the uncertainty level of present BRF product, there is a need for a more comprehensive investigation regarding the performance stability of LAI retrieval algorithm due to model uncertainty and input uncertainties. This paper reports analyses of inter-annual stability and trends of LAI uncertainties (inferred from product quality flag rather than ground-based validation) as well as LAI magnitudes using multi-year (MODIS: 2001–2019, VIIRS: 2013–2019) and multi-site (445 sites) datasets. We found that the quality metrics of the two products are consistent across different biome types and LAI values (R2 ranged from 0.88 to 0.99). In the 19-year MODIS period, we found a significant increasing trend in LAI magnitudes (4.5%/decade) which agrees with previous reports, while there was no significant trend in product quality. Moreover, quality metrics showed much smaller inter-annual variations than LAI values, which confirms the performance stability of retrieval algorithms and suggests that the configuration of MODIS algorithm is still compatible with the uncertainty of algorithm inputs. Additionally, this study provides support to the "Greening Asia" studies by eliminating the possibility that the "greening" could be attributed to artificial trends caused by sensor degradation. We also found very small inter-annual variations in atmospheric conditions (e.g., cloud and aerosols), which further assures that the performance stability of retrieval algorithm. Overall, our results demonstrate the robustness of the retrieval algorithm in the presence of changes in input uncertainties. The temporal stability of the algorithm performance indirectly strengthen the confidence in the continuous use of MODIS LAI products either independently or in combination with its successor - VIIRS LAI - to study global and regional vegetation dynamics • MODIS & VIIRS Leaf Area Index time-series are evaluated from a new perspective. • LAI product quality is a dynamic parameter rather than a constant value. • Terra-MODIS sensor degradation does not reduce LAI product accuracy. • MOD15A2H and VNP15A2H show good consistency and continuity. • Help to better understand trend-related studies (e.g. Greening Earth) [ABSTRACT FROM AUTHOR]