Jindi Wang

Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance

Leaf area index (LAI) products at regional and global scales are being routinely generated from individual instrument data acquired at a specific time. As a result of cloud contamination and other factors, these LAI products are spatially and temporally discontinuous and are also inaccurate for some vegetation types in many areas. A better strategy is to use multi-temporal data. In this paper, a method was developed to estimate LAI from time-series remote sensing data using general regression neural networks (GRNNs). A database was generated from Moderate-Resolution Imaging Spectroradiometer (MODIS) and CYCLOPES LAI products as well as MODIS reflectance products of the BELMANIP sites during the period from 2001-2003. The effective CYCLOPES LAI was first converted to true LAI, which was then combined with the MODIS LAI according to their uncertainties determined from the ground-measured true LAI. The MODIS reflectance was reprocessed to remove remaining effects. GRNNs were then trained over the fused LAI and reprocessed MODIS reflectance for each biome type to retrieve LAI from time-series remote sensing data. The reprocessed MODIS reflectance data from an entire year were inputted into the GRNNs to estimate the 1-year LAI profiles. Extensive validations for all biome types were carried out, and it was demonstrated that the method is able to estimate temporally continuous LAI profiles with much improved accuracy compared with that of the current MODIS and CYCLOPES LAI products. This new method is being used to produce the Global Land Surface Satellite LAI products in China.

A priori knowledge accumulation and its application to linear BRDF model inversion

A priori knowledge can significantly improve the retrieval of surface bidirectional reflectance and spectral albedo from satellite observations. Here a priori knowledge takes the form of field measurements of bidirectional reflectance factors for various surface cover types in red and near-infrared bands. Bidirectional reflectance and albedo retrieval refers to inversion of a kernel-driven bidirectional reflectance distribution function (BRDF) model using surface reflectance observations derived from orbiting spacecraft. A priori knowledge is applied when noise and poor angular sampling reduce the accuracy of model inversion given a limited number of observations. In such cases, a priori knowledge can indicate when retrieved kernel weights or albedos are outside expected bounds, leading to a closer examination of data. If data are noisy, a priori knowledge can be used to smooth the data. If the data exhibit poor angular sampling, a priori knowledge can be used according to Bayesian inference theory to yield a posteriori estimates of unknown kernel weights. In the latter application, Bayes theory is applied in data space rather than in parameter space. Extensive study and simulation using 73 sets of field observations and 395 spaceborne observation sets from the POLDER instrument validates the importance of a priori information in improving inversions and BRDF retrievals.

A Temporally Integrated Inversion Method for Estimating Leaf Area Index From MODIS Data

Multiple leaf area index (LAI) products have been generated from remote-sensing data. Among them, the Moderate-Resolution Imaging Spectroradiometer (MODIS) LAI product (MOD15A2) is now routinely derived from data acquired by MODIS sensors onboard Terra and Aqua satellite platforms. However, the MODIS LAI product is not spatially and temporally continuous and is inaccurate in many areas for some vegetation types. In this paper, a new algorithm is developed to estimate LAI from time-series MODIS reflectance data (MOD09A1). A radiative-transfer model is coupled with a double-logistic LAI temporal-profile model, and the shuffled complex evolution optimization method, developed at the University of Arizona, is used to estimate the parameters of the coupled model from the temporal signature in a given time window. Preliminary analysis using MODIS surface-reflectance data at flux sites was performed to validate this method. The results show that the new algorithm is able to construct a temporally continuous LAI product efficiently, and the accuracy has been significantly improved over the MODIS LAI product as compared to field-measured LAI data.

Crop Leaf Area Index Observations With a Wireless Sensor Network and Its Potential for Validating Remote Sensing Products

The collection of ground measurements for validating remotely sensed crop leaf area index (LAI) is labor and time intensive. This paper presents an automatic measuring system that was designed based on a wireless sensor network (WSN). The corn LAI was continuously observed from June 25 to August 24, 2012. Approximately, 42 in situ WSN measurement nodes were used in a 4 ×4 km2 area in the Heihe watershed of northwest China. The data were analyzed in three ways: 1) a comparison with LAI-2000, 2) a daily and 5-day aggregated time series analysis, and 3) a comparison with a Moderate Resolution Imaging Spectroradiometer (MODIS) LAI using both a ground LAINet LAI and a scaled-up LAI through inversion of Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER) data. The preliminary results indicated that the measured LAI values from the LAINet were correlated with the values derived from LAI-2000 (R2 from 0.27 to 0.96 with an average of 0.42). When compared with the daily crop LAI growth trajectory, the performance of the measurement system was improved by using the data that were aggregated over a 5-day window. When compared with MODIS LAI, we found that the spatial aggregation values of the ground LAINet observations and the scaled-up ASTER LAI were identical or similar to the MODIS LAI values over time. With its low-cost and low-energy consumption, the proposed WSN observation system is a promising method for collecting ground crop LAI in flexible time and space for validating the remote sensing land products.

Daily MODIS 500 m reflectance anisotropy direct broadcast DB products for monitoring vegetation phenology dynamics

Land surface vegetation phenology is an efficient bio-indicator for monitoring ecosystem variation in response to changes in climatic factors. The primary objective of the current article is to examine the utility of the daily MODIS 500 m reflectance anisotropy direct broadcast (DB) product for monitoring the evolution of vegetation phenological trends over selected crop, orchard, and forest regions. Although numerous model-fitted satellite data have been widely used to assess the spatio-temporal distribution of land surface phenological patterns to understand phenological process and phenomena, current efforts to investigate the details of phenological trends, especially for natural phenological variations that occur on short time scales, are less well served by remote sensing challenges and lack of anisotropy correction in satellite data sources. The daily MODIS 500 m reflectance anisotropy product is employed to retrieve daily vegetation indices (VI) of a 1 year period for an almond orchard in California and for a winter wheat field in northeast China, as well as a 2 year period for a deciduous forest region in New Hampshire, USA. Compared with the ground records from these regions, the VI trajectories derived from the cloud-free and atmospherically corrected MODIS Nadir BRDF (bidirectional reflectance distribution function) adjusted reflectance (NBAR) capture not only the detailed footprint and principal attributes of the phenological events (such as flowering and blooming) but also the substantial inter-annual variability. This study demonstrates the utility of the daily 500 m MODIS reflectance anisotropy DB product to provide daily VI for monitoring and detecting changes of the natural vegetation phenology as exemplified by study regions comprising winter wheat, almond trees, and deciduous forest.

Development of the Adjoint Model of a Canopy Radiative Transfer Model for Sensitivity Study and Inversion of Leaf Area Index

Many canopy reflectance models have been developed in the last decades and used for estimating land surface biogeophysical variables, such as leaf area index (LAI), from satellite observations through optimization procedures. In most studies, the derivative information of the canopy reflectance model has not been used effectively, which limits this approach for regional and global applications. The final solutions are often converged to the local minima. To address these issues, the adjoint model of a canopy radiative transfer model is developed in this study through the automatic differentiation technique. The developed adjoint model is used for sensitivity study, and a combination of the adjoint model with the trust region global optimization method is performed to retrieve LAI from the Enhanced Thematic Mapper Plus (ETM+). This study demonstrates that this method can be reliably used for inverting LAI efficiently and is suitable for global applications.

Long-Time-Series Global Land Surface Satellite Leaf Area Index Product Derived From MODIS and AVHRR Surface Reflectance

Leaf area index (LAI) is an important vegetation biophysical variable and has been widely used for crop growth monitoring and yield estimation, land-surface process simulation, and global change studies. Several LAI products currently exist, but most have limited temporal coverage. A long-term high-quality global LAI product is required for greatly expanded application of LAI data. In this paper, a method previously proposed was improved to generate a long time series of Global LAnd Surface Satellite (GLASS) LAI product from Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MOD!S) reflectance data. The GLASS LAI product has a temporal resolution of eight days and spans from 1981 to 2014. During 1981-1999, the LAI product was generated from AVHRR reflectance data and was provided in a geographic latitude/longitude projection at a spatial resolution of 0.05°. During 2000-2014, the LAI product was derived from MODIS surface-reflectance data and was provided in a sinusoidal projection at a spatial resolution of 1 km. The GLASS LAI values derived from MODIS and AVHRR reflectance data form a consistent data set at a spatial resolution of 0.05°. Comparison of the GLASS LAI product with the MODIS LAI product (MOD15) and the first version of the Geoland2 (GEOV1) LAI product indicates that the global consistency of these LAI products is generally good. However, relatively large discrepancies among these LAI products were observed in tropical forest regions, where the GEOV1 LAI values were clearly lower than the GLASS and MOD15 LAI values, particularly in January. A quantitative comparison of temporal profiles shows that the temporal smoothness of the GLASS LAI product is superior to that of the GEOV1 and MODIS LAI products. Direct validation with the mean values of high-resolution LAI maps demonstrates that the GLASS LAI values were closer to the mean values of the high-resolution LAI maps (RMSE = 0.7848 and R2 = 0.8095) than the GEOV1 LAI values (RMSE = 0.9084 and R2 = 0.7939) and the MOD15 LAI values (RMSE = 1.1173 and R2 = 0.6705).

Comparison of Radiative Transfer Models for Simulating Snow Surface Thermal Infrared Emissivity

In this study, three analytical radiative transfer (RT) models and a numerical RT model are used to simulate the thermal-infrared (8-13 μm) emissivity spectra of snow surfaces. The single-scattering albedo and asymmetry factor calculated by Mie theory, in conjunction with that modified by two existing packing correction methods, are used as inputs to these RT models. The simulated snow emissivity spectra are compared with in situ measurements. The best models for simulating snow emissivity spectra are identified at the conclusion of this paper.

Validation of MISR land surface broadband albedo

Land surface broadband albedo is a critical variable for many scientific applications. Due to the scarcity of spectral albedo measurements of the Earths surface environments, it is useful to construct broadband albedo from spectral albedo data obtained by multi‐angle satellite observations. The Multi‐angle Imaging SpectroRadiometer (MISR) onboard NASAs Earth Observing System (EOS) Terra satellite provides land surface albedo products from multi‐angular observations; however, the products have not been comprehensively validated. We convert MISR spectral albedos to total shortwave albedos and validate them using ground measurements at different validation sites. For most surface types, a published narrowband to broadband conversion formula was used, but a new conversion formula for snow and ice covered sites is developed in this study where the spectral range of the instrument is different. Several comparisons are made: (1) between MISR directional‐hemispherical reflectance (DHR) or albedo and MODIS (Moderate Resolution Imaging Spectroradiometer) DHR; and (2) between MISR spectral DHR and bi‐hemispherical reflectance (BHR). The results show that: (1) both the value and the temporal trends of the MISR shortwave albedo and the ground measured shortwave albedo are in good agreement, with the exception of the snow and ice sites; (2) the MISR DHR conforms well to MODIS DHR; and (3) the values of MISR DHR and BHR are nearly identical.

Scale effects and scaling-up by geometric-optical model

This is a follow-up paper to our “Scale effect of Planck’s law over nonisothermal blackbody surface”. More examples are used to describe the scale effect in detail, and the scaling-up of Planck law over blackbody surface is further extended to three-dimension nonisothermal surface. This scaling-up results in a conceptual model for the directionality and spectral signature of thermal radiation at the scale of remote sensing pixels. This new model is also an improvement of Li-Strahler-Friedl conceptual model in a sense that the new model needs only statistic parameters at the pixel scale, without request of sub-pixel scale parameters as the LSF model does.