Huaan Jin

An Enhanced Spatial and Temporal Data Fusion Model for Fusing Landsat and MODIS Surface Reflectance to Generate High Temporal Landsat-Like Data

Remotely sensed data, with high spatial and temporal resolutions, can hardly be provided by only one sensor due to the tradeoff in sensor designs that balance spatial resolutions and temporal coverage. However, they are urgently needed for improving the ability of monitoring rapid landscape changes at fine scales (e.g., 30 m). One approach to acquire them is by fusing observations from sensors with different characteristics (e.g., Enhanced Thematic Mapper Plus (ETM+) and Moderate Resolution Imaging Spectroradiometer (MODIS)). The existing data fusion algorithms, such as the Spatial and Temporal Data Fusion Model (STDFM), have achieved some significant progress in this field. This paper puts forward an Enhanced Spatial and Temporal Data Fusion Model (ESTDFM) based on the STDFM algorithm, by introducing a patch-based ISODATA classification method, the sliding window technology, and the temporal-weight concept. Time-series ETM+ and MODIS surface reflectance are used as test data for comparing the two algorithms. Results show that the prediction ability of the ESTDFM algorithm has been significantly improved, and is even more satisfactory in the near-infrared band (the contrasting average absolute difference [AAD]: 0.0167 vs. 0.0265). The enhanced algorithm will support subsequent research on monitoring land surface dynamic changes at finer scales.

Auto-registration and orthorecification algorithm for the time series HJ-1A/B CCD images

How to deal with geometric distortion is an open problem when using the massive amount of satellite images at a national or global scale, especially for multi-temporal image analysis. In this paper, an algorithm is proposed to automatically rectify the geometric distortion of time-series CCD multispectral data of small constellation for environmental and disaster mitigation (HJ-1A/B) which was launched by China in 2008. In this algorithm, the area-based matching method was used to automatically search tie points firstly, and then the polynomial function was introduced to correct the systematic errors caused by the satellite motion along the roll, pitch and yaw direction. The improved orthorectification method was finally used to correct pixel displacement caused by off-nadir viewing of topography, which are random errors in the images and cannot be corrected by the polynomial equation. Nine scenes of level 2 HJ CCD images from one path/row were taken as the warp images to test the algorithm. The test result showed that the overall accuracy of the proposed algorithm was within 2 pixels (the average residuals were 37.8 m, and standard deviations were 19.8 m). The accuracies of 45.96% validation points (VPs) were within 1 pixel and 90.33% VPs were within 2 pixels. The discussion showed that three main factors including the distortion patterns of HJ CCD images, percent of cloud cover and the varying altitude of the satellite orbit may affect the search of tie points and the accuracy of results. Although the influence of varying altitude of the satellite orbits is less than the other factors, it is noted that detailed satellite altitude information should be given in the future to get a more precise result. The proposed algorithm should be an efficient tool for the geo-correction of HJ CCD multi-spectral images.

Land Cover Mapping in Southwestern China Using the HC-MMK Approach

Land cover mapping in mountainous areas is a notoriously challenging task due to the rugged terrain and high spatial heterogeneity of land surfaces as well as the frequent cloud contamination of satellite imagery. Taking Southwestern China (a typical mountainous region) as an example, this paper established a new HC-MMK approach (Hierarchical Classification based on Multi-source and Multi-temporal data and geo-Knowledge), which was especially designed for land cover mapping in mountainous areas. This approach was taken in order to generate a 30 m-resolution land cover product in Southwestern China in 2010 (hereinafter referred to as CLC-SW2010). The multi-temporal native HJ (HuanJing, small satellite constellation for disaster and environmental monitoring) CCD (Charge-Coupled Device) images, Landsat TM (Thematic Mapper) images and topographical data (including elevation, aspect, slope, etc.) were taken as the main input data sources. Hierarchical classification tree construction and a five-step knowledge-based interactive quality control were the major components of this proposed approach. The CLC-SW2010 product contained six primary categories and 38 secondary categories, which covered about 2.33 million km(2) (accounting for about a quarter of the land area of China). The accuracies of primary and secondary categories for CLC-SW2010 reached 95.09% and 87.14%, respectively, which were assessed independently by a third-party group. This product has so far been used to estimate the terrestrial carbon stocks and assess the quality of the ecological environments. The proposed HC-MMK approach could be used not only in mountainous areas, but also for plains, hills and other regions. Meanwhile, this study could also be used as a reference for other land cover mapping projects over large areas or even the entire globe.

Performance Evaluation of the Triangle-Based Empirical Soil Moisture Relationship Models Based on Landsat-5 TM Data and In Situ Measurements

Surface soil moisture (SSM) is an important parameter at the land–atmosphere interface. In past decades, passive microwave remote sensing offers a good opportunity for obtaining SSM on a global scale, and many downscaling methods have been proposed using the triangle-based empirical soil moisture relationship models to overcome the limitation of coarse spatial resolution of its SSM products for regional applications. This paper aimed to examine and compare the effectiveness of five typical triangle-based empirical soil moisture relationship models for estimating SSM with Landsat-5 data and in situ measurements from the Maqu network on the northeastern part of the Tibetan Plateau for nine cloud-free days. The results showed that the model that treats the SSM as a second-order polynomial with land surface temperature, vegetation indices (VIs), and surface albedo as inputs exhibited the best performance compared with the results of other models. The VI comparison indicated that the use of the normalized difference VI or the fractional vegetation cover in this model outperformed other VIs, with the root-mean-square deviation of approximately 0.055 m3/m3 and the coefficient of determination (

Multi-temporal cloud and snow detection algorithm for the HJ-1A/B CCD imagery of China

\text{R}^{2}

A Synergetic Algorithm for Mid-Morning Land Surface Soil and Vegetation Temperatures Estimation Using MSG-SEVIRI Products and TERRA-MODIS Products

) above 0.78 at the nine-day average level. In addition, a significant spatial scale effect of the model was also found through analyzing the model fitting results at different window sizes. The study provides important insight into the best empirical relationship models for capturing soil moisture dynamics. These models can support the passive microwave soil moisture data spatial downscaling and validation applications in future studies.

A Cost-Constrained Sampling Strategy in Support of LAI Product Validation in Mountainous Areas

How to accurately detect cloud and snow in the remote sensing imagery is an open problem for the remote sensing application. For only visible and near infrared band in HJ-1A/B CCD images, the cloud detection algorithm using the shortwave infrared and thermal infrared band is restricted by the band-lacking problem. Based on the multi-temporal information of the HJ-1A/B CCD images, a new algorithm is proposed in this paper. Using available images in one month, a cloud-free reference image was firstly composed. Then, the cloud and snow pixel are separated through the difference of the blue band between the reference and each date. Subsequently, the regional covariance matrix is computed further to eliminate the non-cloud pixels. The test result shows that, the overall accuracy is about 85.96% to 93%. It indicates that the proposed method can integrate the temporal and texture information to improve detection accuracy for the cloud and snow.

Validation of global land surface satellite (GLASS) downward shortwave radiation product in the rugged surface

Land surface is normally considered as a mixture of soil and vegetation. Many applications, such as drought monitoring and crop-yield estimation, benefit from accurate retrieval of both soil and vegetation temperatures through satellite observation. A preliminary study has been conducted in this study on the estimation of land surface soil and vegetation component temperature using the geostationary satellite data acquired by Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) and TERRA-MODIS data. A synergetic algorithm is proposed to derive soil and vegetation temperatures by using the temporal and spatial information in SEVIRI and MODIS products. The approach is applied to both simulation data and satellite data. For simulation data, the component temperatures are well estimated with root mean squared error (RMSE) close to 0 K. For satellite data application, reasonable spatial distributions of the soil and vegetation temperatures are derived for eight cloud-free days in the Iberian Peninsula from June to August 2009. An evaluation is performed for the estimated vegetation temperature against the near surface air temperature. The correlation analysis between two datasets is found that the R-squareds are from 0.074 to 0.423 and RMSEs are within 4 K. Considering the impact of fraction of vegetation cover (FVC) on the validation, the pixels with FVC less than 30% are excluded in the total data comparison, and an obvious improvement is achieved with R-squared from 0.231 to 0.417 and RMSE from 2.9 K to 2.58 K. The validation indicates that the proposed algorithm is able to provide reasonable estimations of soil and vegetation temperatures. It is a potential way to map soil and vegetation temperature for large areas.

Validation of MODIS global LAI products in forested terrain

Increasing attention is being paid on leaf area index (LAI) retrieval in mountainous areas. Mountainous areas present extreme topographic variability, and are characterized by more spatial heterogeneity and inaccessibility compared with flat terrain. It is difficult to collect representative ground-truth measurements, and the validation of LAI in mountainous areas is still problematic. A cost-constrained sampling strategy (CSS) in support of LAI validation was presented in this study. To account for the influence of rugged terrain on implementation cost, a cost-objective function was incorporated to traditional conditioned Latin hypercube (CLH) sampling strategy. A case study in Hailuogou, Sichuan province, China was used to assess the efficiency of CSS. Normalized difference vegetation index (NDVI), land cover type, and slope were selected as auxiliary variables to present the variability of LAI in the study area. Results show that CSS can satisfactorily capture the variability across the site extent, while minimizing field efforts. One appealing feature of CSS is that the compromise between representativeness and implementation cost can be regulated according to actual surface heterogeneity and budget constraints, and this makes CSS flexible. Although the proposed method was only validated for the auxiliary variables rather than the LAI measurements, it serves as a starting point for establishing the locations of field plots and facilitates the preparation of field campaigns in mountainous areas.

Spatio-temporal variation and driving forces in alpine grassland phenology in the Zoigê plateau from 2001–2013

The downward shortwave radiation (DSR) is an essential parameter of land surface radiation budget and many land surface models that characterize hydrological, ecological and biogeochemical processes. The new Global LAnd Surface Satellite (GLASS) DSR datasets have been generated recently using multiple satellite data in China. This study investigates the performances of direct comparison approach, which is mostly used for validation of surface insolation retrieved from satellite data over the plain area, and indirect comparison approach, which needs a fine resolution map of DSR as reference, for validation of GLASS DSR product in time-steps of 1 and 3 hours over three Chinese Ecosystem Research Network sites located in the rugged surface. Results suggest that it probably has a large uncertainty to assess GLASS DSR product using the direct comparison method between GLASS surface insolation and field measurements over complex terrain, especially at Mt. Gongga 3,000 m station with root mean square error of 279.04 and 229.06 W/m2 in time-steps of 1 and 3 hours, respectively. Further improvement for validation of GLASS DSR product in the rugged surface is suggested by generation of a fine resolution map of surface insolation and comparison of the aggregated fine resolution map with GLASS product in the rugged surface. The validation experience demonstrates that the GLASS DSR algorithm is satisfactory with determination coefficient of 0.83 and root mean square error of 81.91W/m2 over three Chinese Ecosystem Research Network sites, although GLASS product overestimates DSR compared to the aggregated fine resolution map of surface insolation.