Chaoyang Wu

An underestimated role of precipitation frequency in regulating summer soil moisture

Soil moisture induced droughts are expected to become more frequent under future global climate change. Precipitation has been previously assumed to be mainly responsible for variability in summer soil moisture. However, little is known about the impacts of precipitation frequency on summer soil moisture, either interannually or spatially. To better understand the temporal and spatial drivers of summer drought, 415 site yr measurements observed at 75 flux sites world wide were used to analyze the temporal and spatial relationships between summer soil water content (SWC) and the precipitation frequencies at various temporal scales, i.e., from half-hourly, 3, 6, 12 and 24 h measurements. Summer precipitation was found to be an indicator of interannual SWC variability with r of 0.49 (p < 0.001) for the overall dataset. However, interannual variability in summer SWC was also significantly correlated with the five precipitation frequencies and the sub-daily precipitation frequencies seemed to explain the interannual SWC variability better than the total of precipitation. Spatially, all these precipitation frequencies were better indicators of summer SWC than precipitation totals, but these better performances were only observed in non-forest ecosystems. Our results demonstrate that precipitation frequency may play an important role in regulating both interannual and spatial variations of summer SWC, which has probably been overlooked or underestimated. However, the spatial interpretation should carefully consider other factors, such as the plant functional types and soil characteristics of diverse ecoregions.

Gross primary production estimation from MODIS data with vegetation index and photosynthetically active radiation in maize

[1] Gross primary production (GPP) is a significant important parameter for carbon cycle and climate change research. Remote sensing combined with other climate and meteorological data offers a convenient tool for large‐scale GPP estimation. GPP was estimated as a product of vegetation indices (VIs) and photosynthetically active radiation (PAR). Four kinds of vegetation indices [the normalized difference vegetation index (NDVI), the weighted difference vegetation index, the soil‐adjusted vegetation index, and the enhanced vegetation index (EVI)] derived from the Moderate Resolution Imaging Spectroradiometer daily surface reflectance product were selected to test our method. The in situ GPP was calculated using the eddy covariance technique and the PAR data were acquired from meteorological measurements. Because VIs were found to be a reliable proxy of both light use efficiency (LUE) and the fraction of absorbed PAR (fAPAR; R 2 of 0.63–0.87 for LUE and 0.69–0.76 for fAPAR), the product VI × VI × PAR is used as a measure of GPP according to Monteith logic. Moderate determination coefficients R 2 from 0.65 for NDVI to 0.71 for EVI were obtained when GPP was estimated from a single index in maize. When testing our method, calculating GPP as a product of VI × VI × PAR, the determination coefficients R 2 largely improved, fluctuating from 0.81 to 0.91. EVI × EVI × PAR provided the best estimation of GPP with the highest R 2 of 0.91 because EVI was found to be the best indicator of both LUE and fAPAR (R 2 of 0.87 and 0.76, respectively). These results will be helpful for the development of future GPP estimation models.

Use of MODIS and Landsat time series data to generate high-resolution temporal synthetic Landsat data using a spatial and temporal reflectance fusion model

Due to cloud coverage and obstruction, it is difficult to obtain useful images during the critical periods of monitoring vegetation using medium-resolution spatial satellites such as Landsat and Satellite Pour l’Observation de la Terre (SPOT), especially in pluvial regions. Although high temporal resolution sensors, such as the Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS), can provide high-frequency data, the coarse ground resolutions of these sensors make them unsuitable to quantify the vegetation growth processes at fine scales. This paper introduces a new data fusion model for blending observations of high temporal resolution sensors (e.g., MODIS) and moderate spatial resolution satellites (e.g., Landsat) to produce synthetic imagery with both high-spatial and temporal resolutions. By detecting temporal change information from MODIS daily surface reflectance images, our algorithm produced high-resolution temporal synthetic Landsat data based on a Landsat-7 Enhanced Thematic Mapper Plus ( ETM + ) image at the beginning time ( T 1 ). The algorithm was then tested over a 185 × 185     km 2 area located in East China. The results showed that the algorithm can produce high-resolution temporal synthetic Landsat data that were similar to the actual observations with a high correlation coefficient ( r ) of 0.98 between synthetic imageries and the actual observations.

An evaluation of EO-1 hyperspectral Hyperion data for chlorophyll content and leaf area index estimation

Estimation of chlorophyll content and the leaf area index (LAI) using remote sensing technology is of particular use in precision agriculture. Wavelengths at the red edge of the vegetation spectrum (705 and 750 nm) were selected to test vegetation indices (VIs) using spaceborne hyperspectral Hyperion data for the estimation of chlorophyll content and LAI in different canopy structures. Thirty sites were selected for the ground data collection. The results show that chlorophyll content and LAI can be successfully estimated by VIs derived from Hyperion data with a root mean square error (RMSE) of 7.20–10.49 μg cm−2 for chlorophyll content and 0.55–0.77 m2 m−2 for LAI. The special index derived from three bands provided the best estimation of the chlorophyll content (RMSE of 7.19 μg cm−2 for the Modified Chlorophyll Absorption Ratio Index/Optimized Soil-Adjusted Vegetation Index (MCARI/OSAVI705)) and LAI (RMSE of 0.55 m2 m−2 for a second form of the MCARI (MCARI2705)). These results demonstrate the possibilities for analysing the variation in chlorophyll content and LAI using hyperspectral Hyperion data with bands from the red edge of the vegetation spectrum.

Nondestructive estimation of canopy chlorophyll content using Hyperion and Landsat/TM images

Remote sensing offers a nondestructive tool for the quick and precise estimation of canopy chlorophyll content that serves as an important indicator of the plant ecosystem. In this study, the canopy chlorophyll content of 26 samples in 2007 and 40 samples in 2008 of maize were nondestructively estimated by a set of vegetation indices (VIs; Normalized Difference Vegetation Index, NDVI; Green Chlorophyll Index, CIgreen; modified soil adjust vegetation index, MSAVI; and Enhanced Vegetation Index, EVI) derived from the hyperspectral Hyperion and Thematic Mapper (TM) images. The PROSPECT model was used for sensitivity analysis among the indices and results indicated that CIgreen had a large linear correlation with chlorophyll content ranging from 100–1000 mg m−2. EVI showed a moderate ability in avoiding saturation and reached a saturation of chlorophyll content above 600 mg m−2. Both of the other two indices, MSAVI and NDVI, showed a clear saturation at chlorophyll content of 400 mg m−2, which demonstrated they may be inappropriate for chlorophyll interpretation at high values. A validation study was also conducted with satellite observations (Hyperion and TM) and in-situ measurements of chlorophyll content in maize. Results indicated that canopy chlorophyll content can be remotely evaluated by VIs with r 2 ranging from the lowest of 0.73 for NDVI to the highest of 0.86 for CIgreen. EVI had a greater precision (r 2=0.81) than MASVI (r 2=0.75) in canopy chlorophyll content estimation. The results agreed well with the sensitivity study and will be helpful in developing future models for canopy chlorophyll evaluation.

Accelerating Forest Growth Enhancement due to Climate and Atmospheric Changes in British Colombia, Canada over 1956-2001

Changes in climate and atmospheric CO2 and nitrogen (N) over the last several decades have induced significant effects on forest carbon (C) cycling. However, contributions of individual factors are largely unknown because of the lack of long observational data and the undifferentiating between intrinsic factors and external forces in current ecosystem models. Using over four decades (1956–2001) of forest inventory data at 3432 permanent samples in maritime and boreal regions of British Columbia (B.C.), Canada, growth enhancements were reconstructed and partitioned into contributions of climate, CO2 and N after removal of age effects. We found that climate change contributed a particularly large amount (over 70%) of the accumulated growth enhancement, while the remaining was attributed to CO2 and N, respectively. We suggest that climate warming is contributing a widespread growth enhancement in B.C.s forests, but ecosystem models should consider CO2 and N fertilization effects to fully explain inventory-based observations.

A robust multisource image automatic registration system based on the SIFT descriptor

Image registration is an essential step in many remote-sensing (RS) applications. This article presents a study of a multisource image automatic registration system (MIARS) based on the scale-invariant feature transform (SIFT), which has been demonstrated to be the most robust local invariant feature descriptor for automatically registering various RS images. The SIFT descriptor has two shortcomings: it is unsuitable for extremely large images and has an irregular distribution of feature points. Therefore, three steps are proposed for the MIARS: image division, histogram equalization and the elimination of false point matches by a subregion least squares iteration. Image division makes it possible to use the SIFT descriptor to extract control points from an extremely large RS image. Histogram equalization in prematching improves the contrast sensitivity of RS images. The subregion least squares iteration refines the registration accuracy. Images from multisensor systems, including Quickbird, IRS-P6, Landsat/TM, HJ-CCD, HJ-IRS, light detection and ranging (LiDAR) intensity images and aerial data, were selected to test the reliability of the MIARS. The results indicated that better registration accuracy was achieved, which will be very helpful in the future development of a registration model.

An improved high spatial and temporal data fusion approach for combining Landsat and MODIS data to generate daily synthetic Landsat imagery

Two key weaknesses of STDFA including sensor difference and spatial variability were adjusted.Three wildly used spatial and temporal fusion methods were compared.The correlation coefficient r had a negative exponential relationship with ratio of land cover change pixels.The accuracy of ISTDFA method had a logarithmic relationship with the size of applied area. Because of low temporal resolution and cloud influence, many remote-sensing applications lack high spatial resolution remote-sensing data. To address this problem, this study introduced an improved spatial and temporal data fusion approach (ISTDFA) to generate daily synthetic Landsat imagery. This algorithm was designed to avoid the weaknesses of the spatial and temporal data fusion approach (STDFA) method, including the sensor difference and spatial variability. A weighted linear mixed model was used to adjust the spatial variability of surface reflectance. A linear-regression method was used to remove the influence of differences in sensor systems. This method was tested and validated in three study areas located in Xinjiang and Anhui province, China. The other two methods, the STDFA and the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), were also applied and compared in those three study areas. The results showed that the ISTDFA algorithm can generate daily synthetic Landsat imagery accurately, with correlation coefficient r equal to 0.9857 and root mean square error (RMSE) equal to 0.0195, which is superior to the STDFA method. The ISTDFA method had higher accuracy than ESTARFM in areas greater than 200?× 200 MODIS pixels while the ESTARFM method had higher accuracy than the ISTDFA method in small areas. The correlation coefficient r had a negative power relation with ratio of land-cover change pixels. A land-cover change of 20.25% pixels can lead to a reduced correlation coefficient r of 0.295 in the blue band. The accuracy of the ISTDFA method indicated a logarithmic relationship with the size of the applied area, so it is recommended for use in large-scale areas.

Predicting leaf area index in wheat using angular vegetation indices derived from in situ canopy measurements.

Canopy structure significantly affects ecosystem function by influencing light attenuation, and thus there is a great need to characterize changes in canopy structural attributes such as leaf area index (LAI). This study presents an evaluation of a set of anisotropic vegetation indices (VIs) used in the estimation of LAI in the growth cycle of wheat. An analytical two-layer canopy reflectance model (ACRM) was used to simulate a range of bidirectional reflectance with different LAI variations and view zenith angles (VZAs). A number of indices, including the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), the hot spot - dark spot index (HDS), the hot spot – dark spot NDVI (NDVIHD), and a new proposed hot spot – dark spot difference index (HDDI), were selected and compared for linearity with increasing LAI in the sensitivity study. Simulation results indicated that EVI was a better candidate than NDVI for LAI estimation in linearity. HDS560 and HDS670 showed nonmonotonical patterns with increasing LAI, and better resistance to saturation limits was observed for both HDS750 and HDS870. Indices of NDVIHD (NDVIHD870 and NDVIHD750) were found to have low sensitivity at high LAI values, with a clear saturation for LAI values above 3. For the two new angular indices, HDDI705 was found to have a relatively linear relationship in an LAI range of 1–5. HDDI750 showed the best linear relationship with LAI, and no saturation was observed for LAI values in the range 1–8. A validation study was conducted for the growth cycle of two types of wheat. NDVI and EVI showed reasonable potential in the estimation of LAI, with determination coefficients R2 of 0.68 and 0.78, respectively, and corresponding root mean square errors (RMSE in LAI units) of 0.79 and 0.66. Band selection was demonstrated to have significant effects on HDS indices. HDS560 showed a very low correlation with LAI (R2 = 0.40, RMSE = 0.85), and no correlation was found between HDS670 and LAI. However, both HDS750 and HDS870 can provide moderate estimates of LAI, with R2 values of 0.76 and 0.73, respectively, and corresponding RMSE values of 0.61 and 0.65. For the NDVIHD indices, low determination coefficients R2 of 0.51 and 0.47 were obtained (RMSE = 0.81 and 0.88). For the two new angular indices, significant correlations (R2 = 0.84 and 0.85, respectively, and RMSE = 0.48 and 0.45) were observed for HDDI705 and HDDI750. These preliminary results provide certain insights for the development of future multi-angle remote sensing models for LAI estimation with satellite observations in other ecosystems.

High-resolution Leaf Area Index estimation from synthetic Landsat data generated by a spatial and temporal data fusion model

Leaf area index (LAI) is an important input parameter for biogeochemical and ecosystem process models. Mapping LAI using remotely sensed data has been a major objective in remote sensing research to date. However, the current LAI product mapped by remote sensing is both spatially and temporally discontinuous as a result of cloud cover, seasonal snows, and instrumental constraints. This has limited the application of LAI to ground surface process simulations, climatic modeling, and global change research. To fill these gaps in LAI products, this study develops an algorithm to provide high spatial and temporal resolution LAI products with synthetic Landsat data, generated by a spatial and temporal data fusion model (STDFA). The model has been developed and validated within the Changping District of Beijing, China. Using Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data and real Landsat data, this method can generate LAI data whose spatial (temporal) resolution is the same as that of the Landsat (MODIS) data. Linear regression analysis was performed to compare the modeled data with field-measured LAI data, and indicates that this new method can provide accurate estimates of LAI, with R-2 equal to 0.977 and root mean square error (RMSE) equal to 0.1585 m(2) m(-2) (P < 0.005), which is superior to the standard MODIS LAI product. Further, various STDFA model application strategies were tested, with the results showing that the application strategy of the STDFA model has an important influence on the accuracy of LAI estimation: the vegetation index fusion strategy produced a better result than the reflectance fusion strategy. The applications of the STDFA model to eight commonly used vegetation indices were also compared. The results show that some vegetation indices (e.g., Enhanced Vegetation Index (EVI), Normalized difference vegetation index (NDVI), and Normalized difference infrared index (NDII)) exhibited better performance than others (e.g., Infrared simple ratio (ISR), Reduced infrared simple ratio (RISR), Reduced normalized difference vegetation Index (RNDVI), Reduced simple ratio (RSR), and Simple ratio (SR)). However, ISR, RISR, and NDII data produced lower saturation effects than other spectral vegetation indices in the estimation of LAI values higher than 2 m(2) m(-2)