Shiguang Xu

The Potential of Time Series Merged from Landsat-5 TM and HJ-1 CCD for Crop Classification: A Case Study for Bole and Manas Counties in Xinjiang, China

Time series data capture crop growth dynamics and are some of the most effective data sources for crop mapping. However, a drawback of precise crop classification at medium resolution (30 m) using multi-temporal data is that some images at crucial time periods are absent from a single sensor. In this research, a medium-resolution, 15-day time series was obtained by merging Landsat-5 TM and HJ-1 CCD data (with similar radiometric performances in multi-spectral bands). Subsequently, optimal temporal windows for accurate crop mapping were evaluated using an extension of the Jeffries–Matusita (JM) distance from the merged time series. A support vector machine (SVM) was then used to compare the classification accuracy of the optimal temporal windows and the entire time series. In addition, different training sample sizes (10% to 90% of the entire training sample in 10% increments; five repetitions for each sample size) were used to investigate the stability of optimal temporal windows. The results showed that time series in optimal temporal windows can achieve high classification accuracies. The optimal temporal windows were robust when the training sample size was sufficiently large. However, they were not stable when the sample size was too small (i.e., less than 300) and may shift in different agro-ecosystems, because of different classes. In addition, merged time series had higher temporal resolution and were more likely to comprise the optimal temporal periods than time series from single-sensor data. Therefore, the use of merged time series increased the possibility of precise crop classification.

Understanding the dependence of the uncertainty in a satellite precipitation data set on the underlying surface and a correction method based on geographically weighted regression

It is widely acknowledged that the complicated underlying surface is one of the prominent reasons leading to serious uncertainty in satellite precipitation data sets over mountainous regions. However, no analysis has been conducted to quantitatively investigate the correlation between the errors in satellite precipitation data sets and the underlying surface. Using 133 monthly rain gauge observations over the Tibetan Plateau, the Bias and the residuals of ordinary least regression (the latter called ‘Ɛ’) in Climate Prediction Center morphing (CMORPH) data were calculated and were fitted with underlying surface factors using the geographically weighted regression (GWR) method, aiming at quantitatively understanding the dependence of the uncertainty in the CMORPH data set on the underlying surface over the Tibetan Plateau. We found that the 39.4% and 50.5% of the variance of the Bias and Ɛ, respectively, could be explained by the digital elevation model, the normalized difference vegetation index and land surface temperature. Furthermore, the explained variance of the Bias and Ɛ could be increased to 53.6% and 75.1%, respectively, by adding the CMORPH to the explanatory variables. Subsequently, the errors in the CMORPH were estimated by the GWR model, which was selected by comparing the explanation strengths of these models, and then the simulated errors were used to correct the precipitation estimated by CMORPH over areas without gauges. Independent validation indicated that the corrected CMORPH showed obviously better performance compared with the uncorrected CMORPH as well as two widely used precipitation estimates – the Universal Kriging interpolation model and the Tropical Rainfall Measuring Mission 3B43. These results reveal the significant influence of the underlying surface on the uncertainty in satellite precipitation data sets over mountainous areas and provide a promising approach to improve the precipitation estimates derived from satellite observations using underlying surface information.

Estimating Summer Precipitation Over the Tibetan Plateau With Geostatistics and Remote Sensing

Abstract Although precipitation is important to climatology, hydrology, and agricultural research, the spatial pattern of precipitation over the Tibetan Plateau is difficult to determine because of complex surface conditions and a sparse rain gauge network. In the present article, a method we named FETCH_OCK—based on a combination of Yin et als Fetch method (2008) and ordinary cokriging (OCK)—is proposed; it was used to estimate monthly summer precipitation over the Tibetan Plateau, which has limited rain gauge observations and a restricted satellite precipitation dataset. First, the monthly ground observations measured by rain gauges were interpolated using OCK, with a digital elevation model (DEM) as the covariant. Second, the spatial variability of the precipitation monitored by satellite was extracted from the Climate Prediction Center morphing (CMORPH) satellite precipitation dataset by calculating a parameter (FETCH) developed from Yin et als Fetch parameter. Finally, the precipitation datasets estimated by OCK were corrected by the FETCH parameter derived from the CMORPH satellite precipitation dataset. Summer (June to August) precipitation over the Tibetan Plateau from 2005 to 2009 was estimated using this model. The precipitation datasets estimated by FETCH_OCK were tested using ground observations from 55 independent rain gauges. The results indicate that the FETCH_OCK model not only is an improvement compared with the input precipitation datasets (OCK and CMORPH) but also performs better than other widely used precipitation datasets, including universal kriging with DEM as a covariant and Tropical Rainfall Measuring Mission 3B43. The present study aims to correct the smoothing effect of kriging interpolation models and to provide a more accurate precipitation dataset for the Tibetan Plateau.

Delineation of Rain Areas with TRMM Microwave Observations Based on PNN

False alarm and misdetected precipitation are prominent drawbacks of high-resolution satellite precipitation datasets, and they usually lead to serious uncertainty in hydrological and meteorological applications. In order to provide accurate rain area delineation for retrieving high-resolution precipitation datasets using satellite microwave observations, a probabilistic neural network (PNN)-based rain area delineation method was developed with rain gauge observations over the Yangtze River Basin and three parameters, including polarization corrected temperature at 85 GHz, difference of brightness temperature at vertically polarized 37 and 19 GHz channels (termed as TB37V and TB19V, respectively) and the sum of TB37V and TB19V derived from the observations of the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). The PNN method was validated with independent samples, and the performance of this method was compared with dynamic cluster K-means method, TRMM Microwave Imager (TMI) Level 2 Hydrometeor Profile Product and the threshold method used in the Scatter Index (SI), a widely used microwave-based precipitation retrieval algorithm. Independent validation indicated that the PNN method can provide more reasonable rain areas than the other three methods. Furthermore, the precipitation volumes estimated by the SI algorithm were significantly improved by substituting the PNN method for the threshold method in the traditional SI algorithm. This study suggests that PNN is a promising way to obtain reasonable rain areas with satellite observations, and the development of an accurate rain area delineation method deserves more attention for improving the accuracy of satellite precipitation datasets.