Wei Liu

A total suspended sediment retrieval model for multiple estuaries and coasts by Landsat imageries

Based on 119 in-situ data from five estuaries and coasts of South China including Xunwen coast, estuary of Moyangjiang River, estuary and coast of Pearl River, estuary of Hanjiang River and estuary of Yangtze River, this paper aims to develop and establish a TSS retrieval model that applicable in different field conditions. After recalibrating and validating the form with the highest correlation coefficient between reflectance and TSS concentration and other TSS retrieval models that have been successful applied in many places, we found that the quadratic model of the ratio of logarithmic transformation of red band and near infrared band and logarithmic transformation of TSS concentration (QRLTSS) shows the highest performance. QRLTSS model based on Landsat OLI, ETM+ and TM can explained about 71% of the TSS concentration variation (4.3~577.2 mg/L) in the five regions and has a high and acceptable validation accuracy with root mean square error (RMSE) of 21.5-25mg/L and mean relative error (MRE) of 27.2-32.2%. We concluded that QRLTSS model can be used to quantify the TSS concentration of multiple estuaries and coasts of south China which would be helpful to understand the temporal and spatial variation of TSS in a large region. QRLTSS model should be applied to Landsat imagery for further validation in the future. The approach proposed in the paper also could promote the research work of establishing regional and uniform TSS retrieval model forward.

Spatiotemporal computing of cold wave characteristic in recent 52 years: a case study in Guangdong Province, South China

AbstractnBased on daily air-temperature data of 86 weather stations in recent 52 years (1961–2012) in Guangdong Province, South China, annual cold wave events in each station were calculated. Then, the spatiotemporal characteristics of cold wave over Guangdong Province were thoroughly analyzed by wavelet transform and other statistical analysis method. The results indicate that: (1) The cold wave frequency gradually decreased from north to south, from inland to coastal area and from highland to lower land showing evident regional variance; if there occurred a large range of cold wave, the temperature fall caused by cold wave would be bigger in southwestern area than in the rest of Guangdong Province; (2) 161 regional cold wave events in total invaded Guangdong in recent 52 years. The cold waves can be classified into three invasion paths: north, northeast and west. Most of cold waves took the north path, and a few of them took the west path only; (3) Based on the influence range, the cold wave can be divided into 4 grades. The climatic characteristic of each grade shows that the larger the range of influence was, the more severe the hazard of cold wave would be; (4) Cold wave whose influence range was smaller than 10xa0% of provincial area happened almost every year, and the frequency was 1.9 annually; cold wave whose influence range was larger than 50xa0% happened 13 times in recent 52 years; (5) The intensity of cold wave over Guangdong was declining at the rate of 5.5 station-day per year; it also had an inter-decadal oscillation period of 10–14xa0years, and an obvious inter-annual oscillation period of 3–4xa0years before 2000; (6) Cold wave frequency of each month varied significantly, 76xa0% of which occurred during December to next February; (7) The snow disaster of 2008 had caused great losses to Guangdong agriculture. But according to the criteria of cold wave, only 22 stations (25xa0%) of Guangdong actually reached the standard of cold wave. Therefore, the cold disaster index should be additionally considered when analyzing the impact of disaster by cold air process, and the relationship between cold wave and cold disaster is worth further research.

Spatiotemporal Analysis of MODIS Land Surface Temperature With In Situ Meteorological Observation and ERA-Interim Reanalysis: The Option of Model Calibration

The land surface temperature (<italic>Ts</italic>) is an important parameter in land surface and atmosphere studies. A set of synchronously observed “ground-truth” temperature as training data is required for some empirical/semiempirical statistical and neural network methods for retrieving <italic>Ts</italic> from passive microwave (PMW) remote sensing data. To provide information for the choice of the most suitable dataset in <italic>Ts </italic> retrieval of PMW remote sensing, the spatiotemporal comparison between the moderate-resolution imaging spectroradiometer <italic>Ts</italic> (MODIS <italic>Ts</italic>), the meteorologically observed <italic>Ts</italic> ( <italic>in situ Ts</italic>), the meteorologically observed near-surface air temperature (<italic>in situ Ta</italic>), and European Center for Medium-Range Weather Forecast reanalysis products, the ERA-Interim <italic>Ts</italic> (ERA <italic>Ts</italic>), in South China for each seasons daytime and nighttime is conducted in this paper. Results show that a large discrepancy between the MODIS <italic>Ts</italic> and the <italic>in situ Ts</italic> exists, whereas the discrepancies between the MODIS <italic>Ts</italic>, the <italic>in situ Ta</italic> and the ERA <italic> Ts</italic> are relatively smaller in daytime. For nighttime period, the differences between each dataset are relatively much smaller. Because the MODIS <italic>Ts</italic> is representative at the satellite pixel scale, it has a smaller spatial-scale mismatch with PMW data compared to <italic>in situ</italic> meteorological observation. The MODIS <italic>Ts</italic> is suitable for both the daytime and the nighttime PMW <italic>Ts</italic> model calibration if it is synchronously observed under almost clear-sky condition. By contrast, for the PMW <italic>Ts</italic> model calibration within the daytime period, the synchronously obtained <italic>in situ Ts</italic> is not suitable to be used as training data. If the ground temperature of daytime period derived from PMW is required, but the MODIS <italic> Ts</italic> is unavailable, the <italic>in situ Ta</italic> should be selected as the “ground truth” for the model calibration. However, it should be noticed that the inversion results are the near-surface air temperature rather than the <italic>Ts</italic>. Remarkably, reanalysis products such as the ERA <italic>Ts</italic> presents an alternative choice for both the daytime and the nighttime <italic>Ts</italic> model calibration if there are no MODIS <italic>Ts</italic> products or <italic>in situ</italic> temperature available. After the comparison, an example of PMW <italic>Ts</italic> retrieval for nighttime period was given, showing a promising performance on deriving an applicable PMW <italic>Ts</italic> inversion model based on the selection of an appropriate training dataset.

Temperature variation and winter planted potato's NDVI change during early 2016's super cold wave in Guangdong province, South China

under the background of climate warming, obvious decrease trend in frequency and intensity of cold wave that invaded Guangdong province, South China have been observed in recent years. However in January of 2016, a named “super cold wave” which caused serious influence on human health and agriculture production invaded Guangdong. In this paper, satellite derived land surface temperature (LST) from GCOM-W1/AMSR2 brightness temperature (TB) were retrieved. Based on the satellite-derived LST, its variation characteristic was analyzed, which showed an apparent decrease trend and increase trend in Guangdong province during the whole “super cold wave” process. Moreover, based on NDVI retrieved from HJ-1 A/B satellites CCD sensors, obvious declination of potatos NDVI after the “super cold wave” can be seen: nearly 89% area of potatos NDVI decreased reflecting great loss on potatos production which was confirmed by in situ investigation. This study demonstrated that multi-sources satellite remote sensing data have the capability of monitoring temperature change in cold wave and assessing crop loss after cold injury.

Establishment of remote sensing monitoring method and standard on winter planting crops: A case study in Leizhou Peninsula of south China

The winter crop planting can bring increased income for peasants, but also improve the soil quality by rotation system. So, the planting area monitoring remote sensing of winter crops have been an important work for agriculture planting structure statistic, management and decision in south China. At present the cloudy and rainy weather is a very serious issue for remote sensing of winter crops in south China. So grasping the phenology of winter crops is of vital significance to establishment of remote sensing monitoring method and standard on winter crops. In order to form the remote sensing monitor method and standard to extract the planting area of winter crops, the paddy field and dryland based multi-temporal spectral angle remote sensing approach and standard were put forward, that includes five Landsat OLI image spectra and corresponding NDVI to compose the reference spectra of 40 bands during autumn and winter season. The mapping result in 12 experimental plots showed that the remote sensing monitoring standard performed well for retrieving the winter planting crops in Guangdong province of south China.

Winter Wheat Leaf Area Index (LAI) Inversion Combining with HJ-1/CCD1 and GF-1/WFV1 Data

The LAI is the key factor which has an important influence on crop growth. LAI inversion from remote sensing is an important work in crop management. While, the accuracy of LAI inversion from remote sensing data is restricted by the limited number of observation. Multiple-sensor method has been proposed by the researchers. In this study, two sensor remote sensing data (HJ-1A/CCD1 and GF-1/WFV1) were collected in the study area. The random forest regression (RFR) was adopted in LAI inversion. The MODIS LAI product and the measured wheat LAI were used to calibrate and validate the LAI inversion model. The four spectral indices (DVI, SR, EVI, and SAVI) based on remote sensing data were calculated to develop the LAI inversion model. The accuracy of inversion of wheat LAI by remote sensing image can be improved by adding observations of angle data. Our data analysis resulted in an accuracy of R2 = 0.36, MAE = 0.467, and RMSE = 0.613 for the measured LAI. And in the validation by MODIS LAI product, an accuracy of R2 = 0.48, MAE = 1.05, and RMSE = 2.72 was found, which was a little greater than the average accuracy of mono-angle data for inversion of LAI. The result indicates that the reasonable combination of multi-sensor data can improve the accuracy of LAI estimation.

Leaf Area Index Estimation of Winter Pepper Based on Canopy Spectral Data and Simulated Bands of Satellite

Leaf area index (LAI) is an important indicator of crop growth status. In this paper, the relationships between canopy reflectance at 400–2500 nm and leaf area index (LAI) in pepper crop were studied. 102 pair of canopy reflectance and LAI of pepper were collected in 2014–2015. Reflectance of canopy were measured in the field over a spectral range of 400–2500 nm. Simultaneously, the LAI were collected by the LAI-2000. Estimation models of LAI were developed based on the whole spectrum range by partial least squares regression (PLSR) and support vector regression (SVR), respectively. Then the field canopy spectra were resampled according to the band response functions of seven satellite sensors. They were the Vegetation and environment monitoring on a new micro-satellite (VENμS), Worldview-2 (WV-2), RapidEye-1 (RE-1), HJ1/CCD1, Sentinel-2, Landsat 8/OLI and GaoFen (GF) 1/WFV1. The values of common used spectral indices were calculated based on the simulated sensor bands, respectively. Prediction models were also developed based on the spectral indices and simulated bands. The results showed that the PLSR model by whole spectrum had the good accuracy of LAI estimation with the R2c = 0.726, RMSEc = 0.462, R2cv = 0.635, RMSEcv = 0.538. For the simulated satellite datasets, the better LAI estimation were obtained by Sentinel-2 and Venμs bands with the R2cv greater than 0.600 and RMSEcv less than 0.557. The Estimation model by simulated WV-2 bands, and RE-1 bands had the lowest performance with the R2cv between 0.50 and 0.55, and RMSEcv between 0.600 and 0.623. The inversion results demonstrated the potential of the multispectral remote sensing data to calibrate the LAI estimation model of winter pepper for the precision agriculture application.

HJ satellite based mapping technologies of land use products for emergency response of agricultural disasters

Accurate and reliable information on land use is a basis of agricultural disaster warning and emergency action. The natural disasters typhoon, cold disaster, drought, and so on, have great influences on agricultural production in Guangdong Province, China. Through literature analysis at home and abroad, it was pointed out that Chinese HJ satellites have become important sources of remote sensing images due to short imaging period and broad coverage (about 2 days and 700km). It also advances the free sharing of OLI data on the new generation of remote sensor of Landsat. Considering the severe orbit deviation and frequent phenology changes of agricultural land use, this paper described and laid emphasis on the necessity and perspective of developing multi-scale (1:500,000, 1:20,000, 1:50,000) agricultural land use products of Provincial-City-County within Guangdong through the usage of relatively fixed ground control point database and spectral library based advanced hierarchical classification technologies supported by HJ satellite data. Such a tendency of quick disaster emergency response for land use dynamic implies a technological focus on the usage of above-mentioned advanced HJ satellite technologies. Based on Landsat images, we also built the 11 united ground control points (40 points at most for whole Guangdong Province) and standardized the technology system of land use remote sensing mapping with the combination of spectral library-based hierarchy classification technology. Selected key technologies for enhancing land use production mapping efficiency and accuracy that involve Landsat OLI and HJ satellites are presented and discussed. The HJ satellite is an effective information source for land use dynamic mapping under emergency action such as disaster damage evaluation, which can provide a short imaging period with wide spatial coverage and enhance the ability of land use data acquirement from one to three or four times or so in one year. The building of relatively fixed ground control points increase the efficiency of province-level land use mapping from one week to two days. The proposed method is especially useful for the development of land use products in cloudy and rainy south China.