Jianxi Huang

Mapping rice planting areas in southern China using the China Environment Satellite data

The objective of this research is to investigate the potential of application of China Environment Satellite HJ-1A/B in monitoring rice cultivation areas in Guangdong province in southern China. Information on the rice cultivation areas is of global economic and environmental significance. A CCD camera sensor with 30 m spatial resolution onboard China Environment Satellite HJ-1A and B has visible and near infrared bands and a revisit period of four days; the temporal Normalized Difference Vegetation Index (NDVI) can therefore be obtained from HJ-1A and B data. The characteristics of the temporal NDVI derived from HJ-1A and B images of rice fields and other crops at rice growth stages in the western part of Guangdong province of China with an area of about 67000 km^2 were first analyzed in this research and an algorithm for mapping paddy rice fields was developed based on the temporal changes of NDVI of rice fields from January to July, 2009. The mapping result was evaluated by field survey and the data from China Ministry of Agriculture and the promising accuracy was found with a Kappa factor of 0.71. The result of this study suggests that the China Environment Satellite HJ-1A/B has great potential in the development of an operational system for monitoring rice crop growth in southern China.

Jointly Assimilating MODIS LAI and ET Products Into the SWAP Model for Winter Wheat Yield Estimation

Leaf area index (LAI) and evapotranspiration (ET) are two crucial biophysical variables related to crop growth and grain yield. This study presents a crop model-data assimilation framework to assimilate the 1-km moderate resolution imaging spectroradiometer (MODIS) LAI and ET products (MCD15A3 and MOD16A2, respectively) into the soil water atmosphere plant (SWAP) model to assess the potential for estimating winter wheat yield at field and regional scales. Since the 1-km MODIS products generally underestimate LAI or ET values in fragmented agricultural landscapes due to scale effects and intrapixel heterogeneity, we constructed a new cost function by comparing the generalized vector angle between the observed and modeled LAI and ET time series during the growing season. We selected three parameters (irrigation date, irrigation depth, and emergence date) as the reinitialized parameters to be optimized by minimizing the cost function using the shuffled complex evolution method-University of Arizona (SCE-UA) optimization algorithm, and then used the optimized parameters as inputs into the SWAP model for winter wheat yield estimation. We used four data-assimilation schemes to estimate winter wheat yield at field and regional scales. We found that jointly assimilating MODIS LAI and ET data improved accuracy (R2 = 0.43, RMSE = 619 kg · ha-1) than assimilating MODIS LAI data (R2 = 0.28, RMSE = 889 kg · ha-1) or ET data (R2 = 0.36, RMSE = 1561 kg·ha-1) at the county level, which indicates that the proposed estimation method is reliable and applicable at a county scale.

Analyzing disaster-forming environments and the spatial distribution of flood disasters and snow disasters that occurred in China from 1949 to 2000

Flood disasters and snow disasters are frequent disasters in China, causing considerable economic loss and serious damage to towns and farms. The problems of how these two disaster types distribute and what disaster-forming environments are important to their occurrence are the most pressing problems in disaster risk assessment and salvage material arrangement. The present study aims to establish the regularity of flood/snow disaster outbreaks and the important disaster-forming environmental factors, and a spatial autocorrelation analysis method and a canonical correlation analysis method are used to answer these two questions separately. Experimental results indicate that serious flood disasters distribute mainly on the south area of China and snow disasters occur on the north area; those areas should be allocated correspondingly more salvage materials. And some disaster-forming environmental factors are important for the occurrence of flood/snow disasters, and can be used in disaster risk assessment.

Mapping of Daily Mean Air Temperature in Agricultural Regions Using Daytime and Nighttime Land Surface Temperatures Derived from TERRA and AQUA MODIS Data

Air temperature is one of the most important factors in crop growth monitoring and simulation. In the present study, we estimated and mapped daily mean air temperature using daytime and nighttime land surface temperatures (LSTs) derived from TERRA and AQUA MODIS data. Linear regression models were calibrated using LSTs from 2003 to 2011 and validated using LST data from 2012 to 2013, combined with meteorological station data. The results show that these models can provide a robust estimation of measured daily mean air temperature and that models that only accounted for meteorological data from rural regions performed best. Daily mean air temperature maps were generated from each of four MODIS LST products and merged using different strategies that combined the four MODIS products in different orders when data from one product was unavailable for a pixel. The annual average spatial coverage increased from 20.28% to 55.46% in 2012 and 28.31% to 44.92% in 2013.The root-mean-square and mean absolute errors (RMSE and MAE) for the optimal image merging strategy were 2.41 and 1.84, respectively. Compared with the least-effective strategy, the RMSE and MAE decreased by 17.2% and 17.8%, respectively. The interpolation algorithm uses the available pixels from images with consecutive dates in a sliding-window mode. The most appropriate window size was selected based on the absolute spatial bias in the study area. With an optimal window size of 33 × 33 pixels, this approach increased data coverage by up to 76.99% in 2012 and 89.67% in 2013.

Comparison of two retrieval methods with combined passive and active microwave remote sensing observations for soil moisture

The brightness temperature (BT) and backscattering coefficient (BSC) measured simultaneously by passive and active microwave sensors have great potential for the estimation of land surface soil moisture (SM). Several methods with combined passive and active microwave remote sensing observations for SM have been reported. Usually, the use of these methods, requires an accurate roughness condition, especially when dealing over the bare surface or surface with low vegetation. In this paper, two different retrieval methods for estimating SM using synthetic microwave remote sensing data were compared. The difference between the two methods is the way to estimate the roughness parameter. The three-parameter retrieval approach (THRA) takes the roughness parameter as a variable to be retrieved, and derives land surface parameters (e.g., SM, surface temperature (ST) and roughness) simultaneously from BT and BSC using nonlinear algorithms. The two-parameter retrieval approach (TWRA) retrieves roughness from BSC firstly, then takes the roughness as an input parameter to retrieve SM and ST from BT. The two retrieval methods were applied on AMSR-E (Advance Microwave Scanning Radiometer for EOS) and QuikSCAT/Seawinds (The Seawinds scatterometer on NASAs (National Aeronautics and Space Administration) Quick Scatterometer) observations which have been carried out for the SMEX03 (Soil Moisture Experiment 2003) region in ON, the north of Oklanoma. The comparison results shows TWRA has achieved a higher accuracy than THRA in dealing with the active and passive microwave observations at different overpass times.

A new hierarchical moving curve-fitting algorithm for filtering lidar data for automatic DTM generation

Recent advances in laser scanning hardware have allowed rapid generation of high-resolution digital terrain models (DTMs) for large areas. However, the automatic discrimination of ground and non-ground light detection and ranging (lidar) points in areas covered by densely packed buildings or dense vegetation is difficult. In this paper, we introduce a new hierarchical moving curve-fitting filter algorithm that is designed to automatically and rapidly filter lidar data to permit automatic DTM generation. This algorithm is based on fitting a second-degree polynomial surface using flexible tiles of moving blocks and an adaptive threshold. The initial tile size is determined by the size of the largest building in the study area. Based on an adaptive threshold, non-ground points and ground points are classified and labelled step by step. In addition, we used a multi-scale weighted interpolation method to estimate the bare-earth elevation for non-ground points and obtain a recovered terrain model. Our experiments in four study areas showed that the new filtering method can separate ground and non-ground points in both urban areas and those covered by dense vegetation. The filter error ranged from 4.08% to 9.40% for Type I errors, from 2.48% to 7.63% for Type II errors, and from 5.01% to 7.40% for total errors. These errors are lower than those of triangulated irregular network filter algorithms.

An optimized sampling model in the survey of groundwater resource nitrate content of rural areas in the Shandong province

Based on the survey result of the nitrate content of the groundwater samples, this paper studies the variability of nitrate content of the groundwater in the Shandong province by using an optimised sampling model. Meanwhile, it also calculates the sample size required by the survey based on precision (relative error) and guarantying degree of probability. The calculation indicates that the coefficient of variation of nitrate content of groundwater in the Shandong province is 1.12, with the set survey relative error of 10%, confidence probability of 95%, and 482 required survey samples. By using pseudo-random numbers, the residential areas are selected from county level units as the survey sites, and the groundwater sources of the residential sites of villages and townships are taken as the survey targets. The first-order spatial distribution test on the residential sites is conducted by using the nearest neighbor index method, to analyse the spatial relation between the nearest neighbor sites. For the sampling sites which accord with the first-order spatial distribution, the multiple order spatial distribution test is conducted by using Ripleys function, so as to meet the requirement of spatial random distribution of sample sites.

Assimilating MODIS-LAI into Crop Growth Model with EnKF to Predict Regional Crop Yield

Regional crop yield prediction is a vital component of national food security assessment. Data assimilation method which combines crop growth model and remotely sensed data has been proven the most potential method in regional crop production estimation. This paper takes Hengshui district as study area, WOFOST as crop model, MODIS-LAI as observation data to test and verify the efficiency of EnKF assimilation method. The results show that the precision of crop yield estimation are obviously improved with EnKF assimilation, in the WOFOST potential level the R2 improved from 0.10 to 0.38 and RMSE was reduced from 2480 kg/ha to 880kg/ha. Our study indicates that EnKF assimilation method has great potential in regional crop production forecasting.

RDCRMG: A Raster Dataset Clean & Reconstitution Multi-Grid Architecture for Remote Sensing Monitoring of Vegetation Dryness

In recent years, remote sensing (RS) research on crop growth status monitoring has gradually turned from static spectrum information retrieval in large-scale to meso-scale or micro-scale, timely multi-source data cooperative analysis; this change has presented higher requirements for RS data acquisition and analysis efficiency. How to implement rapid and stable massive RS data extraction and analysis becomes a serious problem. This paper reports on a Raster Dataset Clean & Reconstitution Multi-Grid (RDCRMG) architecture for remote sensing monitoring of vegetation dryness in which different types of raster datasets have been partitioned, organized and systematically applied. First, raster images have been subdivided into several independent blocks and distributed for storage in different data nodes by using the multi-grid as a consistent partition unit. Second, the “no metadata model” ideology has been referenced so that targets raster data can be speedily extracted by directly calculating the data storage path without retrieving metadata records; third, grids that cover the query range can be easily assessed. This assessment allows the query task to be easily split into several sub-tasks and executed in parallel by grouping these grids. Our RDCRMG-based change detection of the spectral reflectance information test and the data extraction efficiency comparative test shows that the RDCRMG is reliable for vegetation dryness monitoring with a slight reflectance information distortion and consistent percentage histograms. Furthermore, the RDCGMG-based data extraction in parallel circumstances has the advantages of high efficiency and excellent stability compared to that of the RDCGMG-based data extraction in serial circumstances and traditional data extraction. At last, an RDCRMG-based vegetation dryness monitoring platform (VDMP) has been constructed to apply RS data inversion in vegetation dryness monitoring. Through actual applications, the RDCRMG architecture is proven to be appropriate for timely vegetation dryness RS automatic monitoring with better performance, more reliability and higher extensibility. Our future works will focus on integrating more kinds of continuously Remote Sens. 2018, 10, 1376; doi:10.3390/rs10091376 www.mdpi.com/journal/remotesensing Remote Sens. 2018, 10, 1376 2 of 24 updated RS data into the RDCRMG-based VDMP and integrating more multi-source datasets based collaborative analysis models for agricultural monitoring.

The Estimation of Tree Height Based on LiDAR Data and QuickBird Imagery

The estimation of tree height is advanced following the development of LiDAR technique. The estimation model of tree height considering suppressed trees is developed in order to extract tree height accurately using LiDAR data. Filtered LiDAR data and Quickbird imagery are segmented using watershed segmentation method based on mathematical morphology to get the boundary of trees. And the highest point in each canopy object is used to estimate tree height. Weibull distribution is used to estimate height distribution of the suppressed trees. The experiment results indicate that the watershed segmentation method based on mathematical morphology is an effective method to extract the boundary of trees. And the R2 between the tree height estimated using estimation model of tree height considering suppressed trees and the tree height measured by field work is 0.93.