International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
    
OPTIMIZATION OF DECISION-MAKING FOR SPATIAL SAMPLING IN THE NORTH 
CHINA PLAIN, BASED ON REMOTE-SENSING A PRIORI KNOWLEDGE 
Jianzhong Feng ® © *, Linyan Bai, Shihong Liu *°, Xiaolu Su°, Haiyan Hu“ 
* Key Laboratory of Agri-information Service Technology, Ministry of Agriculture, Beijing, 100081, China - 
fengjzh4680@sina.com 
? Center for Earth Observation and Digital Earth Chinese Academy of Sciences, Beijing, 100101, China - 
lybai@ceode.ac.cn 
* Institute of Agriculture Information, Chinese Academy of Agricultural Sciences, Beijing 100081, China — 
fengjzh0338@sina.com 
Commission VII, WG VII/4 
KEY WORDS: 
RIP(s)/RIV(s), Sampling efficiency 
ABSTRACT: 
Agricultural Spatial Sampling, Remote Sensing, a Priori Knowledge, Spatial Structure Characteristics, 
In this paper, the MODIS remote sensing data, featured with low-cost, high-timely and moderate/low spatial resolutions, in the North China 
Plain (NCP) as a study region were firstly used to carry out mixed-pixel spectral decomposition to extract an useful regionalized indicator 
parameter (RIP) (i.e., an available ratio, that is, fraction/percentage, of winter wheat planting area in each pixel as a regionalized indicator 
variable (RIV) of spatial sampling) from the initial selected indicators. Then, the RIV values were spatially analyzed, and the spatial 
structure characteristics (i.e., spatial correlation and variation) of the NCP were achieved, which were further processed to obtain the scale- 
fitting, valid a priori knowledge or information of spatial sampling. Subsequently, founded upon an idea of rationally integrating 
probability-based and model-based sampling techniques and effectively utilizing the obtained a priori knowledge or information, the spatial 
sampling models and design schemes and their optimization and optimal selection were developed, as is a scientific basis of improving and 
optimizing the existing spatial sampling schemes of large-scale cropland remote sensing monitoring. Additionally, by the adaptive analysis 
and decision strategy the optimal local spatial prediction and gridded system of extrapolation results were able to excellently implement an 
adaptive report pattern of spatial sampling in accordance with report-covering units in order to satisfy the actual needs of sampling surveys. 
1. INTRODUCTION 
For a long time, cropland area estimate using remote sensing 
technique is an important research topic of accurately estimating 
large-area crop yields by remote sensing approaches (Wang et al., 
2008). It is very important for macro-economic decision-making 
departments of governments to timely know the related crop 
production and make scientific and sound decisions, etc. Spatial 
sampling technology is used to be able to effectively resolve this 
problem that a balance is realized between the cropland area 
accurate estimation and limited investigation budget (Li et al., 2004). 
It is essential for spatial sampling that the basic principles and 
methods of statistical sampling are applied to the regionalized 
attributes of geographical objects. Therefore, there are the 
probability spatial sampling techniques, based on traditional 
statistics, (such as simple random, systematic, stratified, and cluster 
spatial sampling) and model spatial sampling that are tied to spatial 
variability theory (Stevens Jr. and Olsen, 2004; Li et al., 2004; 
Dobbie and Henderson, 2008; Jia et al., 2008; Jiang et al., 2009). 
Due to the complexity of geographical features in a region, the 
traditional large-scale spatial sampling of resources or 
environmental investigation (especially on agricultural monitoring 
with remote sensing) are operated not well to use the a priori 
knowledge or information of spatial structure characteristics of 
some research region, but, relied on a certain sampling schema, 
  
* Corresponding author. 
more often to use common probability sampling models and 
methods, such that they cannot meet actual requirements of spatial 
sampling investigation because their sampling efficiencies are not 
very high and ranges of sampling application are not large (Jiao et 
al., 2002, 2006; Wu et al., 2004). 
In this paper, founded upon an idea of rationally integrating 
probability-based and model-based sampling techniques and 
properly using the relevant sampling models and methods, the 
valid spatial sampling design was developed in the North China 
Plain (NCP) as a study region, which is associated with remote 
sensing a priori knowledge or information, and the existing spatial 
sampling schemes were suggested to improve and optimize in 
better order to service the large-scale cropland remote sensing 
monitoring in the NCP. In short, this study can further effectively 
enhance the level of spatial sampling survey and decision-making 
analysis of the related departments of national and local 
governments. 
2. METHODOLOGY 
2.1 Models of a priori information acquisition 
Geographical spatial structure characteristics are represented 
with the spatially correlated and heterogeneous properties of 
regionalized geographical elements (of surface things or 
phenomena) (Feng, 2010).