Qingbo Zhou

Estimation of winter wheat acreage via a combination of remotely sensed data and an optimized spatial sampling scheme

Timely and accurate estimates of crop areas are critical to enhancing agriculture management and ensuring national food security. This study aims to combine remote-sensing data and an optimized spatial sampling scheme to improve the accuracy of crop area estimates and decrease the cost of crop surveys at a regional scale. This study focuses on winter wheat in Mengcheng County in Anhui Province, China. Advanced Land Observing Satellite (ALOS) Advanced Visible light and Near Infrared Radiometer (AVNIR)-2, and Landsat5 Thematic Mapper (TM) images from 2009 and 2010, respectively, are used to extract the winter wheat area and distribution. Additionally, a spatial sampling scheme was optimized by combining remotely sensed data, geographical information system (GIS), Geostatistics, and traditional sampling methods. The experimental results demonstrate that the variability in the proportion of winter wheat acreage in one sampling unit (PWS) increases with increasing sampling unit size. The PWS coefficient of variation (CV) varies from 32.75 to 43.46% among the eight sampling unit sizes. The spatial correlation thresholds of PWS increase with increasing sampling unit size. For small sampling unit sizes (500 m × 500 m–2000 m × 2000 m), the relative error and CV of the population extrapolation for the optimized sample layout are obviously lower than those of the simple random sampling method. For larger sampling unit sizes (2500 m × 2500 m–4000 m × 4000 m), the sample size is obviously lower for the optimized sample layout compared with that of the simple random sampling method, but there are no differences in the relative errors or CVs. By combining remote-sensing data and the optimized spatial sampling scheme, this research can improve the accuracy of crop area estimation at a regional scale.

Extraction of Planting Areas of Major Crops and Crop Growth Monitoring in Northeast China

Abstract This paper presents a method used in China Agriculture Remote Sensing Monitoring System (CHARMS) for automatically identifying crop planting areas and monitoring crop growth conditions at a large scale based on time-series of MODIS NDVI Datasets. In doing that, the characteristics of NDVI time series of spring wheat, spring corn, soybean and rice in Northeastern China were firstly analyzed to determine the threshold values used for extracting different crops. Then using these thresholds, extraction models for above-mentioned four major crops were established and applied to obtain the spatial distribution of these four crops in Northeastern China in 2009. In comparison with the average statistic data of several years, the total extraction accuracy is over 87%, which suggests its feasibility to extract planting areas of different crops at a large scale using MODIS data. Based on the extracted crop planting areas, the same MODIS NDVI time series data were used to monitor crop growth conditions in 20...

Integration of remote sensing and GIS technology to evaluate grassland ecosystem health in north China

Grassland in north China faces serious ecological degradation in recent decades. Overgrazing and unsuitable farming over the grassland are believed to be the direct causes leading to such ecological disasters as sand and dust storms in north China. Objective of the study is to integrate remote sensing and GIS technology for evaluation of grassland environmental heath in north China. Methodology used in the study includes ground observation, grassland database establishment, reclamation mapping, and remote sensing image interpretation. Our results indicate that grassland reclamation for agricultural farming is very serious in recent decade in north China. About 15% of grassland has been reclaimed into farmland in the transaction zone of north China in a short period from 1985 to 2000. Most reclaimed farmlands are not suitable for agricultural cropping. In both transaction and pasture zones the areas of unsuitable farmland account for above 20% of total farmland area. Desertification is also very serious in the grassland. 4 of the 6 provinces under study have over half of grassland areas suffering various levels of desertification. Percentage of grassland areas under intensive desertification to the total is high up to 57% in Gansu and over 30% in Inner Mongolia, Oinghai and Ningxia. We classified the grassland into 5 categories according to the degree of desertification. The results show that the categories with slight, moderate and complete desertification mainly concentrate in southern and central Inner Mongolia. This evaluation provides valuable assistances to policy proposals for local administration of farming and grazing activities in the region

Charms - China Agricultural Remote Sensing Monitoring System

With the sustaining economic development in China, the timely, accurate and objective agricultural production information service has been highly demanded by the central and provincial governments. China Agricultural Remote Sensing Monitoring System (CHARMS) is an operational agricultural monitoring system in the Ministry of Agriculture of China to meet this demand.

Study on information extraction of rape acreage based on TM remote sensing image

Chinas rape acreage and total output of rapeseeds ranks among the top in the world, accounting for more than 30% of the worlds total rape acreage and output of rapeseeds. This paper takes Landsat TM as the main data source in conducting the study of extracting rape acreage information in the Shou County, Anhui Province. Through analysis and calculations of phenological diversity, spectral discrimination, etc. of various main vegetations, with remote sensing image in each growth stages of rape within the studied area, this paper concludes that the optimal time period for information extraction of rape acreage based on TM image is the flowering period for rape. This paper adopts confusion matrix calculation to compare non-supervised and supervised classification methods in extracting rape acreage information using remote sensing image. The results show that the classification results of Mahalanobis Distance method and Isodate non-supervised classification method yielded relatively good results. In which, the Isodate non-supervised classification method combined with human visual inspection can extract the rape planting area information with higher precision and efficiency. The study shows that the method by utilizing TM remote sensing data to extract information of rape acreage can get a relatively satisfactory result. We believe the rape acreage remote sensing identification technology can provide a scientific reference to the understanding of Chinas rape planting situation.

An overview of crop growing condition monitoring in China agriculture remote sensing monitoring system

China is a large agricultural country. To understand the agricultural production condition timely and accurately is related to government decision-making, agricultural production management and the general public concern. China Agriculture Remote Sensing Monitoring System (CHARMS) can monitor crop acreage changes, crop growing condition, agriculture disaster (drought, floods, frost damage, pest etc.) and predict crop yield etc. quickly and timely. The basic principles, methods and regular operation of crop growing condition monitoring in CHARMS are introduced in detail in the paper. CHARMS can monitor crop growing condition of wheat, corn, cotton, soybean and paddy rice with MODIS data. An improved NDVI difference model was used in crop growing condition monitoring in CHARMS. Firstly, MODIS data of every day were received and processed, and the max NDVI values of every fifteen days of main crop were generated, then, in order to assessment a certain crop growing condition in certain period (every fifteen days, mostly), the system compare the remote sensing index data (NDVI) of a certain period with the data of the period in the history (last five year, mostly), the difference between NDVI can indicate the spatial difference of crop growing condition at a certain period. Moreover, Meteorological data of temperature, precipitation and sunshine etc. as well as the field investigation data of 200 network counties were used to modify the models parameters. Last, crop growing condition was assessment at four different scales of counties, provinces, main producing areas and nation and spatial distribution maps of crop growing condition were also created.

Intercomparison on Four Irrigated Cropland Maps in Mainland China

Wide-coverage spatial information on irrigated croplands is a vital foundation for food security and water resources studies at the regional level. Several global irrigated-cropland maps have been released to the public over the past decade due to the efforts of the remote sensing community. However, the consistency and discrepancy between these maps is largely unknown because of a lack of comparative studies, limiting their use and improvement. To close this knowledge gap, we compared the latest four irrigated-cropland datasets (GMIA, GRIPC, GlobCover, and GFSAD) in mainland China. First, the four maps were compared quantitatively and neutral regional- and provincial-level statistics of the relative proportions of irrigated land were obtained through regression analysis. Second, we compared the similarities and discrepancies of the datasets on spatial grids. Furthermore, the contributions of mosaic cropland pixels in GlobCover and GFSAD were also analyzed because of their extensive distribution and ambiguous content. Results showed that GMIA has the lowest dispersion and best statistical correlation followed by GRIPC, while the corresponding features of GlobCover and GFSAD are approximately equal. Spatial agreement of the four maps is higher in eastern than western China, and disagreement is contributed mostly by GlobCover and GFSAD. However, divergence exists in the ratios of the different agreement levels, as well as their sources, on a regional scale. Mosaic pixels provide more than half of the irrigated areas for GlobCover and GFSAD, and they include both correct and incorrect information. Our results indicate a need for a uniform quantitative classification system and for greater focus on heterogeneous regions. Furthermore, the results demonstrate the advantage of numerical restriction in the calculations. Therefore, special attention should be paid to integrating databases and to exploring remote sensing features and methods for spatial reconstruction and identification of untypical irrigation areas.

An optimized two-stage spatial sampling scheme for winter wheat acreage estimation using remotely sensed imagery

ABSTRACT Timely and reliable information on crop acreage is essential for formulating grain production policies and ensuring national food security. The combination of available satellite-based remotely sensed images and traditional sampling methods offers the possibility of improved crop acreage estimation at a regional scale. Due to the administrative convenience, reduced survey cost and workload, two-stage sampling has widely been used for crop acreage survey at the large-scale regions. However, compared with single-stage sampling, the two-stage sampling can introduce larger estimation errors, since it has multiple sampling stages. This study’s aim is to optimize the two-stage sampling scheme using satellite-based remotely sensed imagery to improve the accuracy of crop acreage estimation. Taking Mengcheng County, Anhui Province, China, as the study area, this study explored the influence of stratum boundary and sample selection method on the sampling efficiency at the first sampling stage, analysed the impact of sample size on population extrapolation accuracy and then optimized the sample size of the second sampling stage using crop thematic map retrieved by ALOS (Advanced Land Observing Satellite) AVNIR (Advanced Visible light and Near Infrared Radiometer)-2 images in 2009. The results showed that the relative error (RE), coefficient of variation (CV), standard error (SE) of population extrapolation, and sampling fraction (f) using the cumulative square root of frequency (CSRF) method is the minimum among three methods for the stratum boundary determination at the first sampling stage, followed by the equal interval (EI) and equal sample size (ESS) method. Moreover, the RE, CV, and SE of population extrapolation using the ST sampling method is the minimum, compared with simple random (SI) and systematic (SY) sampling method. Therefore, the sampling scheme of the first stage can be optimized by CSRF method for stratum boundary determination and stratified sampling (ST) sampling method for samples selection. At the second sampling stage, RE and CV values of population extrapolation decrease as the sample size increases. Comprehensively considering the accuracy, stability of population extrapolation and sampling cost, the most cost-effective sample size for estimating the winter wheat acreage of the study area is 4. From the perspective of the reasonable selection of sample selection methods, sample size and determination of stratum boundaries, this study provides an important basis for formulating a cost-effective two-stage spatial sampling scheme for crop acreage estimation.

Application of GF-1 data in agricultural monitoring in China

Agriculture plays important role in economic and social development in China. Diverse climatic and geomorphological conditions make it difficult to monitor agriculture timely and accurately in China. In this paper, we demonstrated the application of GF-1 imager and data products in agricultural monitoring in China.

Optimization of spatial sampling schemes for maize acreage estimation

Abstract. Sampling fraction, sample layout, and sampling unit scale are the three basic elements of a spatial sampling scheme. Optimizing these factors plays an important role in decreasing the sampling cost and improving the extrapolation accuracy of survey sampling. Spatial analysis, “3S” techniques, and traditional sampling methods are employed to optimize the three basic elements. Dehui County in Jilin Province, China was chosen as the study area, maize sown acreage as the study object, and square grids as the shape of the sampling units. The experimental results demonstrate that the spatial autocorrelation of sampling unit increases with its scale. When the scale is 500×500  m, there is almost no spatial autocorrelation among sampling units, so 500×500  m is selected as the optimal sampling unit scale. The spatial stratified heterogeneity of sampling units decreases with increasing scale. When the sampling unit scale is 500×500  m, it must be stratified to improve the sampling efficiency. The cultivated land area in one sampling unit can be selected as a stratification criterion due to the significant linear correlation relationship between it and the maize area in all sampling units. Stratified system isometric sampling and 1% are the optimal sample layout pattern and sampling fraction, respectively. This research provides a theoretical basis for improving the spatial sampling efficiency to estimate crop acreage.