Using a simulation analysis to evaluate the impact of crop mapping error on crop area estimation from stratified sampling     

《International Journal of Digital Earth》2013,6(9):1046-1066

ABSTRACT

To analyze the efficiency of area estimations (i.e. estimation accuracy and variation of estimation) impacted by crop mapping error, we simulated error at eight levels for thematic maps using a stratified sampling estimation methodology. The results show that the estimation efficiency is influenced by the combination of the sample size and the error level. Evaluating the trade-offs between sample size and error level showed that reducing the crop mapping error level provides the most benefit (i.e. higher estimation efficiency). Further, sampling performance differed based on the heterogeneity of the crop area. The results demonstrated that the influence of increasing the error level on estimation efficiency is more detrimental in heterogeneous areas than in homogeneous ones. Therefore, to obtain higher estimation efficiency, a larger sample size and lower error level or both are needed, especially in heterogeneous areas. We suggest that existing land-cover maps should first be used to determine the heterogeneity of the area. The appropriate sample size for these areas then can be determined according to all three factors: heterogeneity, expected estimation efficiency, and sampling budget. Overall, extending our understanding of the impacts of crop mapping error is necessary for decision making to improve our ability to effectively estimate crop area.  相似文献   

The assimilation of spectral sensing and the WOFOST model for the dynamic simulation of cadmium accumulation in rice tissues     

《International Journal of Applied Earth Observation and Geoinformation》2013

The accurate detection of heavy metal-induced stress on crop growth is important for food security and agricultural, ecological and environmental protection. Spectral sensing offers an efficient and undamaged observation tool to monitor soil and vegetation contamination. This study proposed a methodology for dynamically estimating the total cadmium (Cd) accumulation in rice tissues by assimilating spectral information into WOFOST (World Food Study) model. Based on the differences among ground hyperspectral data of rice in three experiments fields under different Cd concentration levels, the spectral indices MCARI1, NREP and RH were selected to reflect the rice stress condition and dry matter production of rice. With assimilating these sensitive spectral indices into the WOFOST + PROSPECT + SAIL model to optimize the Cd pollution stress factor f_wi, the dynamic dry matter production processes of rice were adjusted. Based on the relation between dry matter production and Cd accumulation, we dynamically simulating the Cd accumulation in rice tissues. The results showed that the method performed well in dynamically estimating the total amount of Cd accumulation in rice tissues with R² over 85%. This study suggests that the proposed method of integrating the spectral information and the crop growth model could successfully dynamically simulate the Cd accumulation in rice tissues.  相似文献   

Estimating crop net primary production using national inventory data and MODIS-derived parameters     

《ISPRS Journal of Photogrammetry and Remote Sensing》2013

National estimates of spatially-resolved cropland net primary production (NPP) are needed for diagnostic and prognostic modeling of carbon sources, sinks, and net carbon flux between land and atmosphere. Cropland NPP estimates that correspond with existing cropland cover maps are needed to drive biogeochemical models at the local scale as well as national and continental scales. Existing satellite-based NPP products tend to underestimate NPP on croplands. An Agricultural Inventory-based Light Use Efficiency (AgI-LUE) framework was developed to estimate individual crop biophysical parameters for use in estimating crop-specific NPP over large multi-state regions. The method is documented here and evaluated for corn (Zea mays L.) and soybean (Glycine max L. Merr.) in Iowa and Illinois in 2006 and 2007. The method includes a crop-specific Enhanced Vegetation Index (EVI), shortwave radiation data estimated using the Mountain Climate Simulator (MTCLIM) algorithm, and crop-specific LUE per county. The combined aforementioned variables were used to generate spatially-resolved, crop-specific NPP that corresponds to the Cropland Data Layer (CDL) land cover product. Results from the modeling framework captured the spatial NPP gradient across croplands of Iowa and Illinois, and also represented the difference in NPP between years 2006 and 2007. Average corn and soybean NPP from AgI-LUE was 917 g C m⁻² yr⁻¹ and 409 g C m⁻² yr⁻¹, respectively. This was 2.4 and 1.1 times higher, respectively, for corn and soybean compared to the MOD17A3 NPP product. Site comparisons with flux tower data show AgI-LUE NPP in close agreement with tower-derived NPP, lower than inventory-based NPP, and higher than MOD17A3 NPP. The combination of new inputs and improved datasets enabled the development of spatially explicit and reliable NPP estimates for individual crops over large regional extents.  相似文献   

Efficient collection of training data for sub-pixel land cover classification using neural networks     

Stien Heremans  Bert Bossyns  Herman Eerens  Jos Van Orshoven 《International Journal of Applied Earth Observation and Geoinformation》2011

Artificial neural networks (ANNs) are a popular class of techniques for performing soft classifications of satellite images. They have successfully been applied for estimating crop areas through sub-pixel classification of medium to low resolution images. Before a network can be used for classification and estimation, however, it has to be trained. The collection of the reference area fractions needed to train an ANN is often both time-consuming and expensive. This study focuses on strategies for decreasing the efforts needed to collect the necessary reference data, without compromising the accuracy of the resulting area estimates. Two aspects were studied: the spatial sampling scheme (i) and the possibility for reusing trained networks in multiple consecutive seasons (ii). Belgium was chosen as the study area because of the vast amount of reference data available. Time series of monthly NDVI composites for both SPOT-VGT and MODIS were used as the network inputs. The results showed that accurate regional crop area estimation (R² > 80%) is possible using only 1% of the entire area for network training, provided that the training samples used are representative for the land use variability present in the study area. Limiting the training samples to a specific subset of the population, either geographically or thematically, significantly decreased the accuracy of the estimates. The results also indicate that the use of ANNs trained with data from one season to estimate area fractions in another season is not to be recommended. The interannual variability observed in the endmembers’ spectral signatures underlines the importance of using up-to-date training samples. It can thus be concluded that the representativeness of the training samples, both regarding the spatial and the temporal aspects, is an important issue in crop area estimation using ANNs that should not easily be ignored.  相似文献   

Simulation Study of the Climate Change Impact on the Rice and its Adaptability in Ningxia Province     

Wang Lianxi  Liu Jing  Li Qi  Qian Rui 《地球科学进展》2013,28(11):1248-1256

Yinhuang Irrigation District in Ningxia, as the top rice production area of high quality and quantity, has a long history in rice planting. The studies of the effective measures for the rice production replying the climate change were very important for reducing the harm of the future climate change and crop supply safety in Ningxia Province. Based on the coupling of the PRECIS model and the crop model CERES Rice, the effects of climate change on the rice production and growth stage in Yinhuang Irrigation District in Ningxia Province were simulated and evaluated, and the adaptability measures of rice production were studied. The results showed that the CERES Rice model had the preferable simulation capability, and the modified PRECIS model also could preferably simulate the required climate parameter. The crop model simulation results showed that the climate change had some influence on the rice production and growth stage in Yinhuang Irrigation District. The rice production goes down under future climate change scenarios in Ningxia Province. The trend of reduction of 2050s is more apparent than that of 2020s under the same scenarios,but the spatial change trend is similar. The extent and range of reduction of A2 scenario are wider than that of B2 scenario in the same period, but spatial change trend is different. For the change of growth stage, there has no obvious change in the north and the central part of the Yinhuang Irrigation District. The duration in 2050s shortens more obviously than that of 2020s under the same scenario, and the duration under B2 scenario shortens more obviously than that under A2 scenario in the same period. The results of adjusting the sowing date and the rice variety parameter G4 showed that the negative impact of climate change on the rice production can be reduced by sowing date advance in Yinhuang Irrigation District in Ningxia Province. There has obvious difference for the optimal G4 values of different region in Yinhuang Irrigation District, and the rice production can also be effectively upraised by adjusting the rice variety characteristic and cultivating the heat resistant rice varieties. The optimal G4 values can mitigate the damage of climate change on the rice production in Yinhuang Irrigation District in Ningxia Province.  相似文献   

作物产量与土壤环境的关系   总被引：11，自引：1，他引：10  

曾昭华 《湖南地质》2000,19(1):25-29

作物的产量与土壤元素中Ｎ、Ｐ、Ｋ、Ｎａ、Ｃａ、Ｍｇ、Ｓ、Ｆｅ、Ｍｎ、Ｃｕ、Ｚｎ、Ｂ、Ｍｏ、Ｖ、Ｃｏ、Ｎｉ、Ｃｒ、Ｐｂ、Ｃｄ、Ｈｇ、Ｓｅ、Ｆ、ＴＩ、Ｂａ、Ｔｅ、Ｔａ、Ｓｒ、Ｔｉ、Ｓｉ等元素及稀土、有机质、酸碱度和含水量、含盐量密切有关。  相似文献   

Crop discrimination in Northern China with double cropping systems using Fourier analysis of time-series MODIS data   总被引：4，自引：0，他引：4  

Zhang Mingwei  Zhou Qingbo  Chen Zhongxin  Liu Jia  Zhou Yong  Cai Chongfa 《International Journal of Applied Earth Observation and Geoinformation》2008,10(4):476

Crop identification is the basis of crop monitoring using remote sensing. Remote sensing the extent and distribution of individual crop types has proven useful to a wide range of users, including policy-makers, farmers, and scientists. Northern China is not merely the political, economic, and cultural centre of China, but also an important base for grain production. Its main grains are wheat, maize, and cotton. By employing the Fourier analysis method, we studied crop planting patterns in the Northern China plain. Then, using time-series EOS-MODIS NDVI data, we extracted the key parameters to discriminate crop types. The results showed that the estimated area and the statistics were correlated well at the county-level. Furthermore, there was little difference between the crop area estimated by the MODIS data and the statistics at province-level. Our study shows that the method we designed is promising for use in regional spatial scale crop mapping in Northern China using the MODIS NDVI time-series.  相似文献   

Evaluation and use of Resourcesat-I data for agricultural applications     

M.P. Oza  M.R. Pandya  D.R. Rajak 《International Journal of Applied Earth Observation and Geoinformation》2008,10(2):194-1

Resourcesat-1 satellite offers a unique opportunity of simultaneous observations at three different spatial scales through LISS-IV, LISS-III^* (improved LISS-III) and AWiFS sensors from a common platform. The sensors have enhanced capabilities in terms of spectral, spatial and radiometric resolution as compared to earlier Indian Remote sensing Satellite sensors. This paper summarizes the results of various studies such as evaluation of sensor characteristics, inter-sensor comparison studies, derivation and validation of surface reflectance measurements, quantification of improvements due to Resourcesat-1 sensors, and their use for various agricultural applications. The studies presented in this paper demonstrate that suit of sensors onboard Resourcesat-1 satellite provides better prospects for several agricultural applications like crop identification, discrimination and crop inventory for some major Indian crops, than its predecessors on IRS satellites.  相似文献   

Multi-Data Approach for remote sensing-based regional crop rotation mapping: A case study for the Rur catchment,Germany     

《International Journal of Applied Earth Observation and Geoinformation》2017

Spatial land use information is one of the key input parameters for regional agro-ecosystem modeling. Furthermore, to assess the crop-specific management in a spatio-temporal context accurately, parcel-related crop rotation information is additionally needed. Such data is scarcely available for a regional scale, so that only modeled crop rotations can be incorporated instead. However, the spectrum of the occurring multiannual land use patterns on arable land remains unknown. Thus, this contribution focuses on the mapping of the actually practiced crop rotations in the Rur catchment, located in the western part of Germany. We addressed this by combining multitemporal multispectral remote sensing data, ancillary information and expert-knowledge on crop phenology in a GIS-based Multi-Data Approach (MDA). At first, a methodology for the enhanced differentiation of the major crop types on an annual basis was developed. Key aspects are (i) the usage of physical block data to separate arable land from other land use types, (ii) the classification of remote sensing scenes of specific time periods, which are most favorable for the differentiation of certain crop types, and (iii) the combination of the multitemporal classification results in a sequential analysis strategy. Annual crop maps of eight consecutive years (2008–2015) were combined to a crop sequence dataset to have a profound data basis for the mapping of crop rotations. In most years, the remote sensing data basis was highly fragmented. Nevertheless, our method enabled satisfying crop mapping results. As an example for the annual crop mapping workflow, the procedure and the result of 2015 are illustrated. For the generation of the crop sequence dataset, the eight annual crop maps were geometrically smoothened and integrated into a single vector data layer. The resulting dataset informs about the occurring crop sequence for individual areas on arable land, so that crop rotation schemes can be derived. The resulting dataset reveals that the spectrum of the practiced crop rotations is extremely heterogeneous and contains a large amount of crop sequences, which strongly diverge from model crop rotations. Consequently, the integration of remote sensing-based crop rotation data can considerably reduce uncertainties regarding the management in regional agro-ecosystem modeling. Finally, the developed methods and the results are discussed in detail.  相似文献   

植被指数-地面温度特征空间的生态学内涵及其应用   总被引：32，自引：2，他引：32  

江东  王乃斌  杨小唤  刘红辉 《地理科学进展》2001,20(2):146-152

植被指数与地面温度是描述土地覆盖特征的重要参数 ,对两种数据的综合分析 ,可以衍生出更丰富、更清晰的地表信息 ,有助于更加准确、有效地认知土地覆盖 /土地利用的时空变化规律。本文探讨了植被指数与地面温度构成的二维向量空间的物理意义与生态学内涵 ,以基于 NOAA AVHRR的时间序列数据为本底 ,分析了不同土地覆盖类型在该特征空间上的时序变化规律 ,并以黄淮海地区主要农作物冬小麦为例 ,研究了植被指数-地面温度指标与干旱、半干旱地区农作物产量之间的响应关系。  相似文献