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Zhao C.,Beijing Research Center for Information Technology in Agriculture | Zhao C.,Key Laboratory of Agri informatics
Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | Year: 2014

Agriculture is one of the most important and popular fields of remote sensing applications. The purpose of this paper is to review the advances of research and application in remote sensing for agriculture in the world. The review includes following six main aspects: cropland radiative transfer mechanism and remote sensing inversion of crop parameters, remote sensing classification and identification of crops, cropland nutrient and variable fertilization techniques, crop yield and quality perdition, agricultural disaster monitoring and forecasting, and spatial decision-making support system for agricultural remote sensing monitoring. Finally, the key directions needed more attention and technical breakthrough are figured out according to the current status and trends of agricultural remote sensing techniques. ©, 2014, Chinese Society of Agricultural Machinery. All right reserved. Source


Liu Z.,Chinese Academy of Agricultural Sciences | Liu Z.,Key Laboratory of Agri informatics | Liu Z.,Peking University | Wang Y.,Peking University | And 3 more authors.
Environmental Earth Sciences | Year: 2013

Urbanization has accelerated rapidly over the last century, which has caused surfaces in natural ecosystems to shift to impervious surfaces. As a result, urban watershed ecosystems show altered physical, chemical and ecological process. As an important part of watershed management, urbanization has become one of the key issues involved in the deterioration of water quality. Impervious surface area (ISA) has been recognized as a key indicator of the effects of non-point runoff and water quality within a particular watershed. Numerous case studies have been conducted to investigate the relationship between urbanization and water quality in different study areas. However, there is still a lack of understanding regarding quantitative analysis of the threshold between urbanization and water quality indicators. This study was conducted to improve the understanding of how to quantify a threshold between urbanization and water quality, taking the rapid urbanization zone of Shenzhen, China as a case study. To accomplish this, ISA was extracted from the Landsat™ image using a linear spectral mixture method to quantify the urbanization. The relationship between water quality indicators and ISA was then analyzed by nonlinear regression, and the threshold between ISA and the chemical indicators of water quality was investigated using the statistical segment approach method. The results indicate that the water quality indicators and ISA are significantly correlated, and that, with the exception of Zn, Pb, and CN, the water quality indicators had R2 values greater than 0. 45. Furthermore, with the exception of Zn, F-, Pb and oils, water quality indicators were found to have an ISA threshold of 36. 9-52. 9 %, indicating that it is important to control the ISA below 36. 9 % in urbanization watersheds to enable effective urban watershed management. © 2012 Springer-Verlag. Source


Sun L.,Key Laboratory of Agri informatics | Sun L.,Chinese Academy of Agricultural Sciences | Chen Z.,Key Laboratory of Agri informatics | Chen Z.,Chinese Academy of Agricultural Sciences
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2013

Accurate calculation of land surface evapotranspiration is meaningful for the rational utilization of water resources. Penman-Monteith (PM) theory is a classic method to calculate evapotranspiration (ET) of land surfaces. Although Mu et al. (2011) improved ET estimates in Mu et al (2007) by adding nighttime ET and wet canopy surface ET component, it is difficult to acquire the 1km resolution spatial distribution of daily minimal air temperature (Tmin) for a regional scale, which is used as input variable (25km resolution) in Mu et al. (2007, 2011)'s global ET algorithms. Yuan et al. (2010)'s ET algorithm is modified from Mu et al (2007), they set invariant model parameters across the various vegetation types and Tmin is not used, therefore it is suitable for regional application. Soil resistance is largely controlled by the soil moisture. However, directly monitoring large scale soil moisture is always a challenge for remote sensing. In this study, we developed an ET estimation algorithm by incorporating a soil moisture index (SMI) derived from the improved surface temperature-vegetation cover feature space, denoted as the PM-SMI algorithm. The PM-SMI algorithm was compared with the triangle ET algorithm and another Penman-Monteith based algorithm (PM-Yuan) that calculated soil evaporation using relative humidity. Three ET algorithms were compared and validated by Bowen Ratio measurements at 12 sites in the Southern Great Plain (SGP) that were mainly covered by grassland and cropland with low vegetation cover. For instantaneous latent heat flux, although R2 of PM-SMI was lower than that of triangle and PM-Yuan for some sites (EF2, EF4 and EF12), the RMSE and bias was the lowest across almost all the sites. PM-Yuan obviously underestimated LE with bias of -82.41 W/m2. Triangle overestimated LE with 48.2 W/m2. PM-SMI algorithm showed the best performance with RMSE, bias and R2 of 53.67, 6.83 and 0.86 respectively. For daily latent heat flux, the bias of triangle was lower than PM-SMI for some sites, however, the RMSE was greater and R2 was lower across almost all sites. Similarity, the R2 of the PM-Yuan algorithm was greater for some sites, however the RMSE and bias was greater across almost all sites. Overall, the PM-SMI performed the best, with the highest R2 (0.87) and the lowest RMSE (39.07 W/m2) and bias (-4.04 W/m2). The PM-Yuan algorithm significantly underestimated LE. The results showed that the PM-SMI algorithm performed the best among the three ET algorithms both on the instantaneous scale and the daily scale. PM-SMI is more reliable for estimation of ET over regional scale. Source


Wu S.,North University of China | Liu J.,Chinese Academy of Agricultural Sciences | Liu J.,Key Laboratory of Agri informatics | Yang P.,Chinese Academy of Agricultural Sciences | Yang P.,Key Laboratory of Agri informatics
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2013

Uncertainty is the most important factor which affects the quality of remote sensing image classification (RSIC), research on uncertainty in RSIC is a cutting-edge, hot topic in remote sensing application study. Study of RSIC gradually developed from simple qualitative and non-positioning research into specific quantitative and positioning research. At present, a RSIC uncertainty evaluation model based on pixel scale and independent of the classification method should be established. In recent years, some scholars began to use hybrid entropy model to evaluate uncertainty in RSIC. However, these studies did not focus on a particular area and find out a suitable entropy function. How to find out a suitable entropy function which better integrate both fuzziness and randomness and facilitate a wider range of entropy values has always been a difficult point of research. From the discussion above, this paper established a method for evaluating uncertainty in agricultural RSIC based on exponential hybrid entropy in parametric form (EHEP). In this study, firstly, the exponential hybrid entropy function was deduced in parametric form, and EHEP was obtained. EHEP is improvement of hybrid entropy which has the shortcoming of lacking adjustable parameters. After adjusting parameters, entropy function can better integrate fuzziness and randomness and facilitate a wider range of entropy values, so this function is suitable for evaluating RSIC uncertainty. Moreover, by the research on the relationship between the parameters and the entropy function surface, the paper ascertained parameters which are suitable for evaluating uncertainty in farming area RSIC. Secondly, EHEP was used to establish a RSIC uncertainty evaluation model based on pixel scale and independent of the classification method, in order to offer elicitation to simulation of the uncertainty transferred in space model, and to help fill a vacancy in uncertainty evaluation model based on pixel scale and independent of the classification method. Lastly, the EHEP model was used to test and verify in SPOT-5 image of Zhenlai County, Jilin Province. The results indicate that in EHEP when parameters are equivalent to 4 and 1, respectively, the function better integrates fuzziness with randomness, and increases entropy value range by 2.11 times compared with logarithmic hybrid entropy function. In addition, the EHEP model evaluates contribution of different pixels to the uncertainty based on pixel scale and independent of the classification method, and corrects deficiency of error matrix in evaluation of RSIC accuracy. Furthermore, it visually expresses the uncertainty, contributes to the overall mastery of RSIC uncertainty's value, distribution, spatial structure and trend, and locates the coordinates of the area where uncertainty exists. Therefore, the EHEP model can make more accurate expression of the uncertainty in agricultural RSIC with relatively complex objects based on pixel scale and independent of the classification method, effectively bolstering precision of crop planting area extraction and remote sensing-based regional yield estimation. Source


Li Z.,Key Laboratory of Agri informatics | Li Z.,Chinese Academy of Agricultural Sciences | Yang P.,Key Laboratory of Agri informatics | Yang P.,Chinese Academy of Agricultural Sciences | And 9 more authors.
Regional Environmental Change | Year: 2014

Investigating the temporal changes in crop phenology is essential for understanding crop response and adaption to climate change. Using observed climatic and maize phenological data from 53 agricultural meteorological stations in Northeast China between 1990 and 2012, this study analyzed the spatiotemporal changes in maize phenology, temperatures and their correlations in major maize-growing areas (latitudes 39-48°N) of Northeast China. During the investigation period, seedling and heading dates advanced significantly at 22 out of the 53 stations; maturity dates delayed significantly at 23 stations, and the growing period (GP, from seedling to maturity), the vegetative growing period (VGP, from seedling to heading) and the reproductive growing period (RGP, from heading to maturity) increased significantly at 30 % of the investigated stations. GP length was positively correlated with Tmean at 40 stations and significantly at 10 stations (P < 0.01). Both negative and positive correlations were found between VGP and Tmean, while RGP length was significantly and positively correlated with Tmean. The results indicated that agronomic factors contribute substantially to the shift in maize phenology and that most farmers had adopted longer season cultivars because the increase in temperature provided better conditions for maize germination, emergence and grain filling. The findings on the various changes to maize phenology can help climate change impact studies and will enable regional maize production to cope with ongoing climate change. © 2013 Springer-Verlag Berlin Heidelberg. Source

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