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Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Remote Sensing for Natural Resources is the property of Remote Sensing for Natural Resources Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Soil organic matter (SOM) is an important index to evaluate soil quality. The monitoring of SOM content and its spatial distribution is of great significance to soil utilization, soil conservation, estimation of soil organic carbon pool, and so on. It is difficult to estimate the reserves of soil organic carbon pool by using traditional methods. Early studies showed that there was a significant negative correlation between SOM and soil spectral reflectance, and there was a quantitative relationship between SOM and soil organic carbon, so we can estimate the SOM content by remote sensing and it will provide help to estimate the soil organic carbon pool. In this paper, 4 main soil types (black soil, chernozem soil, meadow soil and aeolian sand) were collected as sampling points in the typical area of Songnen Plain to construct prediction model for estimating the SOM content in the study area. The number of the soil sampling points was 147. Half of the sampling points (74 samples) were used to serve as calibration set and other sampling points (73 samples) were used to serve as validation set. The remote sensing inversion model was established to reveal the spatial distribution of SOM in the study area. To improve the accuracy and stability of inversion model and find the optimal model, spectral indices based on single MODIS image or multi MODIS images during the bare soil period, which could contain temporal information of the variation of soil moisture content and SOM content, were introduced into the multiple linear regression model. The results showed that the 8-day data of surface reflectance (MOD09A1 and MYD09A1) could be used to estimate the SOM content of different types of soils in Songnen Pain, Northeast China. The primary input variable of the model changed regularly, because the bare soil condition changed with the variation of date. Furthermore, because the condition of bare soil was the optimal, the prediction model based on the MODIS image on the 137th day in the year was the best among the models based on single MODIS image. The ratio of spectral index R61 (the ratio of Band 6 to Band 1) is significantly related to SOM, but it has little correlation with soil moisture, so it can eliminate the influence of soil moisture to a certain degree. R61 is suitable as a primary input variable for the inversion model to estimate SOM by using remote sensing method. The accuracy and stability of models based on multi MODIS images were generally better than the models based on single MODIS image. The model based on multi MODIS images on the 137th and 105th day in the year is the best among all models, and its R2 is 0.68, and its RMSE (root mean square error) is 0.84 for calibration set and 0.84 for validation set. The SOM content in Songnen Plain showed a decreasing trend from northeast to southwest. This study provides a rapid and nondestructive method to estimate SOM content in a large scale. Moreover, the results of remote sensing inversion give supports for soil degradation assessment, land use, and estimation of soil carbon pool. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to accurately estimate the crop aboveground biomass at the field scale, improving the accuracy and stability of soybean aboveground biomass inversion model, this paper obtained SPOT-6 6-meter multi-spectral data from July and August of the study area as well as the soybean aboveground biomass of different terrain slope. At the same time, the terrain data of the study area were measured and the topographic factors such as elevation, slope and aspect were extracted. Using the above measured data, intended to build three models, which were the traditional linear regression model, the multiple regression model and the neural network model. At first, the correlation relationships among enhances vegetation index (EVI), normalized difference vegetation index (NDVI) and observed date of soybean aboveground biomass were analyzed by linear regression model. Then added the terrain factors related to the aboveground biomass, establishing multilayer perception stepwise multiple regression and neural network inversion model. Through the model accuracy comparison and estimation accuracy analysis, The results are following 1) In the linear regression model established by the two vegetation indices, the NDVI Model fit degree is high, and the coefficient of determination (R2) reaches 0.712, root mean square error (RMSE) of 0.116. The results can be explained that the use of traditional single vegetation index model to predict soybean aboveground biomass is feasible, but the model accuracy and the stability is not high. 2) After adding the topographic factors such as elevation, slope, aspect and so on, the neural network multilayer sensor model was established. This model has high accuracy and reliability. The results show that R² reaches 0.904 and RMSE is 0.047. The results of model validation show that the average absolute error and the average relative error of the total aboveground biomass of the whole verification area using the neural network model are the smallest, the values are 0.113 kg/m² and 0.212, respectively. In the three types of inversion models, the inversion results of the neural network model are closest to the actual situation of crop aboveground biomass distribution. The inversion results of this study are in good agreement with the terrain, topography, temperature and precipitation characteristics of the plot. Accurately reflects the space distribution features of crop condition and the spatial distribution of crop growth. The results of this study provide a scientific basis for the dynamic monitoring and precise management of soybean aboveground biomass at the field scale. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)