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Extreme weather has posed a great threat to the production safety of corn under global warming, such as seriously agrometeorological disasters. The sowing date can be adjusted in the crop growth period against the climate resources. In this study, a sowing date experiment was carried out in Wuqiao County, Hebei Province of China in 2017-2020. Five sowing dates were set as June 5, 10, 15, 20 and 25. Two varieties of summer maize, Zhengdan958 (ZD958) and Denghai605 (DH605), were selected in the northern region of Huang-Huai-Hai (Beijing, Tianjin, and Hebei Province). The experimental observation included the growth stage (emergence, elongation, big flare, tasseling, silking, flowering, and mature stage), and the yield of summer maize. The historical meteorological data was utilized at Wuqiao County and 16 meteorological stations. The suitable sowing date was determined for the high and stable yield of summer maize, according to the climate resources, the main agrometeorological disasters, and the yield during the growth period. The results were obtained as follows. 1) The growth stage of summer maize was gradually advanced, and the duration of the growth period was significantly shortened, with the delay of the sowing date, where the total active accumulated temperature and total radiation during the growth period decreased, while the precipitation at the flowering stage increased gradually. There was no significant difference in the active accumulated temperature between the years of the same sowing date, but the large differences in the solar radiation and precipitation, where the maximum inter-annual difference was 336 MJ/m2 and 255 mm, respectively. The precipitation distribution during the growth period of summer maize was depended mainly on the sowing date and year type. 2) The occurrence frequency of heat damage at the flowering stage was lower than that of the continuous rain, indicating an increasing trend in the recent decade. The occurrence frequency of the heat damage and continuous rain on June 15 was significantly lower than that on the other sowing dates (P<0.05), which were 0.8% and 23.3%, respectively. 3) The yield of summer maize was higher on the sowing date of June 10 and 15, where there was a significant quadratic correlation with the active accumulated temperature ≥15 ℃ after flowering (P<0.05). The ZD958 and DH605 reached the maximum yield, when the active accumulated temperatures ≥15 ℃ after flowering were around 1 550 ℃·d (the sowing date of June 10) and 1 480 ℃·d (the sowing date of June 15), respectively. Consequently, June 15 was the suitable sowing date for the summer maize in the study area. The finding can provide a strong reference for the sowing date adjustment of crops in response to climate change, particularly for the stable and high yield of summer maize. [ABSTRACT FROM AUTHOR]

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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.)

Near-infrared wave bands have double sensitivities on water bodies and crop growth. Using near-infrared wave band on rice identification has notable advantage, and it is a key wave band in crop identification. The study chose 5 counties of Yinchuan City, Ningxia Hui Autonomous Region as the study area and took the near-infrared (770-890 nm) data of GF-1/WFV (wide field view) images on May 18, June 16, July 30, and September 13, 2016 as its data sources. By using decision-tree classification method, the study achieved the rice identification of 7 temporal combinations, including 4 single temporal data in May, June, July, and September, as well as 3 multi-temporal data of May/June, May/July, and May/June/July/September, and made a comparison with supervision classification results of full wave bands (0.45-0.52, 0.52-0.59, 0.63-0.69, 0.77-0.89 μm) of corresponding GF-1/WFV data. The rice identification accuracies of near-infrared wave bands of single temporal data in May, June, July, and September were 83.63%, 57.40%, 75.82% and 62.61% respectively. Except that the accuracy of May data was 5.75% higher than the full wave bands, the identification accuracies of other temporal phases were lower than that of full wave bands. The highest accuracy deviation was in June, 30.23%, and the lowest accuracy deviation was in July, 1.58%. The deviations in May and September were 5.75% and 25.47% respectively. The rice identification accuracies under 3 multi-temporal near-infrared combinations of May/June, May/July, May/June/July/September were 83.76%, 93.93%, and 94.03% respectively. The accuracies of near-infrared combinations of May/July, and May/June/July/September were 8.58% and 0.73% higher than that of full wave band data results respectively, but the accuracy of May/June was 5.47% lower. Regardless of near-infrared data or full wave band data, the minimum value, average value and maximum value of single temporal data identification accuracies were 57.40%, 76.31% and 88.10% respectively, with the Kappa coefficients of 0.22, 0.44 and 0.64 respectively; the minimum value, average value and maximum value of multi-temporal data identification accuracies were 83.76%, 89.98% and 94.03% respectively, with the Kappa coefficients of 0.52, 0.68 and 0.77 respectively; all identification accuracies of single temporal data were lower than the identification accuracies of the multi-temporal data. If using single temporal data as the data source of rice remote sensing identification, the identification accuracy can reach 75.82% based on the near-infrared WFV data of rice in middle growth period of July, which is consistent with the full wave band data result of this time phase; to reach the accuracy above 88.10%, it is necessary to use full wave band WFV data in September. If taking multi-temporal data of WFV data of temporal combinations of 2 rice growth periods i.e. early period of May and middle period of July as the data source of rice remote sensing identification, the identification accuracy can reach 93.93%, which is close to the highest identification accuracy of multi-temporal data. The study result shows that, for the near-infrared wave band, and in the applications with requirement on the rice identification accuracy of about 75%-85%, the 2 single temporal infrared WFV data in early growth period of May and middle growth period of July can be taken as the data sources of remote sensing identification. To reach the identification accuracy above 86%, it is necessary to choose the combination of near-infrared WFV data in early growth period of May and middle growth period of July as the remote sensing data sources, so as to achieve rice area spatial distribution results with relatively high identification accuracy. After further correction with visual observation, it can be taken as the crop area remote sensing supervision results. [ABSTRACT FROM AUTHOR]

In order to objectively obtain Ningxia rice area spatial distribution information, to lay a technical foundation for regional crop remote sensing monitoring, and according to the principle of spectral consistency, this paper, by taking Ningxia Hui Autonomous Region as its study area, divided the study area into 6 ground types of rice, sparse forest and shrub, dry land/woodland, abandoned land, wetland/water bodies, and others. The period before July 10, 2016 was taken as the early stage of rice growth. The normalized difference vegetation index (NDVI), infrared reflectance (IR) and short waved index (SWI) were established by choosing the data of Operational Land Imager (OLI) carried by US LandSat-8 and using the images taken in the 6 periods of March 11th, April 12th, April 28th, May 30th, June 15th and July 1st. Based on the analysis of dynamic change of 3 indexes of NDVI, IR and SWI, especially on maximum NDVI, minimal IR, and minimal SWI, a decision tree was established, and the identification of rice types in the study area was conducted by using images between March 11 and July 1 of 2016. The basic processes of decision tree classification were: firstly the ground objects such as cities and towns and deserts were eliminated by using maximum NDVI from March to June; the sparse forest and shrubs were eliminated by using maximum NDVI from March to April; the dry land/woodlands were eliminated by using minimum IR from May to June; then wetland/water bodies were eliminated by using minimum IR from March to April; finally, the abandoned lands were eliminated by using minimum SWI from May to June. The remaining pixels were taken as rice. The accuracy verification was also conducted by using the remote sensing survey results of rice area background of this region. The total extraction error was only 4.22% with the Kappa coefficient of 0.83; the user's classification accuracy of rice spatial distribution was 85.11% with the mapping accuracy of 81.67%. Compared with the user's accuracy of maximum likelihood supervised classification algorithm on the rice area extraction of 76.98% and the mapping accuracy of 61.66% in this area, the method used in this paper showed an increase of 8.13 percentage points in the user's accuracy, and an increase of even 20.01 percentage points in the mapping accuracy. The result shows that, the method proposed here of establishment of decision classifying tree by using the satellite images of early stage OLI remote sensing time series of rice growth before July 10, and based on the analysis of changing pattern of time series of staple crops can accurately extract the staple crop planting area, and it is a potential method for regional crop area remote sensing monitoring operations. [ABSTRACT FROM AUTHOR]

For the high-resolution remote sensing imagery, the space scale has effect on the classification accuracy and efficiency. The UAV image can achieve very high spatial resolution, which has promoted the development of object-oriented classification, at the same time, it also takes some influence on high-resolution remote sensing imagery classification. Therefore, it’s necessary to select optimal scale for classification. In this paper, to resolve high-resolution remote sensing imagery classification problem, we used UAV aerial crop images in Zhuozhou, Hebei province, as a data source, and applied wavelet packet transform to the multi-scale quantitative analysis of classification characteristics that belong to high-resolution remote sensing imagery. Wavelet packet decomposition was applied to seven of the most popular crops images, and then considering multi-factors, the most optimal wavelet packet decomposition tree were selected. Then, we selected the high frequency leaf node of each level according to the optimal wavelet packet decomposition tree and built the texture characteristic vector by four kinds of textural information including mean, variance, energy and energy difference which were computed statistically by the wavelet packet coefficient of selected node. Spectral information was obtained from the low frequency part. The classification characteristic vector was built by integrating the texture characteristic vector and the spectral information. To analyze the separability of the crop sample in different levels of wavelet packet decomposition tree, we needed to calculate the J-M distance of the classification characteristic vector between samples in different levels by matching the wavelet packet levels and the resolution, and then acquiring the optimal spatial scales for object-oriented classification. To verify the result, we conducted object-oriented classification experiment based on wavelet packet transform on imagery of different resolution, and chose accuracy of object-oriented classification and time-consuming of division as evaluation criterion to evaluate the result. The original images were decomposed to five levels, from which wavelet packet transform method was used. The texture information and spectral information which can extract from the optimal wavelet packet decomposition tree were used to build classification characteristic diagram. Then we acquired resampling images of different resolutions which matched with the wavelet packet decomposition levels. Classification characteristic diagram as the thematic layers was used to classify the imagery. Finally we employed overall accuracy, Kappa and time-consuming to assess the suitable scale. The results showed that, 1) In the third and fourth levels of wavelet packet decomposition tree (the spatial resolution was 0.32-0.64 m), the J-M distance of different samples become maximum which meant the strongest separability; 2) The accuracy of object-oriented classification based on wavelet packet transform was the highest in overall accuracy (0.90 and 0.89) when the spatial resolution was 0.32-0.64 m, and also saved a lot of time than the higher resolution (0.16 m). We concluded that it was suitable for crop object-oriented classification in the third and fourth levels of wavelet packet decomposition (the spatial resolution is 0.32-0.64 m). The method used in this paper for selecting optimal spatial scale for crop classification in high-resolution remote sensing imagery base on wavelet packet transform can accurately select the spatial scale with optimal classification accuracy and the highest classification efficiency. To some extent, the classification accuracy was improved by the classification characteristic which extracted via the method of wavelet packet transform. This proposed method may help with the fine recognition of crops using high-resolution remote sensing images. [ABSTRACT FROM AUTHOR]

To study the impact of short infrared wave bands (1 100-2 500 nm) setting on crop classification accuracy, and to provide data and application support for designed new types of sensors, in the study, we took north part of Bei'an city, Heilongjiang province as the study area, and studied the impact of the short infrared wave bands of 1 560-1 660 nm (SWIR 1) and 2 100-2 300 nm (SWIR 1) on identification accuracy of two crops of corn (Zea may L) and soybean (Glycine max) by selecting OLI (Operational Land Imager) data carried by US LandSat-8. Based on the mono temporal and multi-temporal conditions, and on the basis of five wave bands of coastal blue (433-453 nm), blue (450-515 nm), green (525-600 nm), red (630-680 nm), and near-infrared (845-885 nm), with five wave band combinations without short infrared wave band were used. One additional short infrared wave band (SWIR 1) was involved, and two additional short infrared wave band (SWIR 1and SWIR 2) were also used, with a total of 6 alternative schemes. In the study, we analyzed the remote sensing identification ability of short infrared on two crops: corns and soybeans, based on the maximum likelihood supervised classification method. Mono temporal OLI image Data of August 7, 2014 were taken for the study and five multi temporal image data of June 13, June 29, July 15, August 7, and September 17 of 2014 were also taken. The 5 km × 5 km grids of the covered study area were taken as the study units and under the principle of equal probability of corn and soybean area ratio, and 21 ground samples were chosen as training samples. used A visual interpretation method was used for identification of crop within ground samples. Accuracy verification data were from the background investigation results of crop areas in the study area. These data were obtained by using visual correction based on Rapideye image automatic classification with a spatial resolution of 5 m. The result showed that under the condition of mono temporal image classification, introduction of short infrared wave band can greatly improve the classification capacity of corn and soybean. Under the condition of introducing one short infrared wave band, the overall classification accuracy has been improved from 87.0% to 90.8%, up by 3.8%, Kappa coefficient was improved from 0.74 to 0.82, with a significant decrease of "Pepper salt problem", The user's accuracy of corn classification has been improved from original 85.4% to 91.5%, up by 6.1%. Mapping accuracy was improved from 89.6% to 90.3%. The mapping accuracy of soybean has been improved from original 84.5% to 91.5%, up by 7.0%, with user's accuracy improved from 88.9% to 90.2%. The separation degree of corn and soybean has been improved from 1.53 to 1.93, indicating that the short wave infrared wave band can remarkably improve the separation capacity of corn and soybean. Under the condition of multi-temporal image classification, the improvement of identification capacity of corn and soybean by introducing of short infrared wave band was limited. Under the condition of introducing one short infrared wave band, the overall classification accuracy was improved from 92.4% to 92.9%, only up by 0.5%, indicating that multi-temporal data, to some extent, can replace short infrared wave band on improving crop identification effect. Regardless of mono temporal or multi-temporal conditions, introduction of two short infrared wave bands did not show any significant changes on overall identification capacity compared with one short infrared wave band. Both correlations of five images on two short infrared wave bands exceeded 0.96, indicating that introduction of redundant bands with strong correlation has limited effect in improving crop area identification accuracy. The results have further quantified the separating capacity of short infrared wave bands on two crops of corns and soybean, and provided basis for wave band setting of domestic satellite short-wave sensors. [ABSTRACT FROM AUTHOR]

Adding rededge waveband (690-730 nm) based on traditional visible light and infrared band is a notable trend of the development of current high resolution satellite sensors.RapidEye satellite carries red-edge waveband sensors.Based on the RapidEye satellite remote sensing data of Dongsheng town, Bei'an city, Helongjiang province on July 27, 2014, and by employing the monitoring classification method, this paper comparatively analyzed the impact of the red-edge waveband on the estimation accuracy of crop planting area under 2 kinds of waveband combinations.The indices including the separable measure, the classification accuracy and the degree of landscape fragmentation of the 3 kinds of ground objects i.e.corn, soybean and the others were computed under the conditions of 2 types of band combinations, which were with or without the involvement of rededge waveband.Training samples of monitoring classification took the 2 km × 2 km grids covering study area as their basic units. Under the principle of equal probability of corn and soybean area proportion, 10 grids were chosen as training samples.Identification of crop within ground samples employed visual interpretation method. Accuracy verification adopted the background survey results of the crop area covering the study area. Based on the preliminary classification of RapidEye data with spatial resolution of 5 m, the background survey was conducted by using visual observation correction method combined with ground survey according to the seasonal changing rule of multitemporal Landsat-8/OLI (Operational Land Imager) data.The result showed that, the overall identification accuracy of 3 types of ground objects (corn, soybean and the others) with rededge was 88.4%, the Kappa coefficient was 0.81, and the mapping accuracies of 3 types of ground objects were 93.1%, 86.0% and 87.3% respectively; the overall identification accuracy of 3 types of ground objects without rededge was 81.7%, the Kappa coefficient was 0.71, and the mapping accuracies of 3 types of ground objects were 83.9%, 73.4% and 84.6% respectively.By introducing rededge band, the overall identification accuracy of 3 ground objects was improved by 6.7%, and the identification accuracies of 3 types of ground objects were improved by 9.20%, 12.6% and 2.7% respectively.By employing JeffriesMatusita method, we calculated the degree of separation of 3 types of ground objects.The degrees of separation between corns and soybeans, corns and the others, as well as soybeans and the others were improved from 0.84 to 1.73, from 1.37 to 1.81, and from 1.27 to 1.29 respectively; by employing fragmentation index, we calculated the landscape fragmentation.The number of land parcels was reduced by 69.2%, with the average parcel area increased by 2.2 times, the average parcel perimeter increased by 60.5%, and the ratio between parcel area and perimeter increased by 1.0 time. So, by introducing rededge band, the study has improved the separable measure of different ground objects, reduced the increase of landscape fragment caused by "Pepper salt" effect, and improved the overall identification accuracy of crop planting area.Currently, more and more satellites carry rededge devices, and the domesticallyproduced satellites to be launched also plan to add rededge band so as to improve crop identification capacity.The result of this paper will provide a reference for the application of domesticallyproduced rededge satellite data in agriculture. [ABSTRACT FROM AUTHOR]