Chinese Journal of Agrometeorology

Phenological Characteristics of Representative Woody Plants at Different Altitude Sites in Jinnan Region and Their Response to Climate Change

LV Ai-li，HUO Zhi-guo，YANG Jian-ying

2020, 41(02): 65-75. doi:10.3969/j.issn.1000-6362.2020.02.001

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Based on the phenological observations of six woody plant species in three stations in the period of 1983?2016, characteristics of phenology and temperature and their relationship were studied in different period. The results showed that: (1) both annual and seasonal temperature increased significantly during the study period. The warming trend was more obvious in the lower altitude areas such as Yudu, especially in Spring. Monthly temperature also increased in most areas except Xixian. Monthly temperature had increased more obvious in March, resulting increased yearly temperature. (2) Variations of phenological phase up to 1?2 months were observed in different areas and for different plants. The phenological stage happened earlier (later) in spring and ended later (earlier) in fall, leading to longer (shorter) growth stage, in Yudu and Anze (Xixian) with lower (higher) altitude. (3) The start of leaf-out had been occurring earlier in spring corresponding to higher yearly and spring average temperature, and the higher temperature a month before for all woody plant. End of leaf fall had been postponed in response to the higher yearly and fall temperature, and the temperature a month ago in Yudu and Anze but not in Xixian, where the plants were located in the highest land in three stations. The study showed the obverse variation of temperature change in three different stations. The responses of woody plants to climate change were different.

Turbulent Heat Exchange and Partitioning and Its Environmental Controls between the Atmosphere and an Alpine Potentilla Fruticosa Shrublands over the Qinghai- Tibetan Plateau

ZHANG Fa-wei, HAN Yun, LI Hong-qin, LI Ying-nian, CAO Guang-min, ZHOU Hua-kun

2020, 41(02): 76-85. doi:10.3969/j.issn.1000-6362.2020.02.002

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The turbulent heat flux plays important role in micro-climate environment and vegetation phenology but its temporal patterns and partitioning characteristics and associated environmental controls remain unclear in alpine shrublands, which is one of the most important vegetation types on the Qinghai-Tibetan Plateau. The continuous turbulent heat flux and routine environmental variables measured by the eddy covariance techniques were analyzed to quantify the exchange and partitioning of sensible heat flux and latent heat flux over an alpine Potentilla fruticosa shrublands on the northeastern Qinghai-Tibetan Plateau. The results showed that (1) the averaged diurnal variations of turbulent heat exchange both exhibited unimodal patterns, peaking at about 13：30 over the whole year-round period. The diurnal heat flux was dominated by sensible heat flux during non-growth season (November to next April) and the beginning and end of growth season (May and October), while by latent heat flux in mid-growth season from June to September; (2) The daily sensible heat flux exhibited a bimodal seasonal pattern, with the largest peak and the second peak appearing in mid-April and beginning October, respectively. The daily latent heat flux presented a unimodal seasonal pattern with a maximum in end July; (3) The diurnal and daily variations of turbulent heat flux were both mainly controlled by solar shortwave radiation; (4) Bowen ratio showed a U-shape seasonal change, while decoupling coefficient, evaporation ratio exhibited a bell-shape seasonal variation. These partitioning indices were controlled by soil temperature during non-growth season and enhanced vegetation index in growth season, respectively. These results revealed that the turbulent heat exchange was determined by solar radiation while the partitioning between sensible heat flux and latent heat flux was regulated by underlying surface temperature and plant coverage in the alpine shrublands.

Performance Test of Energy-saving Active Heat Storage Rear Wall in Solar Greenhouses in Qinghai

WU Zhao-xue，WANG Qiang，ZHANG Yong，ZOU Zhi-rong，YAN Lu-lu

2020, 41(02): 86-93. doi:10.3969/j.issn.1000-6362.2020.02.003

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Taking advantage of the rich sand and soil resources in the non-cultivated land area in northwestern China, an active heat storage solidified sand wall solar greenhouse (SW) was built in Haidong City, Qinghai Province, and a passive heat storage solidified sand wall solar greenhouse (CK) was used as control. The thermal performance of the two was analyzed by experimental comparison. The results showed that compared with CK, the maximum temperature and average temperature in SW decreased by 2.3℃ and 1.5℃ on sunny days, respectively. On a clear night, the minimum and average temperatures in SW were 2.3℃and 1.8℃higher than CK, respectively. During cloudy day and night, the average temperature in SW was 1.8℃ and 2.7℃ higher than CK, respectively. On sunny days, the thickness of the thermal storage layer of SW was 520?720mm, which was greater than 320?520mm of CK, and the temperature difference between the time when the insulation was uncovered and when it was closed gradually decreased along the thickness of the rear wall. On a cloudy day, the thickness of the heat storage layer in the SW wall was 320-520mm, and the thickness of the heat storage layer in the CK wall was 120?320mm. Compared with sunny days, the thickness of the heat storage layer on the cloudy day was reduced. It was shown that the active heat storage solidified sand wall solar greenhouse (SW) can effectively increase the heat storage of the wall and increase the night temperature.

Characteristics and Limiting Factors of Light-temperature Potential Productivity and Yield Gap of Spring Maize in Hunan Province

LIU Si-hua, LI Jing, HUANG Wan-hua, WANG Tian-ying, LI Min-hua

2020, 41(02): 94-101. doi:10.3969/j.issn.1000-6362.2020.02.004

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The stepwise correcting model was applied to calculate the light and temperature potentials of spring maize in Hunan Province from 1987 to 2016. Firstly, the daily light potential productivity was calculated, then the daily light-temperature potential productivity was obtained by correcting it using daily temperature effective coefficient. Finally, the daily values were accumulated to get the yearly light-temperature potential productivity. And the yield gap between actual yield and light-temperature potential productivity was calculated in the 71 major counties. The results showed that the effects of sunshine hours on light-temperature potential productivity and yield gap was limited. In the last ten-day of March, there were significantly negative correlations between accumulated sunshine hours and light-temperature potential productivities, yield gap, and the areas mainly distributed in Southwest and South Hunan. However, the accumulated temperature had wide effects on light-temperature potential productivity and yield gap, especially the limiting effects of high temperature. In the whole growth period, the last ten-day of March, and period from June to July, there were remarkable negative correlations between accumulated temperature and light-temperature potential productivity, yield gap in most areas of the province. In conclusion, affecting by climate change, the limiting effects of high temperature was strengthened, and the light-temperature potential productivity of spring maize was significantly reduced in Hunan Province. With the highest yield gap and the highest light-temperature potential productivity in west area of Hunan, there would be a large improvement space for spring maize yield.

Applicability of Drought Severity Index(DSI) in Remote Sensing Monitoring of Drought in Shandong Province

TONG De-ming, BAI Yun, ZHANG Sha, LIU Qi, YANG Jin-yun

2020, 41(02): 102-112. doi:10.3969/j.issn.1000-6362.2020.02.005

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It is important to select a suitable drought remote sensing monitoring index for timely and accurate assessment of the impact of drought on crop growth. In this paper, the vegetation index and evapotranspiration index were integrated to form the drought severity index (DSI), and the applicability of DSI was quantitatively evaluated in drought monitoring，in order to provide scientific basis for remote sensing dynamic monitoring of drought in Shandong province. In the process of quantitatively analysis of DSI applicability, the Pearson correlation analysis was carried out on the monthly scale DSI, Normalized Difference Drought Index(NDDI), Tempera-ture Vegetation Drought Index(TVDI) and Standard Precipitation Index (SPI), Relative Soil Moisture (RSM) in the typical drought period based on the long-term sequence of the SPI, respectively. The results showed that the correlation coefficients between SPI, RSM and DSI are about 0.40 and 0.30 respectively, which were higher than the correlation between SPI, RSM and NDDI, TVDI. In addition, the occurrence of typical drought events and the change process of drought were accurately described by the spatial-temporal distribution of DSI in Shandong province during the historical period. The meteorological drought and agricultural drought was reflected by DSI, which indicated the good applicability of DSI for remote sensing monitoring of drought in Shandong province.

Study on Potential Productivity of Rubber Model Based on Climate Data

LIU Shao-jun, TONG Jin-he, ZHANG Jing-hong, CHEN Xiao-min, LI Wei-guang

2020, 41(02): 113-120. doi:10.3969/j.issn.1000-6362.2020.02.006

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The production capacity of rubber trees in natural environment is mainly affected by climate factors besides its biological and soil characteristics. The fluctuation of rubber tree yield is closely related to the change of climate factors. Therefore, it is important to accurately and timely evaluate the influence of climatic conditions on rubber production. Based on the climate data and remote sensing data from the year of 2000 to 2015 in Chinese rubber planting areas, the conversion coefficients between potential productivity of rubber and actual productivity of rubber were established by the net primary productivity model of climatic vegetation model which reflecting potential productivity of rubber and the remote sensing CASA model which reflecting actual productivity of rubber. The potential productivity of rubber model based on climate data was established. The results showed that the potential productivity of rubber model based on climate data not only can objectively and quantitatively evaluate the dynamic change of rubber production capacity based on climatic data, but also can indirectly reflect the difference of the impact of climate factors on rubber production capacity in different regions. It would provide decision-making basis for rubber yield prediction, adaptation strategies and measures to climate change for Chinese rubber planting.

Method of Maize Lodging Recognition Based on HJ-1A/B CCD Data

WANG Jie, LIU Shi, LAN Yu-bin, CHEN Li-wen, GUO Yong-qing, WANG Ying

2020, 41(02): 121-128. doi:10.3969/j.issn.1000-6362.2020.02.007

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To quickly and effectively obtain crop lodging information, this study proposed a remote sensing method for monitoring maize lodging using HJ-1(Small Satellite Constellations for Environment and Disaster Monitoring and Forecasting) CCD data. This paper took one area in Gongzhuling, Jilin Province as an example, where large-scale maize lodging occurred in 2012, caused by Typhoon Bolaven. The spectral characteristics of lodging and normal maize were first analyzed and summarized before and after the Typhoon. The results showed that compared with the normal field, the canopy reflectance increased in chromatic and near-infrared bans, but the vegetable index decreased in the lodged field. Four vegetation indices and a principal component were calculated, which extracted from 4 bans spectral data set. Binary Logistic models of lodging and normal maize classification were constructed with these 5 varieties. The prediction accuracies of the classification models were measured by ground survey samples. The principal component model could get the optimal results of recognition, and the classification accuracy on the test set was 96.23%. The classification accuracies of NDVI model and RVI model followed, the classification accuracies were about 80%. Finally, the principal component model was applied to recognize maize lodging using the spectral image, and the results confirmed that the proposed modes can accurately predict the distribution of maize lodging. The proposed maize lodging recognition method based on binary Logistic, provided a theoretical basis for monitoring large-scale lodged maize filed using multispectral data.

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