中国农业科学 ›› 2022, Vol. 55 ›› Issue (16): 3093-3109.doi: 10.3864/j.issn.0578-1752.2022.16.003
杨靖雅1(),胡琼2,魏浩东1,蔡志文1,张馨予1,宋茜3(
),徐保东1(
)
收稿日期:
2021-10-28
接受日期:
2022-01-28
出版日期:
2022-08-16
发布日期:
2022-08-11
通讯作者:
宋茜,徐保东
作者简介:
杨靖雅,E-mail: 基金资助:
YANG JingYa1(),HU Qiong2,WEI HaoDong1,CAI ZhiWen1,ZHANG XinYu1,SONG Qian3(
),XU BaoDong1(
)
Received:
2021-10-28
Accepted:
2022-01-28
Online:
2022-08-16
Published:
2022-08-11
Contact:
Qian SONG,BaoDong XU
摘要:
【目的】微波遥感因具有全天时、全天候数据获取的特点,在多云雨的中国南方水稻识别研究中表现出巨大潜力。本研究通过对比Sentinel-1SAR遥感数据和Sentinel-2光学遥感数据用于水稻遥感制图的效果,分析光学和SAR遥感数据对于单双季稻识别结果的一致性,并探索水稻识别的最优SAR影像特征。【方法】本研究使用Sentinel-1/2卫星数据,基于面向对象的随机森林分类算法和Google Earth Engine平台,提取洞庭湖平原4个典型水稻种植区的单双季稻空间分布。通过比较9种不同传感器和特征组合场景的分类精度和分类结果统计指标,并计算NDVI和SAR特征时序(VH、VV、VH/VV)的R2和DTW距离,分析识别单双季稻的最优SAR特征,评估光学和SAR遥感数据对于单双季稻识别结果的一致性。【结果】VH、VV和VH/VV时序识别单双季的总体精度分别为90.42%、82.08%和88.33%,而联合VH和VH/VV时序识别单双季稻的总体精度可达91.67%。VH(VH/VV、VV)时序与单双季稻NDVI时序的R2和DTW距离分别为0.870(0.915、0.986)、4.715(1.896、5.506)(单季稻)和0.597(0.783、0.673)、2.396(1.839、3.441)(双季稻)。较高的R2和较低的DTW距离说明单双季稻的VH/VV时序与NDVI时序相关度更高,可以较好地反映单双季稻的生长周期规律。同时,VH可以较好地反映单双季稻移栽期的淹水特征。基于光学数据和SAR数据在6个时间窗口的特征(S-2:NDVI、EVI、LSWI;S-1:VH、VH/VV)识别单双季稻的总体精度分别为91.25%和90.00%,识别结果面积相关性可达95.70%。【结论】SAR遥感数据与光学遥感数据水稻识别结果一致性较高。应用Sentinel-1在多云雨区识别单双季稻具有巨大潜力,VH和VH/VV后向散射系数时序是识别水稻的优质特征。研究结果为多云多雨区使用SAR数据进行特征优选以高精度识别单双季稻提供了重要技术支撑。
杨靖雅,胡琼,魏浩东,蔡志文,张馨予,宋茜,徐保东. 基于Sentinel-1/2数据的中国南方单双季稻识别结果一致性分析[J]. 中国农业科学, 2022, 55(16): 3093-3109.
YANG JingYa,HU Qiong,WEI HaoDong,CAI ZhiWen,ZHANG XinYu,SONG Qian,XU BaoDong. Consistency Analysis of Classification Results for Single and Double Cropping Rice in Southern China Based on Sentinel-1/2 Imagery[J]. Scientia Agricultura Sinica, 2022, 55(16): 3093-3109.
表1
不同SAR特征组合的水稻分类场景设置"
场景 Scenarios | 特征组合 Combinations of features | 时间窗口 Time window |
---|---|---|
场景1 Scenario 1 | S-1 VH | 04-11-11-17 20 d中值合成时间序列 Time series of 20-d median composition from 04-11 to 11-17 |
场景2 Scenario 2 | S-1 VV | |
场景3 Scenario 3 | S-1 VH/VV | |
场景4 Scenario 4 | S-1 VH, VV | |
场景5 Scenario 5 | S-1 VH, VH/VV | |
场景6 Scenario 6 | S-1 VV, VH/VV | |
场景7 Scenario 7 | S-1 VV, VH, VH/VV |
表2
不同传感器特征组合的水稻分类场景设置"
场景 Scenarios | 特征组合 Combinations of features | 时间窗口 Time window |
---|---|---|
场景8 Scenario 8 | S-2 NDVI, EVI, LSWI | 03-22-05-21、05-21-06-10、07-20-08-09、08-09-08-29、09-18-10-08、10-28-11-17时间窗口中值合成 Median composition in each time window |
场景9 Scenario 9 | S-1 VH, VH/VV | 04-11-05-01、05-21-06-10、07-20-08-09、08-09-08-29、09-18-10-08、10-28-11-17时间窗口中值合成 median composition in each time window |
表4
不同SAR特征组合场景的分类结果精度评价"
场景 Scenarios | 单季稻 Single rice | 双季稻 Double rice | 其他作物 Other crops | 总体精度 OA | ||||||
---|---|---|---|---|---|---|---|---|---|---|
PA | UA | F1 score | PA | UA | F1 score | PA | UA | F1 score | ||
场景1 Scenario 1 | 93.27% | 88.99% | 0.911 | 93.59% | 92.41% | 0.930 | 81.03% | 90.38% | 0.855 | 90.42% |
场景2 Scenario 2 | 85.58% | 78.76% | 0.820 | 84.62% | 83.54% | 0.841 | 72.41% | 87.5% | 0.792 | 82.08% |
场景3 Scenario 3 | 90.38% | 84.68% | 0.874 | 97.44% | 96.20% | 0.968 | 72.41% | 84% | 0.778 | 88.33% |
场景4 Scenario 4 | 93.27% | 89.81% | 0.915 | 94.87% | 92.50% | 0.937 | 81.03% | 90.38% | 0.855 | 90.83% |
场景5 Scenario 5 | 94.23% | 89.90% | 0.920 | 98.71% | 95.06% | 0.969 | 77.59% | 90% | 0.833 | 91.67% |
场景6 Scenario 6 | 94.23% | 89.90% | 0.920 | 98.71% | 95.06% | 0.969 | 77.59% | 90% | 0.833 | 91.67% |
场景7 Scenario 7 | 93.27% | 90.65% | 0.919 | 98.72% | 95.06% | 0.969 | 79.31% | 88.46% | 0.836 | 91.67% |
表5
不同传感器特征组合场景的分类结果精度评价"
场景 Scenarios | 单季稻 Single rice | 双季稻 Double rice | 其他作物 Other crops | 总体精度 OA | ||||||
---|---|---|---|---|---|---|---|---|---|---|
PA | UA | F1 score | PA | UA | F1 score | PA | UA | F1 score | ||
场景8 Scenario 8 | 90.39% | 90.39% | 0.904 | 100% | 95.12% | 0.975 | 81.03% | 87.03% | 0.839 | 91.25% |
场景9 Scenario 9 | 91.35% | 89.62% | 0.905 | 96.15% | 91.46% | 0.937 | 79.31% | 88.46% | 0.836 | 90.00% |
表6
基于光学和SAR遥感数据识别的单双季稻面积百分比(S,%)和面积偏差系数(D)"
类别 Types | S-2 | S-1 VH&VH/VV | ||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
A | B | C | D | All | A | B | C | D | All | |||||||||||
S | D | S | D | S | D | S | D | S | D | S | D | S | D | S | D | S | D | S | D | |
单季稻 Single rice | 24.78 | -11.35 | 47.21 | -5.55 | 18.76 | -15.52 | 21.43 | -8.56 | 28.74 | -9.03 | 31.14 | 11.35 | 52.76 | 5.55 | 25.65 | 15.52 | 25.44 | 8.56 | 34.44 | 9.03 |
双季稻 Double rice | 2.47 | -22.35 | 5.02 | -5.76 | 24.83 | 2.76 | 45.52 | 2.14 | 18.49 | 0.60 | 3.89 | 22.35 | 5.63 | 5.76 | 23.49 | -2.76 | 43.61 | -2.14 | 18.27 | -0.60 |
其他作物 Other crops | 72.75 | 5.64 | 47.77 | 6.89 | 56.41 | 5.18 | 33.05 | 3.29 | 52.77 | 5.48 | 64.97 | -5.64 | 41.61 | -6.89 | 50.86 | -5.18 | 30.95 | -3.29 | 47.29 | -5.48 |
[1] |
谭深, 吴炳方, 张鑫. 基于Google Earth Engine与多源遥感数据的海南水稻分类研究. 地球信息科学学报, 2019, 21(6): 937-947.
doi: 10.12082/dqxxkx.2019.180423. |
TAN S, WU B F, ZHANG X. Mapping paddy rice in the Hainan province using both Google Earth Engine and remote sensing images. Journal of Geo-information Science, 2019, 21(6): 937-947. (in Chinese)
doi: 10.12082/dqxxkx.2019.180423. |
|
[2] | FAOSTAT. Statistical database of the food and agricultural organization of the united nation, 2019. http://www.fao.org/faostat/en/#data. |
[3] |
JIANG M, XIN L J, LI X B, TAN M H, WANG R J. Decreasing rice cropping intensity in Southern China from 1990 to 2015. Remote Sensing, 2018, 11(1): 35.
doi: 10.3390/rs11010035 |
[4] |
DENG N Y, GRASSINI P, YANG H S, HUANG J L, CASSMAN K G, PENG S B. Closing yield gaps for rice self-sufficiency in China. Nature Communications, 2019, 10(1): 1-9.
doi: 10.1038/s41467-018-07882-8 |
[5] |
蒋敏, 李秀彬, 辛良杰, 谈明洪. 南方水稻复种指数变化对国家粮食产能的影响及其政策启示. 地理学报, 2019, 74(1): 32-43.
doi: 10.11821/dlxb201901003 |
JIANG M, LI X B, XIN L J, TAN M H. The impact of paddy rice multiple cropping index changes in Southern China on national grainproduction capacity and its policy implications. Acta Geographica Sinica, 2019, 74(1): 32-43. (in Chinese)
doi: 10.11821/dlxb201901003 |
|
[6] | 胡琼, 吴文斌, 宋茜, 余强毅, 杨鹏, 唐华俊. 农作物种植结构遥感提取研究进展. 中国农业科学, 2015, 48(10): 1900-1914. |
HU Q, WU W B, SONG Q, YU Q Y, YANG P, TANG H J. Recent progresses in research of crop patterns mapping by using remote sensing. Scientia Agricultura Sinica, 2015, 48(10): 1900-1914. (in Chinese) | |
[7] |
XIAO X M, BOLES S, LIU J Y, ZHUANG D F, FROLKING S, LI C S, SALAS W, MOOREⅢ B. Mapping paddy rice agriculture in southern China using multi-temporal MODIS images. Remote Sensing of Environment, 2005, 95(4): 480-492.
doi: 10.1016/j.rse.2004.12.009 |
[8] |
XIAO X M, BOLES S, FROLKING S, LI C S, BABU J Y, SALAS W, MOOREⅢ B. Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images. Remote Sensing of Environment, 2006, 100(1): 95-113.
doi: 10.1016/j.rse.2005.10.004 |
[9] |
DONG J W, XIAO X M, MENARGUEZ M A, ZHANG G L, QIN Y W, THAU D, BIRADAR C, MOOREⅢ B. Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology- based algorithm and Google Earth Engine. Remote Sensing of Environment, 2016, 185: 142-154.
doi: 10.1016/j.rse.2016.02.016 |
[10] |
SINGHA M, WU B F, ZHANG M. Object-based paddy rice mapping using HJ-1A/B data and temporal features extracted from time series MODIS NDVI data. Sensors, 2016, 17(1): 10.
doi: 10.3390/s17010010 |
[11] |
CHEN J, JöNSSON P, TAMURA M, GU Z H, MATSUSHITA B, EKLUNDH L. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter. Remote Sensing of Environment, 2004, 91(3/4): 332-344.
doi: 10.1016/j.rse.2004.03.014 |
[12] |
BOLOGNESI S F, PASOLLI E, BELFIORE O R, DE MICHELE C, D’URSO G. Harmonized Landsat 8 and Sentinel-2 time series data to detect irrigated areas: An application in Southern Italy. Remote Sensing, 2020, 12(8): 1275.
doi: 10.3390/rs12081275 |
[13] |
KONG D D, ZHANG Y Q, GU X H, WANG D G. A robust method for reconstructing global MODIS EVI time series on the Google Earth Engine. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 155: 13-24.
doi: 10.1016/j.isprsjprs.2019.06.014 |
[14] |
LI B L, TI C P, YAN X Y. Estimating rice paddy areas in China using multi-temporal cloud-free normalized difference vegetation index (NDVI) imagery based on change detection. Pedosphere, 2020, 30(6): 734-746.
doi: 10.1016/S1002-0160(17)60405-3 |
[15] |
QIN Y W, XIAO X M, DONG J W, ZHOU Y T, ZHU Z, ZHANG G L, DU G M, JIN C, KOU W L, WANG J, LI X P. Mapping paddy rice planting area in cold temperate climate region through analysis of time series Landsat 8 (OLI), Landsat 7 (ETM+) and MODIS imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 105: 220-233.
doi: 10.1016/j.isprsjprs.2015.04.008 |
[16] |
LIU L, XIAO X M, QIN Y W, WANG J, XU X L, HU Y M, QIAO Z. Mapping cropping intensity in China using time series Landsat and Sentinel-2 images and Google Earth Engine. Remote Sensing of Environment, 2020, 239: 111624.
doi: 10.1016/j.rse.2019.111624 |
[17] |
WANG J, XIAO X M, LIU L, WU X C, QIN Y W, STEINER J L, DONG J W. Mapping sugarcane plantation dynamics in Guangxi, China, by time series Sentinel-1, Sentinel-2 and Landsat images. Remote Sensing of Environment, 2020, 247: 111951.
doi: 10.1016/j.rse.2020.111951 |
[18] |
SONG P L, MANSARAY L R, HUANG J F, HUANG W J. Mapping paddy rice agriculture over China using AMSR-E time series data. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 144: 469-482.
doi: 10.1016/j.isprsjprs.2018.08.015 |
[19] |
CLAUSS K, OTTINGER M, LEINENKUGEL P, KUENZER C. Estimating rice production in the Mekong Delta, Vietnam, utilizing time series of Sentinel-1 SAR data. International Journal of Applied Earth Observation and Geoinformation, 2018, 73: 574-585.
doi: 10.1016/j.jag.2018.07.022 |
[20] |
MANSARAY L R, HUANG W J, ZHANG D D, HUANG J F, LI J. Mapping rice fields in urban Shanghai, Southeast China, using Sentinel-1A and Landsat 8 datasets. Remote Sensing, 2017, 9(3): 257.
doi: 10.3390/rs9030257 |
[21] |
SINGHA M, DONG J W, ZHANG G L, XIAO X M. High resolution paddy rice maps in cloud-prone Bangladesh and Northeast India using Sentinel-1 data. Scientific Data, 2019, 6: 26.
doi: 10.1038/s41597-019-0036-3 |
[22] |
HE Y L, DONG J W, LIAO X Y, SUN L, WANG Z P, YOU N S, LI Z C, FU P. Examining rice distribution and cropping intensity in a mixed single- and double-cropping region in South China using all available Sentinel 1/2 images. International Journal of Applied Earth Observation and Geoinformation, 2021, 101: 102351.
doi: 10.1016/j.jag.2021.102351 |
[23] |
ZHAN P, ZHU W Q, LI N. An automated rice mapping method based on flooding signals in synthetic aperture radar time series. Remote Sensing of Environment, 2021, 252: 112112.
doi: 10.1016/j.rse.2020.112112 |
[24] |
YOU N S, DONG J W. Examining earliest identifiable timing of crops using all available Sentinel 1/2 imagery and Google Earth Engine. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 161: 109-123.
doi: 10.1016/j.isprsjprs.2020.01.001 |
[25] |
YANG H J, PAN B, WU W F, TAI J H. Field-based rice classification in Wuhua county through integration of multi-temporal Sentinel-1A and Landsat-8 OLI data. International Journal of Applied Earth Observation and Geoinformation, 2018, 69: 226-236.
doi: 10.1016/j.jag.2018.02.019 |
[26] |
ZHANG X, WU B F, PONCE-CAMPOS G, ZHANG M, CHANG S, TIAN F Y. Mapping up-to-date paddy rice extent at 10 m resolution in China through the integration of optical and synthetic aperture radar images. Remote Sensing, 2018, 10(8): 1200.
doi: 10.3390/rs10081200 |
[27] |
LE TOAN T, RIBBES F, WANG L F, FLOURY N, DING K H, KONG J A, FUJITA M, KUROSU T. Rice crop mapping and monitoring using ERS-1 data based on experiment and modeling results. IEEE Transactions on Geoscience and Remote Sensing, 1997, 35(1): 41-56.
doi: 10.1109/36.551933 |
[28] |
BOUVET A, LE TOAN T, NGUYEN L D. Monitoring of the rice cropping system in the Mekong Delta using ENVISAT/ASAR dual polarization data. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(2): 517-526.
doi: 10.1109/TGRS.2008.2007963 |
[29] |
WU F, WANG C, ZHANG H, ZHANG B, TANG Y X. Rice crop monitoring in South China with RADARSAT-2 quad-polarization SAR data. IEEE Geoscience and Remote Sensing Letters, 2011, 8(2): 196-200.
doi: 10.1109/LGRS.2010.2055830 |
[30] | 赵英时. 遥感应用分析原理与方法. 北京: 科学出版社, 2013. |
ZHAO Y S. Principles and Methods of Remote Sensing Application Analysis. Beijing: Science Press, 2013. (in Chinese) | |
[31] |
GORELICK N, HANCHER M, DIXON M, ILYUSHCHENKO S, THAU D, MOORE R. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 2017, 202: 18-27.
doi: 10.1016/j.rse.2017.06.031 |
[32] | 中华人民共和国国家统计局. 中国统计年鉴2019. 北京: 中国统计出版社, 2019. |
National Bureau of statistics of the people's Republic of China. China Statistical Yearbook 2019. Beijing: China Statistics Press, 2019. (in Chinese) | |
[33] |
CHEN J, CHEN J, LIAO A P, CAO X, CHEN L J, CHEN X H, HE C Y, HAN G, PENG S, LU M, ZHANG W W, TONG X H, MILLS J. Global land cover mapping at 30 m resolution: A POK-based operational approach. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 103: 7-27.
doi: 10.1016/j.isprsjprs.2014.09.002 |
[34] |
VELOSO A, MERMOZ S, BOUVET A, LE TOAN T, PLANELLS M, DEJOUX J F, CESCHIA E. Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications. Remote Sensing of Environment, 2017, 199: 415-426.
doi: 10.1016/j.rse.2017.07.015 |
[35] |
BELGIU M, CSILLIK O. Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis. Remote Sensing of Environment, 2018, 204: 509-523.
doi: 10.1016/j.rse.2017.10.005 |
[36] |
DONG J, FU Y Y, WANG J J, TIAN H F, FU S, NIU Z, HAN W, ZHENG Y, HUANG J X, YUAN W Y. Early season mapping of winter wheat in China based on Landsat and Sentinel images. Earth System Science Data, 2020, 12(4): 3081-3095.
doi: 10.5194/essd-12-3081-2020 |
[37] | 黄翀, 许照鑫, 张晨晨, 李贺, 刘庆生, 杨振坤, 刘高焕. 基于Sentinel-1数据时序特征的热带地区水稻种植结构提取方法. 农业工程学报, 2020, 36(9): 177-184. |
HUANG C, XU Z X, ZHANG C C, LI H, LIU Q S, YANG Z K, LIU G H. Extraction of rice planting structure in tropical region based on Sentinel-1 temporal features integration. Transactions of the Chinese Society of Agricultural Engineering, 2020, 36(9): 177-184. (in Chinese) | |
[38] |
TASSI A, VIZZARI M. Object-oriented LULC classification in Google Earth Engine combining SNIC, GLCM, and machine learning algorithms. Remote Sensing, 2020, 12(22): 3776.
doi: 10.3390/rs12223776 |
[39] |
LUO C, QI B S, LIU H J, GUO D, LU L P, FU Q, SHAO Y Q. Using time series Sentinel-1 images for object-oriented crop classification in Google Earth Engine. Remote Sensing, 2021, 13(4): 561.
doi: 10.3390/rs13040561 |
[40] |
QU L A, CHEN Z J, LI M C, ZHI J J, WANG H M. Accuracy improvements to pixel-based and object-based LULC classification with auxiliary datasets from Google Earth Engine. Remote Sensing, 2021, 13(3): 453.
doi: 10.3390/rs13030453 |
[41] |
YANG L B, WANG L M, ABUBAKAR G A, HUANG J F. High-resolution rice mapping based on SNIC segmentation and multi-source remote sensing images. Remote Sensing, 2021, 13(6): 1148.
doi: 10.3390/rs13061148 |
[42] |
YOU N S, DONG J W, HUANG J X, DU G M, ZHANG G L, HE Y L, YANG T, DI Y Y, XIAO X M. The 10-m crop type maps in Northeast China during 2017-2019. Scientific Data, 2021, 8(1): 41.
doi: 10.1038/s41597-021-00827-9 |
[43] | 戴昭鑫, 胡云锋, 张千力. 多源卫星遥感土地覆被产品在南美洲的一致性分析. 遥感信息, 2017, 32(2): 137-148. |
DAI Z X, HU Y F, ZHANG Q L. Agreement analysis of multi- source land cover products derived from remote sensing in South America. Remote Sensing Information, 2017, 32(2): 137-148. (in Chinese) | |
[44] |
侯婉, 侯西勇. 全球海岸带多源土地利用/覆盖遥感分类产品一致性分析. 地球信息科学学报, 2019, 21(7): 1061-1073.
doi: 10.12082/dqxxkx.2019.180441 |
HOU W, HOU X Y. Consistency of the multiple remote sensing-based land use and land cover classification products in the global coastal zones. Journal of Geo-information Science, 2019, 21(7): 1061-1073. (in Chinese)
doi: 10.12082/dqxxkx.2019.180441 |
|
[45] |
BAI Y, FENG M, JIANG H, WANG J L, ZHU Y Q, LIU Y Z. Assessing consistency of five global land cover data sets in China. Remote Sensing, 2014, 6(9): 8739-8759.
doi: 10.3390/rs6098739 |
[46] | 朱江涛, 艾金泉, 陈晓勇, 汤宇豪. 基于多源光学遥感数据的湖泊湿地分类结果一致性分析. 地理与地理信息科学, 2021, 37(4): 45-50. |
ZHU J T, AI J Q, CHEN X Y, TANG Y H. Consistency analysis of classification results for lake wetland based on multi-source optical remote sensing data. Geography and Geo-information Science, 2021, 37(4): 45-50. (in Chinese) | |
[47] | PHAN T H. Rice monitoring using radar remote sensing[D]. Toulouse: Université Paul Sabatier - Toulouse III, 2018. |
[48] | 何泽. 基于多时相RADARSAT-2数据的水稻物候监测[D]. 成都: 电子科技大学, 2019. |
HE Z. Monitoring rice phenology based on multi-temporal RADARSAT-2 datasets[D]. Chengdu: University of Electronic Science and Technology of China, 2019. (in Chinese) | |
[49] |
NGUYEN D B, GRUBER A, WAGNER W. Mapping rice extent and cropping scheme in the Mekong Delta using Sentinel-1A data. Remote Sensing Letters, 2016, 7(12): 1209-1218.
doi: 10.1080/2150704X.2016.1225172 |
[50] |
YANG Z, SHAO Y, LI K, LIU Q B, LIU L, BRISCO B. An improved scheme for rice phenology estimation based on time-series multispectral HJ-1A/B and polarimetric RADARSAT-2 data. Remote Sensing of Environment, 2017, 195: 184-201.
doi: 10.1016/j.rse.2017.04.016 |
[51] |
HE A B, WANG W Q, JIANG G L, SUN H J, JIANG M, MAN J G, CUI K H, HUANG J L, PENG S B, NIE L X. Source-sink regulation and its effects on the regeneration ability of ratoon rice. Field Crops Research, 2019, 236: 155-164.
doi: 10.1016/j.fcr.2019.04.001 |
[52] |
YUAN S, CASSMANB K G, HUANG J L, PENG S B, GRASSINI P. Can ratoon cropping improve resource use efficiencies and profitability of rice in central China? Field Crops Research, 2019, 234: 66-72.
doi: 10.1016/j.fcr.2019.02.004 |
[53] |
LING X X, ZHANG T Y, DENG N Y, YUAN S, YUAN G H, HE W J, CUI K H, NIE L X, PENG S B, LI T, HUANG J L. Modelling rice growth and grain yield in rice ratooning production system. Field Crops Research, 2019, 241: 107574.
doi: 10.1016/j.fcr.2019.107574 |
No related articles found! |
|