Assessment of Wheat Straw Cover and Yield Performance in a Rice-Wheat Cropping System by Using Landsat Satellite Data
文献类型： 外文期刊
作者： Memon, Muhammad Sohail 1 ; Jun, Zhou 1 ; Sun, Chuanliang 3 ; Jiang, Chunxia 1 ; Xu, Weiyue 1 ; Hu, Qiong 8 ; Yang, Han 1 ;
作者机构： 1.Nanjing Agr Univ, Coll Engn, Nanjing 210031, Jiangsu, Peoples R China
2.Sindh Agr Univ, Fac Agr Engn, Tandojam 70060, Pakistan
3.Jiangsu Acad Agr Sci, Inst Agr Informat, Nanjing 210014, Jiangsu, Peoples R China
4.Univ Alberta, Fac Sci, Dept Earth & Atmospher Sci, Edmonton, AB T6G 2E3, Canada
5.Anhui Sci & Technol Univ, Coll Mech Engn, Chuzhou City 233100, Anhui, Peoples R China
6.Univ Calif Davis, Dept Biol & Agr Engn, Davis, CA 95616 USA
7.Texas A&M Univ, Coll Engn, Dept Biol & Agr Engn, Texas, TX 77843 USA
8.Cent China Normal Univ, Key Lab Geog Proc Anal & Simulat Hubei Prov, Sch Urban & Environm Sci, Wuhan 430079, Hubei, Peoples R China
9.Jinhua Polytech, Coll Mech & Elect Engn, Jinhua 321017, Zhejiang, Peoples R China
关键词： agricultural sustainability; remote sensing; Landsat; crop yield; GIS; spatial analysis; wheat straw cover percentage; vegetation indices
期刊名称：SUSTAINABILITY （ 影响因子：3.251； 五年影响因子：3.473 ）
ISSN：
年卷期： 2019 年 11 卷 19 期
页码：
收录情况： SCI
摘要： Proper straw cover information is one of the most important inputs for agroecosystem and environmental modeling, but the availability of accurate information remains limited. However, several remote-sensing (RS)-based studies have provided a residue cover estimation and provided spatial distribution mapping of paddy rice areas in a constant field condition. Despite this, the performance of rice crops with straw applications has received little attention. Furthermore, there are no methods currently available to quantify the wheat straw cover (WSC) percentage and its effect on rice crops in the rice-wheat cropping region on a large scale and a continuous basis. The novel approach proposed in this study demonstrates that the Landsat satellite data and seven RS-based indices, e.g., (i) normalized difference vegetation index (NDVI), (ii) Normalized difference senescent vegetation index (NDSVI), (iii) Normalized difference index 5 (NDI5), (iv) Normalized difference index 7 (NDI7), (v) Simple tillage index (STI), (vi) Normalized difference tillage index (NDTI), and (vii) Shortwave red normalized difference index (SRNDI), can be used to estimate the WSC percentage and determine the performance of rice crops over the study area in Changshu county, China. The regression model shows that the NDTI index performed better in differentiating the WSC at sampling points with a coefficient of determination (R-2 = 0.80) and root mean squared difference (RMSD = 8.46%) compared to that of other indices, whereas the overall accuracy for mapping WSC was observed to be 84.61% and the kappa coefficient was kappa = 0.76. Moreover, the rice yield model was established by correlating between the peak NDVI values and rice grain yield collected from ground census data, with R-2 = 0.85. The finding also revealed that the highest estimated yield (8439.67 kg/ha) was recorded with 68% WCS in the study region. This study confirmed that the NDVI and NDTI algorithms are very effective and robust indicators. Also, it can be strongly concluded that multispectral Landsat satellite imagery is capable of measuring the WSC percentage and successively determines the impact of different WSC percentages on rice crop yield within fields or across large regions through remote sensing (RS) and geographical information system (GIS) techniques for the long-term planning of agriculture sustainability in rice-wheat cropping systems.
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