干旱区生态环境知识资源中心(Arid): A data-driven high spatial resolution model of biomass accumulation and crop yield: Application to a fragmented desert-oasis agroecosystem

Arid

DOI	10.1016/j.ecolmodel.2022.110182
	A data-driven high spatial resolution model of biomass accumulation and crop yield: Application to a fragmented desert-oasis agroecosystem
	Chen, Qiting; Jia, Li; Menenti, Massimo; Hu, Guangcheng; Wang, Kun; Yi, Zhiwei; Zhou, Jie; Peng, Fei; Ma, Shaoxiu; You, Quangang; Chen, Xiaojie; Xue, Xian
通讯作者	Jia, L
来源期刊	ECOLOGICAL MODELLING
ISSN	0304-3800
EISSN	1872-7026
出版年	2023
卷号	475
英文摘要	Information on crop yield is important for food security, in particular under the conditions of climate change and growing population worldwide. We developed a new fully distributed, high spatial resolution, model of biomass accumulation and crop yield applicable to a highly heterogeneous desert-oasis agroecosystem. The bulk of required input data is obtained by retrieving pixel-wise biogeophysical variables from a suite of very diverse satellite data. Both temperature and water stress conditions at field-scale are given full consideration, while the model was designed to strike a balance between model applicability and satisfactory characterization of the heterogeneous desert-oasis system to estimate field-scale yield. The development of this model relies on three main innovations. First, the start and end of the growing season were estimated for each pixel by calibrating the high spatial and temporal resolution observations of Normalized Difference Vegetation Index (NDVI) by Sentinal-2 (S2) MSI (Multi-Spectral Instrument) against limited local phenological information. Second, to monitor crop water stress, account taken of irrigation, a process-based water and energy balance model was applied to esti -mate the actual evapotranspiration (ET). This requires knowledge of soil water availability, which is charac-terized by downscaling the ASCAT (Advanced SCATterrometer) soil moisture data product. To capture the dominant features of the eco-hydrological conditions in the desert and oasis agroecosystem, ET was further downscaled from the 1 km resolution. Third, likewise the water stress indicator, the air temperature stress in-dicator was mapped after characterizing the thermal contrast and heterogeneity of the desert-oasis system, by generating time series of air temperature at 1 km spatial resolution using the MODIS (Moderate Resolution Imaging Spectroradiometer) Land Surface Temperature (LST) data product. In the temporal dimension, gaps were mitigated by applying time series analysis techniques to reconstruct cloud-free time series of LST, NDVI, fAPAR and albedo. These innovations add up to a high resolution characterization of crop response to the geospatial variability of weather and climate forcing in the desert-oasis agroecosystem. The model was applied to the dominant crops, i.e., spring wheat, maize, sunflower, and melon, in the oases of the Shiyang River Basin (northwestern China) characterized by a rather fragmented land use. The high resolution of pixel-wise ecohy-drological parameters, i.e., crop phenology, temperature stress and water stress factors successfully reflect dif-ferences of crops with different phenology and location in the oases. The relative errors for wheat and maize yields compared to the census data are less than 5% at district level. At the county level, the relative errors of wheat yields of Liangzhou, Minqin, Gulang, Jinchuan, and Yongchang equal to 0.87%, 24.2%, 9.7%, 12.5%, and 7.2%. For maize, the dominant crop, the error on estimated yields was less than 5%, except in Gulang. The relative error on estimated yield for sunflower was less than 10% compared to agricultural census data. The relative error on estimated melon yield was 16%. This performance highlights the applicability of the model to estimate field-scale yields in agroecosystems characterized by fragmented land use.
英文关键词	Crop yield estimation Multi-source remote sensing data High resolution Heterogeneous agroecosystem
类型	Article
语种	英语
开放获取类型	Green Published, hybrid
收录类别	SCI-E
WOS记录号	WOS:000883800200001
WOS关键词	NET PRIMARY PRODUCTION ; PHOTOSYNTHETICALLY ACTIVE RADIATION ; LIGHT-USE EFFICIENCY ; REMOTELY-SENSED DATA ; SHIYANG RIVER-BASIN ; MELO-CV HUANGHEMI ; TERRESTRIAL GROSS ; DATA ASSIMILATION ; AIR-TEMPERATURE ; SATELLITE
WOS类目	Ecology
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/396000
推荐引用方式 GB/T 7714	Chen, Qiting,Jia, Li,Menenti, Massimo,et al. A data-driven high spatial resolution model of biomass accumulation and crop yield: Application to a fragmented desert-oasis agroecosystem[J],2023,475.
APA	Chen, Qiting.,Jia, Li.,Menenti, Massimo.,Hu, Guangcheng.,Wang, Kun.,...&Xue, Xian.(2023).A data-driven high spatial resolution model of biomass accumulation and crop yield: Application to a fragmented desert-oasis agroecosystem.ECOLOGICAL MODELLING,475.
MLA	Chen, Qiting,et al."A data-driven high spatial resolution model of biomass accumulation and crop yield: Application to a fragmented desert-oasis agroecosystem".ECOLOGICAL MODELLING 475(2023).