A spatial and temporal evaluation of the SMAP cropland b-parameter across the U.S. Corn Belt

IF 11.1 1区 地球科学 Q1 ENVIRONMENTAL SCIENCES Remote Sensing of Environment Pub Date : 2023-11-01 DOI:10.1016/j.rse.2023.113752
Theodore Hartman , Richard Cirone , Kaitlin Togliatti , Brian K. Hornbuckle , Andy VanLoocke
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Abstract

The liquid water contained within plant tissue per ground area is an indicator of plant biomass and related to plant transpiration. In the U.S. Corn Belt this plant water is dominated by water in crop tissue. Microwave remote sensing provides an opportunity to measure crop water at a 30 to 40-km spatial scale across the U.S. Corn Belt. Crop water has been shown to be directly proportional to L-band vegetation optical depth (VOD) through a proportionality constant called the b-parameter. NASA’s SMAP satellite uses a static b-parameter of 0.11 for cropland areas, however the b-parameter varies with crop type. This study answers the following research questions. First, does the value of the b-parameter value vary across the U.S. Corn Belt given the observed differences in the proportions of corn and soybeans grown across the region? Secondly, does the SMAP b-parameter value vary throughout the growing season given the significant change in the distribution of crop water within tissue types throughout the growing season? Since in-situ measurements of the b-parameter are higher for corn, we hypothesize that as the relative fraction of corn in a SMAP pixel increases, the b-parameter will increase. We test this hypothesis using satellite scale modeling of crop water and SMAP L2 DCA VOD measurements at 18 sites across the U.S. Corn Belt for the years 2015, 2016, and 2017. For each site year, the crop water for the satellite pixel is simulated using the Agro-IBIS crop model and is weighted according to the proportion of corn and soybeans grown in that location. We find that there is no statistically significant correlation between the relative corn fraction and the SMAP b-parameter across the U.S. Corn Belt and that the current SMAP cropland value of 0.11 is appropriate for the beginning of the growing season (growing degree days(GDD)<100̄0Cday). However, we find that the b-parameter in the second half of the growing season (GDD100̄0Cday), during the crop reproductive stages, has an average value of 0.17. The results from this study indicate that while there is no change in the b-parameter due to the proportion of crops growing in a satellite pixel, there may be a difference in b-parameter due to changing water distribution within the crop canopy due to the development of crop reproductive structures (ears and pods) during the second half of the growing season.

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美国玉米带SMAP农田b参数的时空评价
单位地面面积植物组织中所含的液态水是植物生物量的一个指标,与植物蒸腾作用有关。在美国玉米带,这种植物的水分主要是作物组织中的水分。微波遥感提供了在美国玉米带30至40公里的空间尺度上测量作物水分的机会。通过称为b参数的比例常数,作物水分已被证明与l波段植被光学深度(VOD)成正比。美国国家航空航天局的SMAP卫星对耕地面积的静态b参数为0.11,但b参数随作物类型而变化。本研究回答了以下研究问题。首先,考虑到美国玉米带种植的玉米和大豆比例的差异,b参数值的值在美国玉米带是否有所不同?其次,考虑到作物水分在组织类型内的分布在整个生长季节发生了显著的变化,SMAP b参数值在整个生长季节是否会发生变化?由于玉米的原位测量值更高,我们假设随着玉米在SMAP像素中的相对比例的增加,b参数也会增加。我们利用2015年、2016年和2017年美国玉米带18个地点的作物水分卫星尺度模型和SMAP L2 DCA VOD测量数据验证了这一假设。对于每个站点年,使用Agro-IBIS作物模型模拟卫星像元的作物水分,并根据该地点种植的玉米和大豆的比例进行加权。我们发现,美国玉米带的玉米相对分数与SMAP b参数之间没有统计学上的显著相关性,目前的SMAP耕地值0.11适用于生长季节开始(生长度日(GDD)<100°C⋅day)。然而,我们发现生长季后半段(GDD≥100°C⋅day),即作物繁殖期的b参数平均值为0.17。本研究结果表明,虽然卫星像元内作物生长的比例没有引起b参数的变化,但由于作物生长季后半期作物生殖结构(穗和荚果)的发育,导致作物冠层内水分分布的变化,可能会引起b参数的差异。
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来源期刊
Remote Sensing of Environment
Remote Sensing of Environment 环境科学-成像科学与照相技术
CiteScore
25.10
自引率
8.90%
发文量
455
审稿时长
53 days
期刊介绍: Remote Sensing of Environment (RSE) serves the Earth observation community by disseminating results on the theory, science, applications, and technology that contribute to advancing the field of remote sensing. With a thoroughly interdisciplinary approach, RSE encompasses terrestrial, oceanic, and atmospheric sensing. The journal emphasizes biophysical and quantitative approaches to remote sensing at local to global scales, covering a diverse range of applications and techniques. RSE serves as a vital platform for the exchange of knowledge and advancements in the dynamic field of remote sensing.
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