Abstract
Crop growth is an important parameter to monitor in order to obtain accurate remotely sensed estimates of soil moisture, as well as assessments of crop health, productivity, and quality commonly used in the agricultural industry. The Soil Moisture Active Passive (SMAP) mission has been collecting Global Positioning System (GPS) signals as they reflect off the Earth’s surface since August 2015. The L-band dual-polarization reflection measurements enable studies of the evolution of geophysical parameters during seasonal transitions. In this paper, we examine the sensitivity of SMAP-reflectometry signals to agricultural crop growth related characteristics: crop type, vegetation water content (VWC), crop height, and vegetation opacity (VOP). The study presented here focuses on the United States “Corn Belt,” where an extensive area is planted every year with mostly corn, soybean, and wheat. We explore the potential to generate regularly an alternate source of crop growth information independent of the data currently used in the soil moisture (SM) products developed with the SMAP mission. Our analysis explores the variability of the polarimetric ratio (PR), computed from the peak signals at V- and H-polarization, during the United States Corn Belt crop growing season in 2017. The approach facilitates the understanding of the evolution of the observed surfaces from bare soil to peak growth and the maturation of the crops until harvesting. We investigate the impact of SM on PR for low roughness scenes with low variability and considering each crop type independently. We analyze the sensitivity of PR to the selected crop height, VWC, VOP, and Normalized Differential Vegetation Index (NDVI) reference datasets. Finally, we discuss a possible path towards a retrieval algorithm based on Global Navigation Satellite System-Reflectometry (GNSS-R) measurements that could be used in combination with passive SMAP soil moisture algorithms to correct simultaneously for the VWC and SM effects on the electromagnetic signals.
Highlights
Sustaining and enhancing the economical production of crops continues to be an important focus of agricultural research
Using a modified version of the Soil Moisture Active Passive (SMAP)-R processor used in [11,12], data are filtered for those geometries where there is potential to capture a specular point, i.e., within the -3 dB beam width of the SMAP antenna pointed to 40◦, which provides a range of incidence angles between 37.3◦ and 42.7◦
In order to analyze the sensitivity of SMAP-R signals to the crop growth parameters, we selected data collected from the United States (U.S.) Corn Belt, an extensive agricultural area that is planted with primarily corn, soybean, and wheat every year
Summary
Sustaining and enhancing the economical production of crops continues to be an important focus of agricultural research. For agricultural areas characterized by very dynamic landscapes in terms of SM, VWC, VOP, and vegetation height, a 10-day independent VWC product would have a great impact on the soil moisture retrievals obtained by satellite missions as SMAP. When considering VOP (dependent on VWC) information, agricultural areas will show a bigger discrepancy between seasonal climatology and daily estimates as compared to natural spaces Another descriptor of the vegetation is the vegetation height, i.e., the thickness of the vegetation layer. There have been previous studies employing left hand and right hand circularly polarized GNSS-R measurements to compute a polarimetric ratio, such as [18,19] and a previous work using SMAP-R data [12] This manuscript presents a sensitivity analysis of the SMAP-R signals to VWC, VOP, crop height, and type, including corn and soybean.
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