Calibration and evaluation of a crop model using remote sensing data
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| Title: | Main Title: Calibration and evaluation of a crop model using remote sensing data |
| Description: | Abstract: Since precision farming is more and more applied, the heterogeneous structures of fields are also attracting more often interest in crop research. To further improve precise crop management the heterogeneous crop growth within a field has to be examined. The application of crop growth models is one way to analyze field structures. The simulation of within field heterogeneity of soil and crops could lead to an improved understanding of the crop physiological processes, which are directly influenced by variable soil conditions. Although there is a huge number of crop growth models, which can be applied to different environmental conditions, all of them need reliable input data to produce authentic output. To simulate crop growth under heterogeneous soil conditions, the variability of the soil has to be quantified. Atchley et al. 2011. The quantification of soil heterogeneity has been investigated in many studies with different methods like hyperspectral remote sensing (e.g. Hbirkou et al. 2012) or sensor-directed surface sampling techniques (e.g. Carroll et al. 2005, Corwin et al. 2005, Heil & Schmidhalter 2012, Kitchen et al. 2003, Mertens et al. 2008). One inexpensive and quick sensor-directed surface sampling technique to quantify soil variability at field-scale is the electromagnetic induction (EMI). EMI measurements represent a non-destructive method to estimate the apparent electrical conductivity (ECa) of the soil. In general, an EMI instrument induces current loops into the soil, which generate a secondary electromagnetic field. This secondary electromagnetic field is proportional to the current loop, which in turn is directly proportional to the electrical conductivity around this loop. As a result of existing soil properties the amplitude of the secondary field differs from the current loop (Corwin & Lesch 2005a). Due to its measuring principle EMI measurements are influenced by several factors like soil water content, soil salinity, bulk density, organic matter, and clay content (Corwin & Lesch 2005a, Johnson et al. 2003). Within these influencing factors can be differentiated between static and dynamic factors. Dynamic factors comprise e.g. soil water content and soil salinity, while static factors are texture for instance. Spatial patterns vary when dynamic factors are dominant; in systems containing more static influences, like texture-driven systems, spatial patterns remain consistent since the dynamic factors within this system only change the magnitude of the measured ECa values (Johnson et al. 2003). Since crop growth depends on the soil conditions, it is assumed that it reflects the soil heterogeneity patterns. Therefore, ground truth measurements of crop growth should have a direct relation to the soil. According to Corwin and Lesch (2005a) a relation between ECa and clay content could be proved at several locations, so that EMI measurements can serve as a proxy for the soil conditions. Only a few studies are engaged in finding relations between heterogeneity patterns of soil and crop growth (Corwin et al. 2003, Hupet et al. 2004, Juma 1993, Kitchen et al. 2003, Perez Quezada 2003, Pettersson et al. 2006). All of these studies correlated ECa with crop yield but a relation could only be verified in some cases. For cotton yield in California a moderate positive correlation has been found (Corwin et al. 2003), just like for the grain yields of corn, soybean, grain sorghum and winter wheat grown in Colorado, Kansas and Missouri (Kitchen et al. 2003). It is noticeably that the correlation results from areas with less precipitation are lower than from those areas with higher precipitation rates. Corwin and Lesch (2005c) mention that EMI measurements are not usable, if the topsoil is dry due to its non-conductivity. As our test sites are located in the western part of Germany where topsoils desiccate only at rare intervals, we hypothesize that the EMI measurements correlate with the crop physiological measurements taken in frequent intervals during the growing period. This correlation could help to get a better understanding of the interactions between soil and crops and how to quantify the patterns resulting from these interactions. Based on these findings, our main question to be answered here is, which crop physiological processes are the driving mechanisms for heterogeneity of crop growth in sugar beet and winter wheat stands under field conditions. Therefore, our objectives are (I) to identify soil characteristics with EMI measurements and (II) to correlate the EMI measurement patterns with crop measurements (III) to estimate which crop growth processes are mainly influenced by varying soil characteristics. |
Responsible Party
| Creator: | Anja Stadler (Author) |
| Publisher: | CRC/TR32 Database (TR32DB) |
| Publication Year: | 2013 |
Topic
| TR32 Topic: | Vegetation |
| Related Subproject: | B5 |
| Subject: | Keyword: PhD Report |
File Details
| Filename: | Report3_Stadler_2012.pdf |
| Data Type: | Text - Text |
| File Size: | 299 KB |
| Date: | Available: 02.10.2013 |
| Mime Type: | application/pdf |
| Data Format: | |
| Language: | English |
| Status: | Completed |
Constraints
| Download Permission: | Only Project Members |
| General Access and Use Conditions: | According to the TR32DB data policy agreement. |
| Access Limitations: | According to the TR32DB data policy agreement. |
| Licence: | [TR32DB] Data policy agreement |
Geographic
Specific Information - Report
| Report Date: | 8th of April, 2013 |
| Report Type: | PhD Report |
| Report City: | Bonn, Germany |
| Report Institution: | Institute of Crop Science and Resource Conservation, INRES, University Bonn |
| Number of Pages: | 12 (1 - 12) |
| Further Information: | TR32 Student Report Phase II |
Metadata Details
| Metadata Creator: | Anja Stadler |
| Metadata Created: | 28.11.2013 |
| Metadata Last Updated: | 28.11.2013 |
| Subproject: | B5 |
| Funding Phase: | 2 |
| Metadata Language: | English |
| Metadata Version: | V50 |
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| Metadata Downloads: | 0 |
| Dataset Downloads: | 4 |
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