Progress 03/12/14 to 02/28/19
Outputs
Target Audience:The target audience is the scientific community that is engaged in improving predictions of future weather and climate. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?Undergraduate and graduate students received scientific training in the process of conducting the research described above. How have the results been disseminated to communities of interest?We have published four articles in scientific journals. What do you plan to do during the next reporting period to accomplish the goals?
Nothing Reported
Impacts
What was accomplished under these goals?
IMPACT: My work in this project has increased society's understanding of how Earth-orbiting satellites can be used to quantify water stored in soil and vegetation in regions where vegetation conditions vary significantly in space and in time.A specific example of such an area is the Corn Belt where crops are planted and harvested annually.Eventually satellite observations of soil moisture and vegetation could be incorporated into weather and climate models.These observations will improve the treatment of processes that transfer energy and mass between Earth's surface and atmosphere.The result will be improved weather and climate predictions for Corn Belt residents that can be used to make better crop management decisions.In addition, new satellite measurements of vegetation could allow for better monitoring of large-scale crop production. Objective 1. Assess the validity of soil moisture and vegetation observations made by microwave remote sensing instruments on current Earth-orbiting satellites at scales similar to Iowa counties (40 km). In the past decade the European Space Agency (ESA) and NASA have launched two first-of-their-kind satellite instruments.ESA's Soil Moisture and Ocean Salinity (SMOS) satellite and NASA's Soil Moisture Active Passive (SMAP) satellite each carry a passive remote sensing instrument (essentially a camera) that measures microwave radiation naturally emanating from Earth's land surface.At microwave wavelengths, vegetation is semi-transparent and SMOS and SMAP can "see" radiation that originates from beneath the soil surface. This radiation increases when soils are dry and decreases when soils are wet.SMOS and SMAP are a great advancement over visible and infrared satellites that are only sensitive to the condition of either the first millimeter (or less) of the soil surface.As a result, SMOS and SMAP are able to produce an estimate of the water content of the first several centimeters of the soil, commonly called soil moisture.These data are available at a spatial resolution close to the size of a typical Midwestern county (about 40 by 40 km), and at a temporal frequency of nearly every day. While SMOS and SMAP can "see through" vegetation, vegetation does contribute to the microwave radiation sensed by the satellites. Consequently, SMOS and SMAP also retrieve a quantity called the vegetation optical depth (VOD), a measure of the degree to which vegetation attenuates microwave radiation. Because attenuation is primarily a function of the total amount of liquid water in a vegetation canopy, and this amount of water changes as the canopy grows and as crops progress through developmental stages, VOD could be a useful product itself. Consequently, VOD could be used to monitor crop water, the mass of water contained within crop tissue per ground area, in major agricultural regions such as the Corn Belt, and crop water could be used to characterize crop growth and development. There are two main advantages of satellite VOD observed by SMOS and SMAP. First, VOD is representative of the entire canopy volume, from the soil surface to the top of the canopy, in contrast to other satellite observations made at shorter wavelengths that are only able to detect changes in the top-most layer of vegetation. Second, since SMOS and SMAP can "see through" clouds and most precipitation, near daily measurements are possible in stark contrast to current weekly-at-best satellite vegetation observations. Here I highlight four journal papers published during this project. The first (http://dx.doi.org/10.1175/JHM-D-14-0137.1) sought to explain why SMOS and SMAP appear to have a "dry bias," that is, that satellite-observed soil moisture is consistently lower than the soil moisture measured by networks of instruments on the ground, buried in the soil, arranged to sample an entire 40 by 40 km SMOS or SMAP pixel. By analyzing periods following significant rain events, when soil moisture rapidly decreases (called a "dry down"), we were able to conclude that satellites and ground-based instruments measure two different soil layers because the rates of soil drying were not the same. Satellite soil moisture decreased more rapidly than ground-based soil moisture, implying that the soil layer observed by ground instruments is deeper into the soil than the layer observed by satellites. However, model results and additional ground measurements specifically designed to characterize these two soil layers led us to find that while these two soil layers have different soil moisture values over short periods, they trend to the same value of soil moisture over time, and thus cannot explain the satellite dry bias. We hypothesized other causes of the dry bias to be tested in future investigations. The second paper (http://dx.doi.org/10.1016/j.rse.2016.02.043) is the first to directly link VOD to the growth and development of crops. Specifically, we determined that peak value of VOD in the Corn Belt occurs at the third corn reproductive stage because: crop VOD is directly proportional to amount of liquid water in plant tissue per ground area; corn contributes the most to growing season VOD changes in the Corn Belt; and corn reaches its peak "crop water" at its third reproductive stage. As a result, VOD can be used to monitor Corn Belt crop progress. The third and fourth papers (http://dx.doi.org/10.1109/JSTARS.2018.2864897 and http://dx.doi.org/10.3390/rs11212488) represent our latest efforts to explain the satellite dry bias and propose possible solutions. The third paper quantifies the degree to which SMOS underestimates "true" soil moisture according to what is measured by instruments in the ground. The fourth quantifies the SMAP dry bias. The difference between SMOS and SMAP and ground measurements is similar. A key finding of the third paper is that the network of ground measurements is robust: soil moisture is measured at 20 different sites, and it appears that this is about twice as many as needed to correctly characterize pixel-scale soil moisture; and even though the ground-based soil moisture measurements are installed on the edges of fields (so they can be permanently installed), this does not result in the soil moisture measured by the network of measurements to be artificially too high or too low. A key finding of the fourth paper is that the SMAP dry bias varies seasonally: it is greatest in June, July, August, and September and thus the dry bias can be contributed to the presence of crops. We conclude that the most important next step needed to correct SMOS and SMAP soil moisture observations in the Corn Belt is to separate the calendar year into three periods (the period before crops emerge, when crops are present, and after harvest) and develop separate procedures for retrieving soil moisture from the raw satellite measurements in each period. Objective 2: nothing to report. Objective 3: nothing to report.
Publications
- Type:
Journal Articles
Status:
Published
Year Published:
2019
Citation:
Walker, V. A., B. K. Hornbuckle, M. H. Cosh, and J. H. Prueger, Seasonal Evaluation of SMAP Soil Moisture in the U.S. Corn Belt, Remote Sensing, 11, 2488, doi:10.3390/rs11212488, 2019.
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Progress 10/01/17 to 09/30/18
Outputs
Target Audience:The target audience is the scientific community that is engaged in improving predictions of future weather and climate. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?One graduate student received scientific training in the process of conducting the research described above. How have the results been disseminated to communities of interest?The graduate student gave an oral presentation at the 15th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment in Cambridge, MA. The graduate student gave an oral presentation at the 2018 IEEE International Geoscience and Remote Sensing Symposium in Valencia, Spain. The PI gave an oral presentation at the 2018 IEEE International Geoscience and Remote Sensing Symposium in Valencia, Spain. The graduate student gave an oral presentation at the 5th Satellite Soil Moisture Validation and Application Workshop at George Mason University in Fairfax, VA. A paper on this research has been published by the journal IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. What do you plan to do during the next reporting period to accomplish the goals?
Nothing Reported
Impacts
What was accomplished under these goals?
Overall Impact Statement: Two Earth-observing satellites, NASA's Soil Moisture Active Passive (SMAP) and the European Space Agency's Soil Moisture and Ocean Salinity (SMOS), are exploratory science missions that will help establish regular space-based observations of soil moisture. In the future, satellite soil moisture will be used to improve weather and climate predictions. We know that the soil moisture values measured by SMAP and SMOS are "too dry" as compared to values recorded by a unique on-the-ground soil moisture monitoring network in Iowa. Consequently it is very likely that SMAP and SMOS are not producing accurate soil moisture observations throughout the US Corn Belt. We have recently determined that NASA's effort to correct SMAP soil moisture is not appropriate for the Corn Belt. Furthermore, we have also found that explicit representation of both soil and vegetation temperature, instead of using a single temperature for both soil and vegetation in the satellite retrieval algorithm, would improve SMAP soil moisture. These findings will help scientists correctly interpret the remote-sensing observations of SMAP and SMOS, produce satellite soil moisture that is consistent with ground observations, and eventually improve weather and climate predictions. Objective 1: Assess the validity of observations of near-surface soil moisture, crops, and soil roughness made by microwave remote sensing instruments on current Earth-orbiting satellites at scales similar to Iowa counties (40 km) and townships (10 km). The European Space Agency launched the Soil Moisture and Ocean Salinity (SMOS) satellite in late 2009. In 2015 NASA launched the Soil Moisture Active Passive (SMAP) satellite. Both satellites carry passive remote sensing instruments that operate at a frequency of 1.4 GHz (wavelength of 21 cm) which is in the microwave region of the electromagnetic spectrum. At microwave wavelengths, vegetation is semi-transparent and SMOS and SMAP can "see" radiation that originates from beneath the soil surface. This is in stark contrast to visible and infrared satellites that are only sensitive to the condition of either the first millimeter (or less) of the soil surface, or the very top of the vegetation canopy, if present. Furthermore, radiation at 1.4 GHz emitted by Earth's terrestrial surface changes dramatically with water content. Hence the SMOS and SMAP satellites are able to produce an estimate of the water content of the first several centimeters of the soil, called the near-surface soil moisture. These data are available at a spatial resolution close to the size of a typical Midwestern county (about 40 by 40 km), and at a temporal frequency of about every-other day. We have found that the near-surface soil moisture observed by both SMOS and SMAP is "too dry" in the US Midwest. In other words, the soil moisture reported by SMOS and SMAP is about 0.07 m^3 m^{-3} lower than the "true" value of soil moisture. (Soil moisture ranges from about 0.05 to 0.45 m^3 m^{-3}.) The goal of SMOS and SMAP is to measure soil moisture with a mean error (or bias) of less than 0.04 m^3 m^{-3}. Here we define the "true" value of soil moisture to be the soil moisture measured by a network of in situ soil sensors within the watershed of the South Fork Iowa River in central Iowa. This network consists of 20 stations at which soil moisture is measured at a depth into the soil consistent with what SMOS and SMAP are able to "see." Our findings in Iowa are consistent with what has been found by other researchers in other agricultural areas. SMAP soil moisture is generated by an algorithm that produces a soil moisture estimate from the raw data measured by the SMAP satellite. The algorithm is based upon a model that represents our current understanding of how the microwave radiation received by a satellite depends on soil moisture, soil type, the amount and type of vegetation covering the soil, the temperature of the soil and vegetation, and atmospheric conditions. In 2018 NASA released a new version of SMAP data. We compared the new SMAP soil moisture with the soil moisture measured by the in situ sensors in the South Fork. While we found that the dry bias in SMAP soil moisture had been reduced overall, there were still specific months of the year when the error exceeded 0.04 m^3 m^{-3}. What NASA had changed in the new algorithm was the soil temperature: soil temperature was increased by a factor of 2%. We compared in situ soil temperature measured at the 20 stations in the South Fork with the new estimate of soil temperature used in the algorithm. We found that the new soil temperature was 5 to 9 degrees Celsius (9 to 16 degrees Fahrenheit) warmer than in situ soil temperature. Consequently, while the new version of SMAP data does perform better overall, it is not for the right reason. Artificially increasing soil temperature in the algorithm does reduce the dry bias, but the new soil temperature is not realistic. While investigating soil temperature, we also critically examined the assumption made in the algorithm that soil and vegetation temperatures can be considered to be the same. This is a reasonable assumption in many situations because SMAP passes over local areas on Earth's surface near sunrise and sunset. At these times it is plausible that soil and vegetation temperature are similar as they occur before the heat of the day and as the transition from day to night occurs. We examined in situ measurements of soil and vegetation temperatures. We found that for corn and soybean fields vegetation temperature is, on average, 2 to 3 degrees Celsius (4 to 5 degrees Fahrenheit) colder than soil temperature at sunrise, and 2 to 3 degrees (4 to 5 degrees Fahrenheit) warmer than soil temperature at sunset. Therefore, using separate soil and vegetation temperatures in the SMAP algorithm would be more appropriate than assuming that they are the same. Objective 2: nothing to report. Objective 3: nothing to report
Publications
- Type:
Journal Articles
Status:
Published
Year Published:
2018
Citation:
Walker, V. A., B. K. Hornbuckle, and M. H. Cosh, A Five-Year Evaluation of SMOS Level 2 Soil Moisture in the Corn Belt of the United States, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, doi:10.1109/JSTARS.2018.2864897, 2018.
- Type:
Conference Papers and Presentations
Status:
Accepted
Year Published:
2018
Citation:
Walker, V. A., B. K. Hornbuckle, and B. K. Gelder, Identifying SMOS and SMAP Pixels that Exhibit Distinct Roughness-Vegetation Patterns in Level-2 Optical Thickness Retrievals, 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, doi:10.1109/IGARSS.2018.8518101.
- Type:
Conference Papers and Presentations
Status:
Accepted
Year Published:
2018
Citation:
Lewis-Beck, C., P. Caragea, J. Niemi, B. Hornbuckle, and V. Walker, A Nonlinear Hierarchical Model for Modeling Crop Growth in the US Corn Belt, 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, doi:10.1109/IGARSS.2018.8518473.
- Type:
Conference Papers and Presentations
Status:
Accepted
Year Published:
2018
Citation:
Lewis-Beck, C., J. Niemi, P. Caragea, B. Hornbuckle, and V. Walker, Monitoring Crop Growth in the US Corn Belt with SMOS Level 2 Tau, 2018 IEEE 15th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment, Cambridge, MA, doi:10.1109/MICRORAD.2018.8430697.
|
Progress 10/01/16 to 09/30/17
Outputs
Target Audience: The target audience is the scientific community that is engaged in improving predictions of future weather and climate. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?One graduate student received scientific training in the process of conducting the research described above. How have the results been disseminated to communities of interest?The graduate student gave an oral presentation at the 2017 IEEE International Geoscience and Remote Sensing Symposium in Forth Worth, TX. The graduate student and the PI submitted a paper on this research to the journal IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. The PI presented a poster at the 2017 IEEE International Geoscience and Remote Sensing Symposium in Forth Worth, TX. What do you plan to do during the next reporting period to accomplish the goals?We plan to apply what we have learned regarding the effect of soil surface roughness on SMOS to the SMAP satellite. We also plan to compare the values of measured soil surface roughness to those that can be inferred through special experimental processing of SMOS and SMAP satellite data.
Impacts
What was accomplished under these goals?
Overall Impact Statement: Two Earth-observing satellites, NASA's Soil Moisture Active Passive (SMAP) and the European Space Agency's Soil Moisture and Ocean Salinity (SMOS), are exploratory science missions that will help establish regular space-based observations of soil moisture. In the future, satellite soil moisture will be used to improve weather and climate predictions. We know that the soil moisture values measured by SMAP and SMOS are "too dry" as compared to values recorded by a unique on-the-ground soil moisture monitoring network in Iowa. We helped conduct an experiment in this important agricultural region to investigate. We hypothesized that soil surface "roughness," the cm-scale variations in the height of the soil surface caused by tillage, could be the problem. We have analyzed data from the experiment and have been able to confirm that measured roughness is higher than what is assumed by the SMAP and SMOS missions. This finding will help scientists correctly interpret the remote-sensing observations of SMAP and SMOS, produce satellite soil moisture that is consistent with ground observations, and eventually improve weather and climate predictions. Objective 1. Assess the validity of observations of near-surface soil moisture, crops, and soil roughness made by microwave remote sensing instruments on current Earth-orbiting satellites at scales similar to Iowa counties (40 km) and townships (10 km). The European Space Agency launched the Soil Moisture and Ocean Salinity (SMOS) satellite in late 2009. In 2015 NASA launched the Soil Moisture Active Passive (SMAP) satellite. Both satellites carry passive remote sensing instruments that operate at a frequency of 1.4 GHz (wavelength of 21 cm) which is in the microwave region of the electromagnetic spectrum. At microwave wavelengths, vegetation is semi-transparent and SMOS and SMAP can "see" radiation that originates from beneath the soil surface. This is in stark contrast to visible and infrared satellites that are only sensitive to the condition of either the first millimeter (or less) of the soil surface, or the very top of the vegetation canopy, if present. Furthermore, radiation at 1.4 GHz emitted by Earth's terrestrial surface changes dramatically with water content. Hence the SMOS and SMAP satellites are able to produce an estimate of the water content of the first several centimeters of the soil, called the near-surface soil moisture. These data are available at a spatial resolution close to the size of a typical Midwestern county (about 40 by 40 km), and at a temporal frequency of about every-other day. We have found that the near-surface soil moisture observed by both SMOS and SMAP is "too dry" in the US Midwest. In other words, the soil moisture reported by SMOS and SMAP is about 0.07 m^3 m^{-3} lower than the "true" value of soil moisture. (Soil moisture ranges from about 0.05 to 0.45 m^3 m^{-3}.) The goal of SMOS and SMAP is to measure soil moisture with a mean error (or bias) of 0.00 m^3 m^{-3}. Here we define the "true" value of soil moisture to be the soil moisture measured by a network of in situ soil sensors within the watershed of the South Fork Iowa River in central Iowa. This network consists of 20 stations at which soil moisture is measured at a depth into the soil consistent with what SMOS and SMAP are able to "see." Our findings in Iowa are consistent with what has been found by other researchers in other agricultural areas. In order to understand why SMOS and SMAP are too dry, we have investigated several aspects of the soil moisture retrieval process. It appears that soil surface "roughness," the cm-scale variations in the height of the soil surface, could be the cause. Soil surface roughness increases due to management activities such as tillage and planting, and decreases after subsequent rainfall when water drops erode the soil and "smooth it out." Adjusting the parameter in the SMOS algorithm that accounts for the effect of soil surface roughness, to make the soil surface "rougher," decreases the dry bias. However, increasing soil surface roughness had a detrimental effect of decreasing the overall sensitivity of SMOS and SMAP to soil moisture. We used data collected during a 20-day 2016 field experiment to further investigate the effect of soil surface roughness on satellite soil moisture.This experiment, called SMAPVEX16-IA, was funded by both the USDA and NASA and involved approximately 50 scientists from around the US and the world. Our group helped make in situ measurements of soil moisture, vegetation, and soil surface roughness. We collected roughness data using three different instruments: a laser scanner, a pin board, and visible pictures of the soil surface. We have analyzed the data and have found that the three measurements have different sensitivities to roughness.The laser scanner is the most sensitive, while the visible pictures are the least sensitive.However, all three methods produced values of roughness consistent with what other scientists have reported in other experiments conducted in other agricultural regions.We found that the roughness values did not change significantly over a 10-day period in late-May and early-June. However, the measured values were much higher than what SMOS and SMAP assume for this area of the world.This is consistent with our hypothesis that incorrect values of roughness are part of the reason why SMOS and SMAP are "too dry." Objective 2: nothing to report. Objective 3: nothing to report.
Publications
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2017
Citation:
Hornbuckle, B., V. Walker, B. Eichinger, V. Wallace, and E. Yildirim, Soil Surface Roughness Observed During SMAPVEX16-IA and its Potential Consequences for SMOS and SMAP, IEEE International Geoscience and Remote Sensing Symposium, Fort Worth, TX.
|
Progress 10/01/15 to 09/30/16
Outputs
Target Audience:The target audience is the scientific community that is engaged in improving predictions of future weather and climate. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?Two students received scientific training in the process of conducting the research described above. One student was a graduate student and the other an undergraduate. The graduate students received an M.S. in agricultural meteorology. The undergraduate became involved through the freshman honors program and continued their work via a paid summer internship. How have the results been disseminated to communities of interest?The graduate student presented previous work at the 14th Specialist Meeting on Miocrowave Radiometry and Remote Sensing of the Environment in Espoo, Finland. A journal paper detailing previous work has been published in a special issue of Remote Sensing of Environment on "ESA's Soil Moisture and Ocean Salinity Mission - Achievements and Novel Applications after Five Years in Orbit." We presented some initial results from the soil surface roughness measurements at the Soil Science Society of American Annual Meeting in Phoenix, AZ. What do you plan to do during the next reporting period to accomplish the goals?We plan to continue to pursue Objective 1 in the coming year. We will test another hypothesis for why SMOS and SMAP are too dry in the US Midwest. Our hypothesis is that spring tillage, subsequent smoothing due to rainfall, harvest, and fall tillage cause significant changes in soil surface roughness that must be taken into account by SMOS and SMAP. We will also analyze the data collected during the field experiment held during the summer of 2016.
Impacts
What was accomplished under these goals?
IMPACT:In order to understand why SMOS and SMAP--satellites that provide soil moisture measurements--are too dry, we investigated several aspects of the soil moisture retrieval process. We were able to rule out several possibilities for why SMOS and SMAP have a dry bias and began exploring ways to reduce dry bias. In addition, by participating in a USDA/NASA world-wide field experiment, which included a watershed in the South Fork Iowa River, we provided data that will help validate the accuracy of agronomic measurements taken by flying a microwave radiometer over land. Our investigations are contributing to the creation of tools that will facilitate better weather and climate forecasts for the Midwest United States. Objective 1... Assess the validity of observations of near-surface soil moisture, crops, and soil roughness made by microwave remote sensing instruments on current Earth-orbiting satellites at scales similar to Iowa counties (40 km) and townships (10 km). The European Space Agency launched the Soil Moisture and Ocean Salinity (SMOS) satellite in late 2009. In 2015 NASA launched the Soil Moisture Active Passive (SMAP) satellite. Both satellites carry passive remote sensing instruments that operate at a frequency of 1.4 GHz (wavelength of 21 cm) which is in the microwave region of the electromagnetic spectrum. At microwave wavelengths, vegetation is semi-transparent and SMOS and SMAP can "see" radiation that originates from beneath the soil surface. This is in stark contrast to visible and infrared satellites that are only sensitive to the condition of either the first millimeter (or less) of the soil surface, or the very top of the vegetation canopy, if present. Furthermore, radiation at 1.4 GHz emitted by Earth's terrestrial surface changes dramatically with water content. Hence the SMOS and SMAP satellites are able to produce an estimate of the water content of the first several centimeters of the soil, called the near-surface soil moisture. These data are available at a spatial resolution close to the size of a typical Midwestern county (about 40 by 40 km), and at a temporal frequency of about every-other day. We have found that the near-surface soil moisture observed by both SMOS and SMAP is "too dry" in the US Midwest. In other words, the soil moisture reported by SMOS and SMAP is about 0.07 m^3 m^{-3} lower than the "true" value of soil moisture. (Soil moisture ranges from about 0.05 to 0.45 m^3 m^{-3}.) The goal of SMOS and SMAP is to measure soil moisture with a mean error (or bias) of 0.00 m^3 m^{-3}. Here we define the "true" value of soil moisture to be the soil moisture measured by a network of in situ soil sensors within the watershed of the South Fork Iowa River in central Iowa. This network consists of 20 stations at which soil moisture is measured at a depth into the soil consistent with what SMOS and SMAP are able to "see." Our findings in Iowa are consistent with what has been found by other researchers in other agricultural areas. In order to understand why SMOS and SMAP are too dry, we investigated several aspects of the soil moisture retrieval process. We found that the error is not due to SMOS data that has been corrupted by "radio frequency interference" originating from other electrical systems (like airport radars and wireless electronics) that emit microwave radiation. (SMAP has methods that remove potential interference.) It is also not caused by numerical instabilities in the algorithms used by SMOS. We found that it is not caused by poor estimates of surface temperature used in the satellite algorithms. These temperature estimates are an essential piece of ancillary data, and come from weather forecasting models. We found that it is not due to inaccurate maps of soil properties that had at one time been used by the SMOS mission. In fact, updated soil maps made the dry bias worse. It is also not caused by unrealistic assumptions made concerning the type of vegetation (annual crops of mainly corn and soybean) that covers the soil surface in this region. When we changed parameters in the soil moisture retrieval algorithm to better represent this type of vegetation, the dry bias also increased. Finally, we found that adjusting the parameter in the SMOS algorithm that accounts for the effect of soil surface roughness, to make the soil surface "rougher," would decrease the dry bias. However, increasing soil surface roughness had a detrimental effect of decreasing the overall sensitivity of SMOS and SMAP to soil moisture. Our research group also facilitated, and participated in, a large field experiment held in the watershed of the South Fork Iowa River. This experiment, called SMAPVEX16-IA, was funded by both the USDA and NASA and involved approximately 50 scientists from around the US and the world. A NASA airplane flew a microwave radiometer over the watershed on approximately 20 days during the summer. At the same time microwave radiometers on towers observed plots of corn and soybean. Our group helped make in situ measurements of soil moisture, vegetation, and soil surface roughness. Objective 2... Determine the viability of using the intensity of free neutrons just above Earth's surface to monitor changes in soil water and crops at the scale of an individual field (0.5 km). Nothing to report. Objective 3... Evaluate the ability of agro-ecosystem models operating within weather and climate models to simulate the evolution of soil moisture and crops and their effect on the exchange of water and energy between Earth's land surface and atmosphere. Nothing to report.
Publications
- Type:
Journal Articles
Status:
Published
Year Published:
2016
Citation:
Hornbuckle, B. K., J. C. Patton, A. VanLoocke, A. E. Suyker, M. C. Roby, V. A. Walker, E. R. Iyer, D. E. Herzmann, and E. A. Endacott, SMOS Optical Thickness Changes in Response to the Growth and Development of Crops, Crop Management, and Weather, Remote Sensing of Environment, doi:10.1016/j.rse.2016.02.0432016, 2016.
- Type:
Conference Papers and Presentations
Status:
Accepted
Year Published:
2016
Citation:
Hornbuckle, B. K., J. C. Patton, A. VanLoocke, A. E. Suyker, M. C. Roby, V. A. Walker, E. R. Iyer, D. E. Herzmann, and E. A. Endacott, SMOS Optical Thickness Changes in Response to the Growth and Development of Crops, Crop Management, and Weather, 14th Specialist Meeting on Miocrowave Radiometry and Remote Sensing of the Environment in Espoo, Finland, 2016.
- Type:
Conference Papers and Presentations
Status:
Accepted
Year Published:
2016
Citation:
Hornbuckle, B. K., W. E. Eichinger, V. Wallace, E. Yildirim, and V. A. Walker, Spatial and Temporal Variability of Soil Surface Roughness during SMAPVEX16-IA, Soil Science Society of America Annual Meeting, Phoenix, AZ, 2016.
|
Progress 10/01/14 to 09/30/15
Outputs
Target Audience:The target audience is the scientific community that is engaged in improving predictions of future weather and climate. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?Five students (3 graduate, 2 undergraduate) received scientific training in the process of conducting the research described above. One graduate student has subsequently received a Ph.D. in agricultural meteorology. The other two are currently working toward an M.S. in agricultural meteorology. One of the undergraduates completed a senior thesis research project on this topic and is now a graduate student in meteorology at another institution. The other undergraduate became involved through the freshman honors program and continued their work via an independent study project. How have the results been disseminated to communities of interest?The research described above was presented by the senior undergraduate at a university-wide undergraduate research symposium. The PD presented the preliminary results of this work at a NASA meeting. The topic of this NASA meeting was the validation of a new satellite instrument that is similar to SMOS, the Soil Moisture Active Passive (SMAP) mission. A journal paper detailing this work is currently under review for inclusion in a special issue of Remote Sensing of Environment on "ESA's Soil Moisture and Ocean Salinity Mission - Achievements and Novel Applications after Five Years in Orbit." What do you plan to do during the next reporting period to accomplish the goals?We plan to continue to pursue Objective 1 in the coming year. We have also found that both SMOS and SMAP are "too dry" by about 10% in the Corn Belt. That is, SMOS and SMAP observations of near-surface soil moisture are lower than what is reported by an in situ soil moisture monitoring network in the watershed of the South Fork Iowa River that is maintained by the USDA ARS. We plan to work with colleagues from the ARS, NASA, and other academic institutions to determine why the satellite soil moisture is too dry. This work will focus on the execution of a large field experiment that will be held during the summer of 2016 in the South Fork. In the near term, a graduate student will report on several hypotheses regarding the SMOS dry bias when she defends her masters thesis in the spring of 2016.
Impacts
What was accomplished under these goals?
My long-term goal is to develop satellite observations of Earth's land surface that will facilitate better weather and climate forecasts for the Corn Belt of the Midwest United States. Satellites orbiting Earth capture electromagnetic radiation that is either emitted by, or scattered by, Earth's surface and atmosphere. The measured electromagnetic radiation can be interpreted using mathematical models to provide information about many important geophysical variables such as water vapor in the atmosphere, the fraction of cloud cover, changes in vegetation, and the surface albedo. My focus is on the use of satellite observations to observe changes in liquid water stored by vegetation and the soil. The movement of water in and out of these two reservoirs is an important part of the global water cycle and also affects how the energy from the Sun absorbed by the land surface is distributed within the Earth System. We can improve weather and climate forecasts as we improve our understanding of the global water and energy cycles. The overall impact of my work will be improved long-term profitability of Corn Belt agriculture. This improved long-term profitability will be the result of decisions made by farmers using better weather and climate forecasts. "Long-term profitability" is emphasized because the decisions that will be aided by better forecasts will be relevant to both near-term profits (yields) but also long-term profits (conservation of natural resources). In the near-term, better forecasts of precipitation, for example, can be used by a farmer to determine when to plant a crop. A question a farmer might ask in this situation is: "The soil is a little too wet for optimum planting conditions (I might risk soil compaction that may result in yield loss) but will I have a window of drier weather soon enough in the near future for me to get the crop in before it is too late in the growing season?" In the long-term better forecasts can be used to decide on conservation management strategies. A question in this situation is: "Will the spring season continue to be wetter in the future, and if so, should I start using a cover crop to reduce soil erosion and nutrient loss, and to increase transpiration in order to reduce soil water content?" My long-term goal can be subdivided into three more specific objectives. Objective 1: Assess the validity of observations of near-surface soil moisture, crops, and soil roughness made by microwave remote sensing instruments on current Earth-orbiting satellites at scales similar to Iowa counties (40 km) and townships (10 km). The European Space Agency launched the Soil Moisture and Ocean Salinity (SMOS) satellite in late 2009. This satellite carries a passive remote sensing instrument that operates at a frequency of 1.4 GHz (wavelength of 21 cm) which is in the microwave region of the electromagnetic spectrum. At microwave wavelengths, vegetation is semi-transparent and SMOS can "see" radiation that originates from beneath the soil surface. This is in stark contrast to visible and infrared satellites that are only sensitive to the condition of either the first millimeter (or less) of the soil surface, or the very top of the vegetation canopy, if present. The SMOS satellite produces two main types of data. The first type is an estimate of the water content of the first several centimeters of the soil, called the near-surface soil moisture. The second type is related to the amount of vegetation that covers the soil surface, called the vegetation optical thickness. These data are available at a spatial resolution close to the size of a typical Midwestern county (about 40 by 40 km), and at a temporal frequency of about every-other day. Theoretically, the vegetation optical thickness is a measure of the mass of water stored within vegetation tissue per ground area. However, several scientific investigations have not been able to confirm this expected behavior. We have found that in the Corn Belt the vegetation optical thickness increases as crops grow and decreases as plants senesce and are harvested. We have also found that changes in soil surface roughness due to tillage, planting, and rainfall "confuse" SMOS and are interpreted as changes in vegetation optical thickness. Consequently we see that the vegetation optical thickness increases after spring tillage and planting, decreases after subsequent rainfall, increases as crops grow, decreases before harvest, and then increases again in response to fall tillage. We have now determined that SMOS vegetation optical thickness reaches its peak (maximum) value in the Corn Belt during the third reproductive stage of corn (R3, milk), which occurs about 1000 deg C day growing degree days after planting. Consequently, SMOS could be used to monitor both the growth as well as the development of crops in the Corn Belt. This is significant because SMOS satellite measurements could then be used verify the predictions of crop models which attempt to predict crop yield. In order to accurately predict crop yield, crop models must first correctly determine the timing of important stages of development, like the grain-filling period for corn. We suggest that SMOS observations be used to observe the timing of the third reproductive stage of corn, which then could be used to assess whether crop models are accurately predicting the timing of crop development across the Corn Belt. Crop models can be used by farmers to make decisions, which can result in increased profitability in both the short- and long-term. Crop models are also beginning to be used in weather and climate models in order to better replicate the movement of water and energy between the land surface and the atmosphere. This will result in better weather and climate forecasts. We did not collect any new data in this work: instead we used data collected by other scientists. Objective 2: Determine the viability of using the intensity of free neutrons just above Earth's surface to monitor changes in soil water and crops at the scale of an individual field (0.5 km). Nothing was accomplished for this objective. Objective 3: Evaluate the ability of agro-ecosystem models operating within weather and climate models to simulate the evolution of soil moisture and crops and their effect on the exchange of water and energy between Earth's land surface and atmosphere. Nothing was accomplished for this objective.
Publications
- Type:
Journal Articles
Status:
Published
Year Published:
2015
Citation:
Rondinelli, W. J., B. K. Hornbuckle, J. C. Patton, M. H. Cosh, V. A. Walker, B. D. Carr, and S. D. Logsdon. Different Rates of Soil Drying After Rainfall are Observed by the SMOS Satellite and the South Fork In Situ Soil Moisture Network. Journal of Hydrometeorology. doi:10.1175/JHM-D-14-0137.1, 2015.
- Type:
Journal Articles
Status:
Published
Year Published:
2015
Citation:
Dogusgen, C. and B. K. Hornbuckle. A Nonlinear Relationship between Terrestrial Microwave Emission at 1.4 GHz and Soil Moisture Caused by Ponding of Water. Remote Sensing Letters. doi:10.1080/2150704X.2015.1029088, 2015.
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Progress 03/12/14 to 09/30/14
Outputs
Target Audience: The scientific community engaged in improving predictions of weather and climate. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided? Four students received scientific training in the process of conducting the research described above. One of these students was an undergraduate and the other three were graduate students. The undergraduate completed a senior thesis project on this topic, received a bachelors degree in meteorology, and is currently employed by the USDA-ARS. One of the graduate students received a masters degree in agricultural meteorology. Another graduate student received a doctor of philosophy degree in agricultural meteorology. The other graduate student is currently pursuing a masters degree in agricultural meteorology. How have the results been disseminated to communities of interest? The research described above was presented by the undergraduate student at Iowa State University in both the meteorology department's senior thesis seminar and at a university-wide undergraduate research symposium. The undergraduate also presented this research at the International Geoscience and Remote Sensing Symposium (IGARSS), the premier international satellite remote sensing conference, in Quebec City, Canada. A journal paper describing this research has been accepted by the Journal of Hydrometeorology, is available electronically, and will be published in a special issue on results from the Iowa Flood Studies field experiment. What do you plan to do during the next reporting period to accomplish the goals? We plan to pursue research related to both Objective 1 and Objective 2 (using free neutrons to monitor soil moisture) in the coming year. In terms of Objective 1, we plan to validate SMOS observations of vegetation by using USDA estimates of crop yield in Iowa. We also plan to compare SMOS observations of vegetation with the estimates of vegetation that will be used by NASA's SMAP (Soil Moisture Active Passive) microwave remote sensing satellite to retrieve near-surface soil moisture. In terms of Objective 2, we plan to determine how observations of soil moisture made with cosmic-ray neutron detectors are affected by the presence of vegetation, specifically crops of corn and soybean.
Impacts
What was accomplished under these goals?
Work during this reporting period directly addressed Objective 1: assess the validity of observations of microwave remote sensing satellites. We determined that Soil Moisture Ocean Salinity (SMOS) satellite observations of near-surface soil moisture are lower than in situ measurements made by a network of soil moisture sensors in the state of Iowa, an important state in the U.S. Corn Belt. In other words, SMOS is "too dry" as compared to in situ measurements. This work is important because satellite near-surface soil moisture observations have the potential for improving short-term weather forecasts, seasonal forecasts of drought and extreme wetness, predictions of soil erosion from agricultural fields, predictions of agricultural productivity, and predictions of the extent and severity of flooding. However, before these satellite observations can be used to their fullest potential, their statistical characteristics must be determined. In other words, they must be validated. SMOS is an acronym for Soil Moisture Ocean Salinity. It is a European Space Agency satellite that was launched in late 2009. SMOS carries a passive microwave remote sensing instrument that operates at L-band (wavelength of 21 cm). The spatial resolution of SMOS is close to 40 km, which is approximately the size of a typical Iowa county. Its temporal resolution is about every-other day at Iowa's latitude. The in situ soil moisture measurements used to assess SMOS come from a 20-site network developed by the USDA-ARS in the watershed of the South Fork Iowa River in central Iowa. The spatial extent of this network matches the spatial resolution of the SMOS observations. We have begun to determine why SMOS is dry as compared to the network. SMOS has also been observed to be dry in other agricultural regions in the U.S. Some scientists have hypothesized that this is because the soil moisture sensors used by the in situ networks are buried a depth below the soil surface, will satellites like SMOS observe a layer of soil immediately at the soil surface. We found that this discrepancy between the layer of soil observed by in situ networks and the layer of soil observed by SMOS is not responsible for the difference between the satellite and network in the South Fork Iowa River watershed but can explain the random error (characterized by the root-mean-square error or RMSE) in the relationship between SMOS and the network. For example, SMOS soil moisture is higher than the network immediately after precipitation, but the rate of soil drying observed by SMOS is also higher than the network. Over long periods of time, however, there is no bias between these two soil layers. This was determined by using both a mini-network of in situ soil moisture sensors buried at two depths corresponding to these two layers in a single agricultural field and through the use of a land surface model called Agro-IBIS.
Publications
- Type:
Journal Articles
Status:
Published
Year Published:
2015
Citation:
Rowlandson, T., M. Gleason, P. Sentelhas, T. Gillespie, C. Thomas, and B. Hornbuckle, Reconsidering Leaf Wetness Duration Determination for Plant Disease Management, Plant Disease, doi:10.1094/PDIS-05-14-0529-FE, 2015.
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