Progress 07/15/17 to 07/14/20
Outputs
Target Audience:The target audience of this study includes scientists and researchers who are interested in agricultural monitoring withremote sensing. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?This project provided trainning and professional development opportunites to graduate students, postdoctral researchassociates, and undergraduate interns. How have the results been disseminated to communities of interest?The outcomes and results of this project have been disseminated to the communities through journal publications, conferenceand workshop presentations, and Extension activities. What do you plan to do during the next reporting period to accomplish the goals?
Nothing Reported
Impacts
What was accomplished under these goals?
Achievement #1: Continuous Measurements of Canopy-Level Sun-Induced Chlorophyll Fluorescence for Inferring Photosynthesis Over Croplands Over the past three years, we have established four SIF towers over the U.S. Midwestern agroecosystem (two in Illinois andtwo in Nebraska) and collected twelve site-year continuous canopy SIF observations for either corn, soybean, or Miscanthus.We used the Fluospec2 system to collect signals required for SIF retrieval and several associated vegetation indices. SIF wasretrieved by spectral fitting method. With the collected data, we have make the following progresses using SIF to inferdynamics in photosynthesis: 1. Sun-induced fluorescence, photosynthesis, and light use efficiency of a soybean field We confirmed that at the canopy scale a strong positive relationship between photosynthesis (measured as gross primaryproduction, GPP) and SIF is dominated by an even stronger relationship between SIF and absorbed photosynthetically activeradiation (APAR). In the meantime, we also found that under stable sunny conditions the soybean field exhibited a clearpositive SIF yield (SIFy) and APAR relationship and a weak negative light use efficiency (LUE) and SIFy relationship.These patterns, which might be related to the high photosynthetic capacity of soybean and the unique structural dynamics ofsunlit and shaded leaves of soybean field, added a different scenario into the variety of SIFy:APAR and LUE:SIFy relationshipsthat have been published previously from some wild ecosystems and modeling results. Our study suggested that SIF mightalso contain the information associated with some fundamental differences between ecosystems in addition to the APARinformation. 2. Attributing the drivers on the relationship between canopy photosynthesis and far-red sun-induced fluorescence at different temporal scales The continuous data collected in the maize fields in 2017 confirmed the patterns that we observed in the soybean field in2016, that is, the positive SIFy:APAR relationship and the negative LUE:SIFy relationship. The negative LUE:SIFy relationshipwas even stronger than the one in the soybean field.We found that for individual growth stages when canopy structure and chlorophyll content were relatively stable, both GPPand SIF were strongly controlled by PARin, while LUE (contributed 9.7% to GPP variability at the rainfed site) and SIFy (19.6% to SIF variability) had much lower contributions to the overall GPP:SIF relationship. Within a specific growth stage,LUE and SIFy either had a slightly negative or no clear relationship, which explained some deviations from the linear GPP:SIFrelationship. At the seasonal scale, we found the contribution of LUE (47.7% at the rainfed site) to the GPP variability as wellas the contribution of SIFy (51.9%) to the SIF variability significantly increased, and was comparable to the PARin'scontribution; and at the seasonal scale, the LUE:SIFy relationship also showed a strong linear relationship, whichstrengthened the linear GPP:SIF relationship. Both maize sites showed similar patterns. We then proposed a framework toapply the seasonal-scale LUE:SIFy relationship to estimate LUE at individual stages, and thus to improve the GPP estimationfrom SIF. This significant improvement indicates an additional SIF potential for inferring photosynthesis. Achievement #2: Using NIRv,rad as a new proxy of GPP at regional scale Substantial uncertainty exists in daily and sub-daily gross primary production (GPP) estimation, which dampens accuratemonitoring of the global carbon cycle. In this project, we find that near-infrared radiance of vegetation (NIRv,Rad), defined asthe product of observed NIR radiance and normalized difference vegetation index (NDVI), can accurately estimate corn andsoybean GPP at daily and half-hourly time scales, benchmarked with multi-year tower-based GPP at three sites with differentenvironmental and irrigation conditions. Overall, NIRv,Rad explains 84% and 78% variations of half-hourly GPP for corn andsoybean, respectively, outperforming NIR reflectance of vegetation (NIRv,Ref), enhanced vegetation index (EVI), and far-redsolar-induced fluorescence (SIF760). The strong linear relationship between NIRv,Rad and absorbed photosynthetically activeradiation by green leaves (APARgreen), and that between APARgreen and GPP, explain the good NIRv,Rad-GPP relationship. TheNIRv,Rad-GPP relationship is robust and consistent across sites. The scalability and simplicity of NIRv,Rad indicate a greatpotential to estimate daily or sub-daily GPP from high-resolution and/or long-term satellite remote sensing data. Achievement #3: Using satellite-based SIF to estimate crop production We examined how well solar-nduced chlorophyll fluorescence (SIF) can inform crop productivity across the United States.Based on tower-level observations and process-based modeling, we find highly linear gross primary production (GPP):SIFrelationships for C4 crops, while C3 crops show some saturation of GPP at high light when SIF continues to increase. C4crops yield higher GPP:SIF ratios (30-50%) primarily because SIF is most sensitive to the light reactions (does not account forphotorespiration). Scaling to the satellite, we compare SIF from the TROPOspheric Monitoring Instrument (TROPOMI) againsttower-derived GPP and county-level crop statistics. Temporally, TROPOMI SIF strongly agrees with GPP observationsupscaled across a corn and soybean dominated cropland (R2 = 0.89). Spatially, county-level TROPOMI SIF correlates withcrop productivity (R2 = 0.72; 0.86 when accounting for planted area and C3/C4 contributions), highlighting the potential of SIFfor reliable crop monitoring. We assessed the benefits of using three satellite-based SIF products in yield prediction for maize and soybean in the U.S.Midwest: Gap-filled SIF from Orbiting Carbon Observatory 2 (OCO-2), new SIF retrievals from the TROPOspheric MonitoringInstrument (TROPOMI), and the coarse-resolution SIF retrievals from the Global Ozone Monitoring Experiment-2 (GOME-2).The yield prediction performances of using SIF data were benchmarked with those using satellite-based vegetation indices(VIs), including normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and near-infraredreflectance of vegetation (NIRv), and land surface temperature (LST). Five machine-learning algorithms were used to buildyield prediction models with both remote-sensing-only and climate-remote-sensing-combined variables. We found that highresolutionSIF products from OCO-2 and TROPOMI outperformed coarse-resolution GOME-2 SIF product in crop yieldprediction. Using high-resolution SIF products gave the best forward predictions for both maize and soybean yields in 2018, indicating the great potential of using satellite-based high-resolution SIF products for crop yield prediction. However, usingcurrently available high-resolution SIF products did not guarantee consistently better yield prediction performances than usingother satellite-based remote sensing variables in all the evaluated cases. The relative performances of using different remotesensing variables in yield prediction depended on crop types (maize or soybean), out-of-sample testing methods (five-foldcross-validation or forward), and record length of training data. We also found that using NIRv could generally lead to betteryield prediction performance than using NDVI, EVI, or LST, and using NIRv could achieve similar or even better yieldprediction performance than using OCO-2 or TROPOMI SIF products. We concluded that satellite-based SIF products couldbe beneficial in crop yield prediction with more high-resolution and good-quality SIF products accumulated in the future.
Publications
- Type:
Journal Articles
Status:
Published
Year Published:
2020
Citation:
He, L., Magney, T., Dutta, D., Yin, Y., Kohler, P., Grossmann, K., Stutz, J., Dold, C., Hatfield, J., Guan, K., Peng, B. and Frankenberg, C. 2020. From the ground to space: Using solar-induced fluorescence (SIF) to estimate crop productivity. Geophysical Research Letters, 47(7), e2020GL087474.
- Type:
Journal Articles
Status:
Published
Year Published:
2020
Citation:
Peng, B., Guan, K., Zhou, W., Jiang, C., Frankenberg, C., Sun, Y., He, L. and Kohler, P. 2020. Assessing the benefit of satellite-based solar-induced chlorophyll fluorescence in crop yield prediction. Volume 90, International Journal of Applied Earth Observation and Geoinformation.
- Type:
Journal Articles
Status:
Published
Year Published:
2020
Citation:
Wang, S., Guan, K., Wang, Z., Ainsworth, E.A., Zheng, T., Townsend, P.A., Li, K., Moller, C., Wu, G. and Jiang, C. 2020. Unique contributions of chlorophyll and nitrogen to predict crop photosynthetic capacity from leaf spectroscopy. Journal of Experimental Botany.
- Type:
Journal Articles
Status:
Published
Year Published:
2020
Citation:
Miao, G., Guan, K., Suyker, A.E., Yang, X., Arkebauer, T.J., Walter-Shea, E.A., Kimm, H., Hmimina, G.Y., Gamon, J.A., Franz, T.E., Frankenberg, C., Berry, J.A. and Wu, G. 2020. Varying contributions of drivers to the relationship between
canopy photosynthesis and far-red sun-induced fluorescence for two maize sites at different temporal scales. Journal of Geophysical Research - Biogeosciences, 125, e2019JG005051.
- Type:
Journal Articles
Status:
Published
Year Published:
2020
Citation:
Li, Y., Guan, K., Peng, B., Franz, T.E., Wardlow, B. and Pan, M. 2020. Quantifying irrigation cooling benefits to maize yield in the U.S. Midwest. Global Change Biology, 26(5), 3065-3078.
- Type:
Journal Articles
Status:
Published
Year Published:
2019
Citation:
Jiang, C., Guan, K., Pan, M., Ryu, Y., Peng, B. and Wang, S. 2019. BESS-STAIR: A framework to estimate daily, 30-meter, and allweather crop evapotranspiration using multi-source satellite data for the U.S. Corn Belt. Hydrology and Earth System Sciences Discussions, 1-36.
- Type:
Journal Articles
Status:
Published
Year Published:
2019
Citation:
Li, Y., Guan, K., Yu, A., Peng, B., Zhao, L., Li, B. and Peng, J. 2019. Toward building a transparent statistical model for improving crop yield prediction: Modeling rainfed corn in the U.S. Field Crops Research, 234, 55-65.
- Type:
Journal Articles
Status:
Published
Year Published:
2019
Citation:
Li, Y., Guan, K., Schnitkey, G.D., DeLucia, E. and Peng, B. 2019. Excessive rainfall leads to maize yield loss of a comparable magnitude to extreme drought in the United States. Global change biology, 25(7), 2325-2337.
- Type:
Journal Articles
Status:
Published
Year Published:
2018
Citation:
Yang, X., Shi, H., Stovall, A., Guan, K., Miao, G., Zhang, Y. and Lee, J.E. 2018. FluoSpec 2an automated field spectroscopy system to monitor canopy solar-induced fluorescence. Sensors, 18(7), 2063.
- Type:
Journal Articles
Status:
Published
Year Published:
2018
Citation:
Peng, B., Guan, K., Pan, M. and Li, Y. 2018. Benefits of seasonal climate prediction and satellite data for forecasting U.S. maize yield. Geophysical Research Letters, 45(18), 9662-9671.
|
Progress 07/15/18 to 07/14/19
Outputs
Target Audience:The target audience for our research is scientists investigating photosynthesis,crop productivity, and remote sensing application in agriculture. Other audiences include farmers and members of agricultural industries. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?The student and postdoc working on this project attendedprofessonal trainning workshops (such as those for LICOR 6800 and hyperspectral sensors) and academic meetings such as the American Geophysical Union. How have the results been disseminated to communities of interest?Research results havebeen disseminatedthrough keynote talks, conference presentations, and journal papers. What do you plan to do during the next reporting period to accomplish the goals?(1) We will continue to maintain SIF towers in Nebraska in 2019. (2) We will synthesize multi-year tower-based SIF observations and quanfity the GPP-SIF relationship. (3) We will test how SIF improves corn and soybean yield prediction in the U.S.
Impacts
What was accomplished under these goals?
Milestone #1: Continuous Measurements of Canopy-Level Sun-Induced Chlorophyll Fluorescence for Inferring Photosynthesis over Croplands. Over the past three years, we have established 4 SIF towers over the U.S. Midwestern agroecosystem (2 in Illinois and 2 in Nebraska) and collected 8 site-year continuous canopy SIF observations for either corn or soybean.Continuous SIF measurements were continuingly conducted in crop fields in the U.S. Corn Belt in 2017. Two FluoSpec2 systems were deployed in a rain-fed and an irrigated maize (Zea mays) field in eastern Nebraska at the University of Nebraska Agricultural and Research Development Center, Mead, Nebraska to explore the potential of SIF as an indicator of crop stress. SIF data, paired with eddy-covariance flux measurements, were collected from 7/15 to 10/15/2017, covering from tasseling stage to the end of the growing season. The continuous data collected in the maize fields in 2017 confirmed the patterns that we observed in the soybean field in 2016, that is, the positive SIFy:APAR relationship and the negative LUE:SIFy relationship. The negative LUE:SIFy relationship was even stronger than the one in the soybean field. We found that for individual growth stages when canopy structure and chlorophyll content were relatively stable, both GPP and SIF were strongly controlled by PARin, while LUE (contributed 9.7% to GPP variability at the rainfed site) and SIFy (19.6% to SIF variability) had much lower contributions to the overall GPP:SIF relationship. Within a specific growth stage, LUE and SIFy either had a slightly negative or no clear relationship, which explained some deviations from the linear GPP:SIF relationship. At the seasonal scale, we found the contribution of LUE (47.7% at the rainfed site) to the GPP variability as well as the contribution of SIFy (51.9%) to the SIF variability significantly increased, and was comparable to the PARin's contribution; and at the seasonal scale, the LUE:SIFy relationship also showed a strong linear relationship, which strengthened the linear GPP:SIF relationship. Both maize sites showed similar patterns. We then proposed a framework to apply the seasonal-scale LUE:SIFy relationship to estimate LUE at individual stages, and thus to improve the GPP estimation from SIF. This significant improvement indicates an additional SIF potential for inferring photosynthesis. Milestone #2: Solar-induced chlorophyll fluorescence (SIF) measured from space has been increasingly used to approximate plant photosynthesis at the regional and global scales. SIF yield (SIFyield), defined as the emitted SIF per photon absorbed, which together with the absorbed photosynthetically active radiation (APAR), is crucial in driving the spatio-temporal variability of SIF. While strong linkages between SIFyield and plant physiological responses have been suggested,the spatial and temporal variability of SIFyield remains largely unclear, which limits our understanding of SIF and SIF's ability to estimate photosynthesis. In this study, we utilize the satellite SIF data with high spatial resolutions from two new satellites, OCO-2 and TROPOMI, along with multiple other datasets, to study SIFyield across space, time, and different vegetation types in the U.S. Midwest during the crop growing season (May to September) from 2015-2018. We find that SIFyield of cropland is larger than non-cropland during the peak season (June-August). However, the difference of SIFyield between corn (C4 crop) and soybean (C3 crop) is weak, implying a minor role of the photosynthesis pathways in determining SIF. SIFyield of corn, soybean, forest, and grass/pasture show clear seasonal and spatial patterns. The spatial variability of precipitation and temperature during the growing season can explain the overall spatial pattern of SIFyield. The difference of SIFyield among different forest groups or between grass and pasture also contributes to the spatial pattern of SIFyield over forest and grass/pasture areas. Our results reveal significant spatio-temporal and inter-/intra-vegetation type variabilities of SIFyield which improves the understanding in the variability of SIF and our ability for estimating GPP using SIF over large spatial scales. Milestone #3: The shared and unique values of optical, fluorescence, thermal and microwave satellite data for estimating large-scale crop yields. We conducted one of the first attempts at synergizing multiple satellite data spanning a diverse spectral range, including visible, near-infrared, thermal and microwave, into one framework to estimate crop yield for the U.S. Corn Belt, one of the world's most important food producing regions. Specifically, we include MODIS Enhanced VI (EVI), estimated Gross Primary Production based on GOME-2 Solar-induced fluorescence (SIF-GPP), thermal-based ALEXI Evapotranspiration (ET), QuikSCAT Ku-band radar backscatter, and AMSR-E/2 X-band passive microwave Vegetation Optical Depth (VOD) in this study, benchmarked on USDA county-level crop yield statistics. We use Partial Least Square Regression (PLSR) to distinguish commonly shared and unique individual information from the various satellite data and other ancillary climate information for crop yield estimation. We find that most of the satellite derived metrics (e.g. SIF-GPP, radar backscatter, EVI, VOD, ALEXI-ET) share common information related to above-ground crop biomass. For this shared information, the SIF-GPP and backscatter data contain almost the same amount of information as EVI at the county scale. When removing the above shared component from all of the satellite data, we find that EVI and SIF-GPP does not provide much extra information; instead, Ku-band backscatter, thermal-based ALEXI-ET, and X-band VOD provide information that improves overall crop yield predictive skill. In particular, Ku-band backscatter and associated differences between morning and afternoon overpasses contribute unique information on crop growth and environmental stress. Overall, using satellite data from various spectral bands significantly improves regional crop yield predictions. The additional use of ancillary climate data (e.g. precipitation and temperature) further improves model skill, in part because the crop reproductive stage related to harvest index is highly sensitive to environmental stresses that are not fully captured by the satellite data used in our study. We conclude that using satellite data across various spectral ranges can improve monitoring of large-scale crop growth and yield beyond what can be achieved with current individual sensors. These results also inform the synergistic use and development of current and next generation satellite missions, including NASA RapidSCAT, ECOSTRESS, SMAP, and OCO-2 for agricultural applications.
Publications
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2018
Citation:
Guan, K. 2018. Harnessing Remote Sensing Big Data for Predicting Food Production. Keynote invited talk, in OPTIMISE 2018 Final Meeting, Sofia, Bulgaria.
- Type:
Journal Articles
Status:
Published
Year Published:
2018
Citation:
Miao, G., Guan, K., Yang, X., Bernacchi, C.J., Berry, J. A., Delucia, E.H., Wu, J., Moore, C.E., Meacham, K., Cai, Y., Peng, B., Kimm, H. and Masters, M.D. 2018. Sun-Induced Chlorophyll Fluorescence, Photosynthesis, and Light Use Efficiency of a Soybean Field From Seasonally Continuous Measurements. Journal of Geophysical Research - Biogeosciences. Doi: 10.1002/2017JG004180.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2017
Citation:
Guan, K., Wu, J., Kimball, J.S., Anderson, M.C., Frolking, S., Li, B., Hain, C.R. and Lobell, D.B. 2017. The Shared and Unique Values of Optical, Fluorescence, Thermal and Microwave Satellite Data for Estimating Large-Scale Crop Yields. Remote Sensing of Environment, 199, 333-349.
|
Progress 07/15/17 to 07/14/18
Outputs
Target Audience:The target audience for our research is (1) biological scientists who study crop responses to global change, (2) remote sensing scientists who study the application of remote sensing technologies in monitoring crops, and (3) other potential audiences such as farmers, crops industries, and policy makers. Changes/Problems:In June of 2017, a severe windstorm in Nebraska destroyed the field scaffolds in the two maize fields and damaged all the fiber optics of the two Fluospec2 systems. We lost one month of data (mid-June to mid-July) and had to replace all the fiber optics at a cost of over five thousand dollars. What opportunities for training and professional development has the project provided?This project provided full support for a postdoctoral researcher and partial supportfor one doctoral student and one undergraduate summer intern. The PIs supervised the postdoctoral researcher and the doctoral student indesigning and conducting field experiments and data analysis. The postdoctoral researcher and the doctoral student trained the undergraduate intern to conduct field measurements and gain research experiences in agricultural and climate change research.The summer experiencealso helped the undergraduate student win the 2018 College Student Awards. How have the results been disseminated to communities of interest?Preliminary results from the 2017 growing season were presented at the 2017 American Geophysical Union Fall Meeting in December of2017. What do you plan to do during the next reporting period to accomplish the goals?We will continue the canopy level SIF measurements in rain-fed and irrigated crop fields in 2018 and the study crops will be soybean. While we will conduct crop stress research on soybean similar to the research on maize, with the data collected from maize fields in 2017 and soybean fields in 2018, we will compare the plant responses to stress between C3 (soybean) and C4 (maize) crops.
Impacts
What was accomplished under these goals?
Objective 1: Investigate the link between photosynthesis (measured as gross primary production, GPP) and sun-induced chlorophyll fluorescence (SIF) of crops. Continuous canopy level SIF measurements were conducted in maize (Zea mays) fields in the U.S. Corn Belt in 2017. Two FluoSpec2 systems were deployed in a rain-fed and an irrigated maize field in eastern Nebraska on the University of Nebraska, Agricultural and Research Development Center in Mead, Nebraska.One FluoSpec2 system were deployed in a rain-fed maize field in the Energy Farm of University of Illinois at Urbana-Champaign. The continuous data collected in the maize fields in 2017 confirmed the patterns that were observed in a soybean field in Illinois in 2016 (Miao et al. 2018), that is, the positive relationship between SIF yield (SIFy) and absorbed photosynthetically active radiation (APAR) and the negative relationship between light use efficiency (LUE) and SIFy. Objective 2: Explore the potential of SIF as an indicator of crop stress. Significant differences in GPP and SIF were observed between the rain-fed and the irrigated fields in Mead, Nebraska in the midday of sunny days during the peak growing season, while no significant differences in the morning and afternoon of sunny days or on cloudy days. These simultaneous changes in SIF and GPP implied that SIF was able to capture the change in GPP resulting from short-term stress. Reference: Miao, G., Guan, K., Yang, X., Bernacchi, C.J., Berry, J. A., Delucia, E.H., Wu, J., Moore, C.E., Meacham, K., Cai, Y., Peng, B., Kimm, H., Masters, M.D. 2018. Sun-Induced Chlorophyll Fluorescence, Photosynthesis, and Light Use Efficiency of a Soybean Field from Seasonally Continuous Measurements. Journal of Geophysical Research - Biogeosciences, 123, 610-623 DOI: 10.1002/2017JG004180).
Publications
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2017
Citation:
Miao, G., Guan, K., Suyker, A.E., Bernacchi, C., Yang, X., Gamon, J.A., Berry, J.A., Delucia, E.H., Trenton, F.E., Arkebbauer, T.J., Zygielbaum, A.I., Walter-Shea, E.A., Zhang, Y., Kimm, H., Hmimina, G.Y. and Moore, C.E. 2017. The potential of sun-induced fluorescence as an indicator of crop stress: A study from paired rainfed and irrigated maize fields. In: 2017 Annual Meeting of American Geophysical Union (Poster Presentation).
|