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As global temperature and water availability changes become more extreme, how can we precisely track the effects of these changes on plant life? ECOSTRESS may provide the answer.
Molly Spater, Sol Kim, Leah Kucera, Dr. Joshua B. Fisher, Dr. Christine M. Lee, Dr. Andrew French
Global agricultural systems are intrinsically linked with biodiversity and together constitute a positive feedback loop with climate change. Current models suggest the greatest threat to future global species richness is land cover change, which has widely been driven by the spatial requirements of agricultural and pastoral activities (1, 2). Land use and cover modifications can affect regional climates by altering land surface temperatures, precipitation patterns, and soil moisture regimes (2, 3, 4). Such changes have severely impacted forests which not only comprise the majority of the world’s 36 biodiversity hotspots, but whose canopies function as one of the largest sources of transpiration, a critical pillar of the water cycle (2). Loss in canopy cover can therefore significantly impact regional hydrological systems and exacerbate local drought conditions (5, 6). As of 2006, 31.8 percent of the world’s population lived in biodiversity hotspots (7). Additionally, approximately 21 percent of malnourished children live in hotspots with population growth rates still outpacing the global average (7, 8). The need to coalesce land management strategies that accomplish both agricultural and conservation goals is well-documented, and will only increase as environmental conditions become more variable (9, 10).
Evapotranspiration (ET), or the combination of evaporation from the Earth’s surface and transpiration from plants, is an environmental variable able to capture information about both the hydrological and energy cycles — two known determinants of plant life. Measures of ET record stomatal response to water stress via changes in canopy temperature (11, 12, 13). In this study, we explored the application of ET to identify water-efficient crops at the local scale and to track larger changes in species richness at the global scale to demonstrate its utility in monitoring the elements of this feedback loop.
With the anticipated launch of the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) in 2018, we investigated potential applications of its ET products to inform agricultural management practices and global conservation efforts. Currently, ET datasets such as those produced from Landsat (Land remote-sensing satellite) and MODIS (MODerate resolution Imaging Spectroradiometer) instruments lack the temporal or spatial resolution necessary to capture the diurnal cycle. The diurnal cycle, or patterns in environmental variables — such as solar insolation and temperature — over the 24-hour day, reveals important information about plant water stress. Landsat imagery offers high spatial resolution at 30 meters, but the temporal resolution is limited to a 16-day cycle (14, 15). In contrast, MODIS imagery offers high temporal resolution of one-two days, but at a 250 meters or coarser spatial resolution (16). Both Landsat and MODIS are further limited by placement aboard sun-synchronous platforms with fixed daily observation times. ECOSTRESS will provide high spatial (70 m) and temporal resolution (every four days) ET data. In addition, ECOSTRESS will have a unique overpass cadence due to its placement on the International Space Station (ISS). As the ISS flies over the same area at different times during its four-day orbital path, it will be possible for ECOSTRESS to capture diurnal ET fluctuations.
In-situ cotton data were provided from a two-year experiment in Maricopa, Arizona, for the growing season (July- September) in 2013 and 2014 by the U.S. Department of Agriculture (USDA). Thirty-five cotton varieties were subjected to two drip irrigation regimes: (a) well-watered and (b) water-limited, with each variety replicated three times under each regime. The experimental field was comprised of 210 plots, each containing one variety. The plots were dispersed in six rows (Figure 1). Remotely-sensed data were collected four times a day (7 a.m., 11 a.m., 1 p.m., 3 p.m.) via tractor-mounted sensors from Aug. 1 to Sept. 6 in seven-day intervals in 2013, and from July 3 to Sept. 4 in approximately seven-day intervals in 2014. Aboard the tractor were eight infrared thermospectrometers (IRT), four acoustic sensors, two LIDAR units, and four crop canopy sensors. Yield data were provided at the plot level.
As a proxy for ET data, we used land surface temperature (LST) measurements taken by the IRT sensors. LST is directly correlated with ET, as it reflects surface level water and energy dynamics, such as soil moisture deficiencies which induce plant canopy stress (17). Data falling outside one standard deviation of the median by run time was eliminated to account for accidentally included bare patches of ground as well as instrumental error. Linear corrections were then performed to account for intra-hour temperature variation.
Next, we evaluated how consistently varieties ranked amongst each other in terms of temperature and yield. Using temperature, we ranked varieties low to high (i.e., least to most water-stressed). Varieties that ranked consistently at the top and bottom for both temperature and yield were selected for further analysis. We performed t-tests between the top and bottom varieties at run 1 and run 3 to determine whether the temperature values were statistically different. Next, we subtracted the run 1 temperature values from run 3 and run 4 separately to approximate diurnal variation. Similar t-tests were then performed between the diurnal range of the top and bottom varieties
For the global scale analysis, a full decade of global actual ET data
(1986-1995) at 0.5-by-0.5-degree resolution was acquired from Fisher et al. (2008). This dataset was generated using Advanced Very High Resolution Radiometer (AVHRR) and International Satellite Land Surface Climatology Project, Initiative II (ISLSCP II) data and was tested and validated using eddy covariance measurements (FLUXNET) (16). The data spans the globe at a monthly temporal resolution. Text files of the data were downloaded and converted into images (geotiff format) using Matlab R2016b.
Two vascular plant biodiversity datasets were acquired from Kier et al., 2005, and Kreft and Jetz, 2007 (18, 19). The Kier dataset was produced from species richness data from 867 ecoregions. A global map was then produced with vascular plant species per ecoregion. The Kreft and Jetz dataset was produced from 1,032 geographic regions. Ordinary kriging of species richness from the 1,032 geographic regions produced a global map across an equal area grid (about 12,100 square kilometers, 1 degree latitude by 1 degree longitude near the equator).
Global ET rates (millimeters per month) were calculated in the form of decadal and seasonal (summer, fall, winter, spring) averages. The biodiversity data also were converted into images (geotiff formats) with the same resolution as the ET data (0.5 by 0.5 degrees). Both biodiversity datasets included data on the geographic extent of 13 different biomes. After ensuring “no data” values were masked, simple linear regressions between each of the ET datasets and the biodiversity datasets were performed. Scatterplots also were created, plotting ET against plant species richness with point values color-coded by biome.
Results and Discussion
Observed temperature responses to water stress by cotton varieties in the Maricopa field experiment indicate discernable varietal differences due to phenotypic characteristics, rather than solely from irrigation practices. If phenotypical differences were not a factor, yield rankings would not be expected to change across varieties in the water-limited and well-watered scenarios. Our results demonstrate that expression of water stress is strongly affected by cotton varieties. Statistically significant differences were found between the run 3 temperature and diurnal temperature values of top and bottom-ranked varieties. Temperature differences also were larger in 2014, as water stress was increased by about 50 percent (as compared to the 2013 well-watered conditions). These findings are consistent with previous studies which have noted a negative impact on yield associated with larger diurnal temperature ranges (20).
Phenotypic characteristics of the plants comprising biomes, as well as the seasonality of ET, also play a major role in determining R-squared values on the global scale. These values are highest for both Kier and Kreft at the decadal average, as it normalizes for seasonal variation. R-squared values are lowest in summer and winter due to large seasonal shifts in ET that occur in certain biomes. For instance, ET values in boreal forests/taiga and tundra biomes clustered under 10 millimeters per month during the winter, as these biomes, which are primarily located near the pole in the northern hemisphere, receive less solar insolation due to the Earth’s tilt. Decreased energy available to the biome results in lowered ET rates as less energy is present to stimulate evaporation rates and plants within are entering dormant states halting photosynthetic activity. As solar insolation increases in the summer months, reported ET values for that period also rose, clustering around 100 millimeters per month. In contrast, biomes closer to the equator experience less dramatic seasonal variation in ET, as incoming solar insolation is relatively consistent throughout the year. This finding supports Hawkins et al.’s 2003 study, indicating water availability is most important in determining species richness near the equator while water-energy interaction is more important at the poles (21).
We are now in a world of increased climatic variability, which necessitates the ability to monitor the impacts of drought on vegetation in managed and natural ecosystems as an integrated unit. Heat and water stress damage essential physiological functions of plants with the potential to devastate yield and escalate rates of species richness loss by reducing community resistance to extreme conditions (22, 23, 24). ET is an essential variable in the effort to monitor climatic shifts to protect global agricultural systems and species richness. On the local scale, phenotyping crops via remote sensing can expedite the process of determining plants with superior abilities to withstand stress applied during different stages of the growing season (25). Plant breeders are also known to prefer genotypes of crops with traits that have been previously successful in the field (25). Applying methods such as those used in this study with the aid of high-resolution diurnal ET data from ECOSTRESS could dramatically improve large-scale phenotyping efforts. Furthermore, at the global scale, our study demonstrated the strength of the relationship between ET and global biodiversity. To better target conservation efforts, land managers could potentially use ECOSTRESS ET data to track anomalies and identify biomes where shifts in biodiversity are occurring. Coming full circle, ET also could be used to further studies into the effect of agricultural expansion on regional hydrological and climatic regimes to avoid exacerbating drier conditions and additional loss of species richness in the future.
The research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. We would like to thank Cotton Inc. for providing support for the Maricopa field trials and analyses of cotton quality. We would like to acknowledge support from ECOSTRESS Applications in this work.
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Molly Spater is a recent graduate from University of California, Los Angeles, working with DEVELOP at the Jet Propulsion Laboratory (JPL) as an independent research consultant on the Arizona Agriculture project. You may contact Spater at firstname.lastname@example.org
Sol Kim is a recent graduate from University of California, Berkeley, working with DEVELOP at JPL a Geoinformatics and Mission Applications Fellow on the Arizona Agriculture project. You may contact Kim at email@example.com
Leah Kucera is a recent graduate from Washington University in St. Louis working with DEVELOP at JPL as an independent research consultant on the Arizona Agriculture project. You may contact Kucera at firstname.lastname@example.org
Dr. Joshua B. Fisher is currently the science lead on the ECOSTRESS mission at JPL. You may contact Fisher at email@example.com
Dr. Christine M. Lee is a scientific applications engineer at JPL and applications lead on the ECOSTRESS mission. You can contact Lee at firstname.lastname@example.org
Dr. Andrew French is a research physical scientist in the Water Management and Conservation Research Unit at the U.S. Arid-Land Agricultural Research Center, USDA/ARS in Maricopa, Arizona, and a Science Team Member of the ECOSTRESS mission. You may contact French at email@example.com
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