Data from: Linking grassland canopy structure and function responses to field experimental drought both seasonally and interannually
Data files
Apr 23, 2026 version files 1.10 MB
-
AUC_NIR.csv
1.61 KB
-
AUC_SWIR.csv
1.58 KB
-
AUC_VIS___NIR.csv
1.61 KB
-
AUC_VIS.csv
1.60 KB
-
Codesoftware_Mariela_Encarnacion.md
68.59 KB
-
JuneJuly_2022.csv
13.73 KB
-
JuneJuly_2023.csv
13.79 KB
-
LAI_2022-2023_All_Plots.csv
13.62 KB
-
LAI_2022-2023_Extremes.csv
6.87 KB
-
LAI_NDVI.csv
3.95 KB
-
May_2022.csv
12.11 KB
-
May_2023.csv
12.04 KB
-
NDVI_2022-2023_All_Plots.csv
18.52 KB
-
NDVI_2022-2023_Extremes.csv
9.13 KB
-
NDVI_Ref.csv
7.23 KB
-
NDWI.csv
7.27 KB
-
README.md
27.31 KB
-
Spectral_Monthly_Averages_100cm_2022-2023_Without_Noise.csv
394.44 KB
-
Spectral_Monthly_Averages_100cm_2022-2023.csv
468.36 KB
-
SPI.csv
9.18 KB
-
SPI2.csv
395 B
-
Treatment_Extremes_Indices_Soil_Moisture.csv
3.73 KB
-
Vegetation_Metrics_All_Data_2022___2023.csv
3.33 KB
-
Water_Spectral_Regions.csv
2.72 KB
Abstract
Shifts in species composition in water-limited ecosystems such as temperate prairies during summer droughts can lead to community-level changes. In both short-term experimental and long-term drought events, C4 species decline in abundance while C3 species increase, causing reductions in above ground net primary productivity (ANPP). Thus, this study focused on linking experimental drought-induced shifts in species composition with changes in canopy structure and function using remote sensing approaches. We took advantage of a long-term experimental precipitation study in Central Oklahoma that has established a gradient of seven levels of precipitation in a fully factorial randomized block design: −100%, −80%, −60%, −40%, −20% rainfall exclusion, 0% change in precipitation (i.e., control) and precipitation addition +50%. Our aim was to answer the following questions: (1) How does an experimental precipitation gradient impact canopy spectral reflectance both seasonally and interannually? (2) How does canopy structure and plant species composition vary seasonally and interannually along an experimental precipitation gradient? (3) Can we link variation in canopy reflectance to canopy structure and plant species composition under experimental drought both seasonally and interannually? Our results showed that variation in seasonal and interannual precipitation can be observed in the near-infrared (NIR) portion of the electromagnetic spectrum. A drier year has lower normalized vegetation index (NDVI) values and higher leaf area index (LAI) values due to reduced canopy greenness and increased plant litter, respectively, due to the limited water availability. High abundance of C3 species such as Lespedeza cuneata that have advantageous traits can mediate canopy responses to drought. Thus, species specific abundance, in this case the high abundance of L. cuneata, can influence canopy reflectance. Future studies should focus on understanding the impacts of resource allocation to canopy architecture as well as the relationship between leaf traits and canopy response and how these affect canopy reflectance.
Dataset DOI: 10.5061/dryad.280gb5n3b
Description of the data and file structure
This data was collected at Kessler Atmospheric & Ecological Field Station (KAEFS) located in Central Oklahoma during the summers of 2022 and 2023. Our study site is a temperate mesic grassland and has been part of a long-term drought experiment established in 2016. We have experimentally manipulated precipitation in the field by randomly assigning seven levels of precipitation in a fully factorial randomized block design: −100%, −80%, −60%, −40%, −20% rainfall exclusion, 0% change in precipitation (i.e., control) and precipitation addition +50%. We acquired canopy-level spectral reflectance measurements using an ASD FieldSpec3, which is a hyperspectral sensor that collects data in the full spectrum (350nm – 2500nm). To preserve the integrity of our experimental design, we only measured in three treatments, 0% (control), -100% (severe drought) and +50% (precipitation addition) because data collection required panel removal to avoid noise caused by scattering. We used the Accu-PAR-LP-80 Ceptometer to acquire the Leaf Area Index (LAI) once every two weeks. We acquired species richness, evenness and diversity (Shannon’s Diversity Index) from foliar monthly cover data for the beginning and middle of the growing seasons of 2022 and 2023 by using the median of each cover class category as our abundance value and then using the DIVERSE function in PRIMER-e v. 6 software (www.primer-e.com; Anderson et al., 2008). We acquired site-level monthly precipitation data from the Washington (WASH) Mesonet Station located in KAEFS and calculated the Standardized Precipitation Index (SPI).
Files and variables
File: Treatment_Extremes_Indices_Soil_Moisture.csv
Description: This contains NDVI, NDWI, LAI and Soil Moisture for the extreme treatment plots (0% (control), +50% (addition) and -100% (severe drought)).
Variables
- Plot: unique numerical identifier for plot. Plots 6, 14 and 16 are severe drought plots. Plots 5, 13 and 17 are control plots (no change in rainfall). Plots 1, 11 and 20 are rainfall addition plots.
- Year: designates the year the data was collected 2022 or 2023.
- Month: designates the month the data was collected.
- Treatment: numerical identifier for rainfall conditions (0% (control), +50% (addition) and -100% (severe drought)).
- Spectral NDVI: Normalized difference vegetation index calculated from canopy reflectance. Typical values range from -1 to 1.
- NDWI: Normalized difference water index calculated from canopy reflectance. Typical values range from -1 to 1.
- Avg LAI: Averaged Leaf Area Index. This data was collected with an Accupar LP-80 Ceptometer.
- Soil Moisture 4 in: Soil Moisture(%) collected at 4 inch depth.
- Soil Moisture 8 in: Soil Moisture(%) collected at 8 inch depth.
File: LAI_NDVI.csv
Description: This contains NDVI and LAI for all 7 treatments and 21 plots.
Variables
- Month: designates the month the data was collected.
- Plot: unique identifier for plot ranging from 1-21.
- Treatment: numerical identifier for rainfall conditions (0% (control), +50% (addition), -20% (20% precipitation reduction), -40% (40% precipitation reduction), -60% (60% precipitation reduction), -80% (80% precipitation reduction) and -100% (severe drought)).
- Year: designates the year the data was collected 2022 or 2023.
- NDVI Avg: Averaged Normalized difference vegetation index acquired using the RapidSCAN CS-45 from Holland Scientific. Typical values range from -1 to 1.
- LAI Avg: Averaged Leaf Area Index acquired using the Accupar LP-80 from METER.
File: NDVI_2022-2023_All_Plots.csv
Description: This contains abiotic and biotic data for conditional inference trees.
Variables
- Month: designates the month the data was collected.
- Plot: unique identifier for plot ranging from 1-21.
- Block: categorical variable, each block contains 7 plots as part of our fully factorial randomized block design. There are 3 blocks.
- Treatment: numerical identifier for rainfall conditions (0% (control), +50% (addition), -20% (20% precipitation reduction), -40% (40% precipitation reduction), -60% (60% precipitation reduction), -80% (80% precipitation reduction) and -100% (severe drought)).
- Year: designates the year the data was collected 2022 or 2023.
- NDVI Avg: Averaged Normalized difference vegetation index acquired using the RapidSCAN CS-45 from Holland Scientific. Typical values range from -1 to 1.
- Treatment2: broad categories for the 7 treatments ranging from control, wet and dry. Dry conditions include all precipitation reduction treatments (-20% to -100%).
- SPI: The Standardized Precipitation Index (SPI) is a drought index that is widely used for drought detection as it measures normalized anomalies in precipitation (McKee et al. 1993; Guttman, 1999; Stagge et al. 2015). This SPI is calculated at 1 month using 29 years of rainfall data from Mesonet.
- Species Richness: number of species within a plot.
- Evenness: Pielou's evenness index ranging from 0 to 1.
- Diversity: Shannon's diversity.
- Presence of Lespedeza: categorical variable, yes or no, denominating the overall presence of Lespedeza cuneata in each plot.
- Presence of Bothrichloa: categorical variable, yes or no, denominating the overall presence of Bothrichloa ischaemum in each plot.
- SPI_3: This SPI is calculated at 3 months using 29 years of rainfall data from Mesonet.
- SPI_6: This SPI is calculated at 3 months using 29 years of rainfall data from Mesonet.
- Early Season Presence of Lespedeza: categorical variable, yes or no, denominating the overall presence of Lespedeza cuneata in each plot for the month of May.
- Late Season Presence of Lespedeza: categorical variable, yes or no, denominating the overall presence of Lespedeza cuneata in each plot for the month of August.
File: NDVI_2022-2023_Extremes.csv
Description: This contains abiotic and biotic data for conditional inference trees.
Variables
- Month: designates the month the data was collected.
- Plot: unique identifier for plot ranging from 1-21.
- Block: categorical variable, each block contains 7 plots as part of our fully factorial randomized block design. There are 3 blocks.
- Treatment: numerical identifier for rainfall conditions (0% (control), +50% (addition) and -100% (severe drought)).
- Year: designates the year the data was collected 2022 or 2023.
- NDVI Avg: Averaged Normalized difference vegetation index acquired using the RapidSCAN CS-45 from Holland Scientific. Typical values range from -1 to 1.
- Treatment2: broad categories for the 7 treatments ranging from control, wet and dry. Dry conditions include all precipitation reduction treatments (-20% to -100%).
- SPI: The Standardized Precipitation Index (SPI) is a drought index that is widely used for drought detection as it measures normalized anomalies in precipitation (McKee et al. 1993; Guttman, 1999; Stagge et al. 2015). This SPI is calculated at 1 month using 29 years of rainfall data from Mesonet.
- Species Richness: number of species within a plot.
- Evenness: Pielou's evenness index ranging from 0 to 1.
- Diversity: Shannon's diversity.
- Presence of Lespedeza: categorical variable, yes or no, denominating the overall presence of Lespedeza cuneata in each plot.
- Presence of Bothrichloa: categorical variable, yes or no, denominating the overall presence of Bothrichloa ischaemum in each plot.
- SPI_3: This SPI is calculated at 3 months using 29 years of rainfall data from Mesonet.
- SPI_6: This SPI is calculated at 3 months using 29 years of rainfall data from Mesonet.
- Early Season Presence of Lespedeza: categorical variable, yes or no, denominating the overall presence of Lespedeza cuneata in each plot for the month of May.
- Late Season Presence of Lespedeza: categorical variable, yes or no, denominating the overall presence of Lespedeza cuneata in each plot for the month of August.
File: LAI_2022-2023_Extremes.csv
Description: This contains abiotic and biotic data for conditional inference trees.
Variables
- Month: designates the month the data was collected.
- Plot: unique identifier for plot ranging from 1-21.
- Block: categorical variable, each block contains 7 plots as part of our fully factorial randomized block design. There are 3 blocks.
- LAI Avg: Averaged Leaf Area Index acquired using the ACCUPAR LP-80 from METER.
- Year: designates the year the data was collected 2022 or 2023.
- Treatment: numerical identifier for rainfall conditions (0% (control), +50% (addition) and -100% (severe drought)).
- SPI: The Standardized Precipitation Index (SPI) is a drought index that is widely used for drought detection as it measures normalized anomalies in precipitation (McKee et al. 1993; Guttman, 1999; Stagge et al. 2015). This SPI is calculated at 1 month using 29 years of rainfall data from Mesonet.
- Species Richness: number of species within a plot.
- Evenness: Pielou's evenness index ranging from 0 to 1.
- Diversity: Shannon's diversity.
- Presence of Lespedeza: categorical variable, yes or no, denominating the overall presence of Lespedeza cuneata in each plot.
- Presence of Bothrichloa: categorical variable, yes or no, denominating the overall presence of Bothrichloa ischaemum in each plot.
- SPI_3: This SPI is calculated at 3 months using 29 years of rainfall data from Mesonet.
- SPI_6: This SPI is calculated at 3 months using 29 years of rainfall data from Mesonet.
- Early Season Presence of Lespedeza: categorical variable, yes or no, denominating the overall presence of Lespedeza cuneata in each plot for the month of May.
- Late Season Presence of Lespedeza: categorical variable, yes or no, denominating the overall presence of Lespedeza cuneata in each plot for the month of August.
File: LAI_2022-2023_All_Plots.csv
Description: This contains abiotic and biotic data for conditional inference trees.
Variables
- Month: designates the month the data was collected.
- Plot: unique identifier for plot ranging from 1-21.
- Block: categorical variable, each block contains 7 plots as part of our fully factorial randomized block design. There are 3 blocks.
- LAI Avg: Averaged Leaf Area Index acquired using the ACCUPAR LP-80 from METER.
- Year: designates the year the data was collected 2022 or 2023.
- Treatment: numerical identifier for rainfall conditions (0% (control), +50% (addition), -20% (20% precipitation reduction), -40% (40% precipitation reduction), -60% (60% precipitation reduction), -80% (80% precipitation reduction) and -100% (severe drought)).
- Treatment2: broad categories for the 7 treatments ranging from control, wet and dry. Dry conditions include all precipitation reduction treatments (-20% to -100%).
- SPI: The Standardized Precipitation Index (SPI) is a drought index that is widely used for drought detection as it measures normalized anomalies in precipitation (McKee et al. 1993; Guttman, 1999; Stagge et al. 2015). This SPI is calculated at 1 month using 29 years of rainfall data from Mesonet.
- Species Richness: number of species within a plot.
- Evenness: Pielou's evenness index ranging from 0 to 1.
- Diversity: Shannon's diversity.
- Presence of Lespedeza: categorical variable, yes or no, denominating the overall presence of Lespedeza cuneata in each plot.
- Presence of Bothrichloa: categorical variable, yes or no, denominating the overall presence of Bothrichloa ischaemum in each plot.
- SPI_3: This SPI is calculated at 3 months using 29 years of rainfall data from Mesonet.
- SPI_6: This SPI is calculated at 3 months using 29 years of rainfall data from Mesonet.
- Early Season Presence of Lespedeza: categorical variable, yes or no, denominating the overall presence of Lespedeza cuneata in each plot for the month of May.
- Late Season Presence of Lespedeza: categorical variable, yes or no, denominating the overall presence of Lespedeza cuneata in each plot for the month of August.
File: NDWI.csv
Description: This contains abiotic and biotic data for conditional inference trees.
Variables
- NDWI: Normalized difference water index calculated from canopy reflectance. Typical values range from -1 to 1.
- Month: designates the month the data was collected.
- Plot: unique identifier for plot ranging from 1-21.
- Block: categorical variable, each block contains 7 plots as part of our fully factorial randomized block design. There are 3 blocks.
- Year: designates the year the data was collected 2022 or 2023.
- Treatment: numerical identifier for rainfall conditions (0% (control), +50% (addition), -20% (20% precipitation reduction), -40% (40% precipitation reduction), -60% (60% precipitation reduction), -80% (80% precipitation reduction) and -100% (severe drought)).
- Treatment2: broad categories for the 7 treatments ranging from control, wet and dry. Dry conditions include all precipitation reduction treatments (-20% to -100%).
- SPI: The Standardized Precipitation Index (SPI) is a drought index that is widely used for drought detection as it measures normalized anomalies in precipitation (McKee et al. 1993; Guttman, 1999; Stagge et al. 2015). This SPI is calculated at 1 month using 29 years of rainfall data from Mesonet.
- Species Richness: number of species within a plot.
- Evenness: Pielou's evenness index ranging from 0 to 1.
- Diversity: Shannon's diversity.
- Presence of Lespedeza: categorical variable, yes or no, denominating the overall presence of Lespedeza cuneata in each plot.
- Presence of Bothrichloa: categorical variable, yes or no, denominating the overall presence of Bothrichloa ischaemum in each plot.
- SPI_3: This SPI is calculated at 3 months using 29 years of rainfall data from Mesonet.
- SPI_6: This SPI is calculated at 3 months using 29 years of rainfall data from Mesonet.
- Early Season Presence of Lespedeza: categorical variable, yes or no, denominating the overall presence of Lespedeza cuneata in each plot for the month of May.
- Late Season Presence of Lespedeza: categorical variable, yes or no, denominating the overall presence of Lespedeza cuneata in each plot for the month of August.
- Soil Moisture 4 in: Soil Moisture(%) collected at 4 inch depth.
- Soil Moisture 8 in: Soil Moisture(%) collected at 8 inch depth.
File: NDVI_Ref.csv
Description: This contains abiotic and biotic data for conditional inference trees.
Variables
- NDVI: Normalized difference vegetation index calculated from canopy reflectance. Typical values range from -1 to 1.
- Month: designates the month the data was collected.
- Plot: unique identifier for plot ranging from 1-21.
- Block: categorical variable, each block contains 7 plots as part of our fully factorial randomized block design. There are 3 blocks.
- Year: designates the year the data was collected 2022 or 2023.
- Treatment: numerical identifier for rainfall conditions (0% (control), +50% (addition), -20% (20% precipitation reduction), -40% (40% precipitation reduction), -60% (60% precipitation reduction), -80% (80% precipitation reduction) and -100% (severe drought)).
- SPI: The Standardized Precipitation Index (SPI) is a drought index that is widely used for drought detection as it measures normalized anomalies in precipitation (McKee et al. 1993; Guttman, 1999; Stagge et al. 2015). This SPI is calculated at 1 month using 29 years of rainfall data from Mesonet.
- Species Richness: number of species within a plot.
- Evenness: Pielou's evenness index ranging from 0 to 1.
- Diversity: Shannon's diversity.
- Presence of Lespedeza: categorical variable, yes or no, denominating the overall presence of Lespedeza cuneata in each plot.
- Presence of Bothrichloa: categorical variable, yes or no, denominating the overall presence of Bothrichloa ischaemum in each plot.
- SPI_3: This SPI is calculated at 3 months using 29 years of rainfall data from Mesonet.
- SPI_6: This SPI is calculated at 3 months using 29 years of rainfall data from Mesonet.
- Early Season Presence of Lespedeza: categorical variable, yes or no, denominating the overall presence of Lespedeza cuneata in each plot for the month of May.
- Late Season Presence of Lespedeza: categorical variable, yes or no, denominating the overall presence of Lespedeza cuneata in each plot for the month of August.
- Soil Moisture 4 in: Soil Moisture(%) collected at 4 inch depth.
- Soil Moisture 8 in: Soil Moisture(%) collected at 8 inch depth.
File: Vegetation_Metrics_All_Data_2022___2023.csv
Description: This contains vegetation data for all 7 treatments and 21 plots.
Variables
- Month: designates the month the data was collected.
- Plot: unique identifier for plot ranging from 1-21.
- Block: categorical variable, each block contains 7 plots as part of our fully factorial randomized block design. There are 3 blocks.
- Year: designates the year the data was collected 2022 or 2023.
- Treatment: numerical identifier for rainfall conditions (0% (control), +50% (addition), -20% (20% precipitation reduction), -40% (40% precipitation reduction), -60% (60% precipitation reduction), -80% (80% precipitation reduction) and -100% (severe drought)).
- Species Richness: number of species within a plot.
- Evenness: Pielou's evenness index ranging from 0 to 1.
- Diversity: Shannon's diversity.
File: May_2022.csv
Description: This contains vegetation data for all 7 treatments and 21 plots.
Variables
- Year: designates the year the data was collected 2022 or 2023.
- Date: designates the month the data was collected.
- Plot: unique identifier for plot ranging from 1-21.
- Precipitation: numerical identifier for rainfall conditions (0% (control), +50% (addition), -20% (20% precipitation reduction), -40% (40% precipitation reduction), -60% (60% precipitation reduction), -80% (80% precipitation reduction) and -100% (severe drought)).
- Cover: relativized cover for all 12 focal species.
- Species: scientific name of all 12 focal species
File: JuneJuly_2022.csv
Description: This contains vegetation data for all 7 treatments and 21 plots.
Variables
- Year: designates the year the data was collected 2022 or 2023.
- Date: designates the month the data was collected. June for 2022.
- Plot: unique identifier for plot ranging from 1-21.
- Precipitation: numerical identifier for rainfall conditions (0% (control), +50% (addition), -20% (20% precipitation reduction), -40% (40% precipitation reduction), -60% (60% precipitation reduction), -80% (80% precipitation reduction) and -100% (severe drought)).
- Cover: relativized cover for all 12 focal species.
- Species: scientific name of all 12 focal species.
File: JuneJuly_2023.csv
Description: This contains vegetation data for all 7 treatments and 21 plots.
Variables
- Year: designates the year the data was collected 2022 or 2023.
- Date: designates the month the data was collected. July for 2023.
- Plot: unique identifier for plot ranging from 1-21.
- Precipitation: numerical identifier for rainfall conditions (0% (control), +50% (addition), -20% (20% precipitation reduction), -40% (40% precipitation reduction), -60% (60% precipitation reduction), -80% (80% precipitation reduction) and -100% (severe drought)).
- Cover: relativized cover for all 12 focal species.
- Species: scientific name of all 12 focal species.
File: May_2023.csv
Description: This contains vegetation data for all 7 treatments and 21 plots.
Variables
- Year: designates the year the data was collected 2022 or 2023.
- Date: designates the month the data was collected.
- Plot: unique identifier for plot ranging from 1-21.
- Precipitation: numerical identifier for rainfall conditions (0% (control), +50% (addition), -20% (20% precipitation reduction), -40% (40% precipitation reduction), -60% (60% precipitation reduction), -80% (80% precipitation reduction) and -100% (severe drought)).
- Cover: relativized cover for all 12 focal species.
- Species: scientific name of all 12 focal species.
File: SPI.csv
Description: This dataset contains SPI calculated at 1-month using 29 years of Mesonet rainfall for KAEFS.
Variables
- Month: designates the month that the data was collected.
- Year: designates the year the data was collected.
- SPI: Standardized Precipitation Index calculated for 1-month.
File: SPI2.csv
Description: This dataset contains SPI calculated at 1-month using 29 years of Mesonet rainfall for KAEFS.
Variables
- Month: designates the month that the data was collected.
- Year: designates the year the data was collected.
- Season: designates if it is spring or summer.
- SPI: Standardized Precipitation Index calculated for 1-month.
File: Water_Spectral_Regions.csv
Description: This contains spectral data for three regions known to be water absorption troughs.
Variables
- Plot: unique identifier for each plot.
- Treatment: numerical identifier for rainfall conditions (0% (control), +50% (addition) and -100% (severe drought)).
- Month: designates the month that the data was collected.
- Year: designates the year the data was collected.
- 970nm: spectral reflectance for the 970nm wavelength.
- 1,175nm: spectral reflectance for the 1,175nm wavelength.
- 1,450nm: spectral reflectance for the 1,450nm wavelength.
File: Spectral_Monthly_Averages_100cm_2022-2023_Without_Noise.csv
Description: This data was collected using a FieldSpec3 spectroradiometer at 100cm. All reflectance has been averaged to from plot level to treatment level and was only collected for the extreme treatments (control, addition and drought). For example drought reflectance is generated from reflectance from plots 6, 14 and 16.
Variables
- Wavelength: ranges from 350nm to 2,500nm.
- Each column header designates the treatment, month and year and contains canopy reflectance for each respective wavelength. Areas of noise (scattering and/or absorption) have been removed for visualization and analyses.
File: Spectral_Monthly_Averages_100cm_2022-2023.csv
Description: This data was collected using a FieldSpec3 spectroradiometer at 100cm. All reflectance has been averaged to from plot level to treatment level and was only collected for the extreme treatments (control, addition and drought). For example drought reflectance is generated from reflectance from plots 6, 14 and 16.
Variables
- Wavelength: ranges from 350nm to 2,500nm.
- Each column header designates the treatment, month and year and contains canopy reflectance for each respective wavelength. This dataset contains areas of noise (scattering and/or absorption) and present as negative values or values above 1.
File: AUC_NIR.csv
Description: KaleidaGraph (KaleidaGraph, Version 4.5.4 for Windows. Synergy Software, Reading, PA, USA. www.synergy.com) was used to acquire the area under the curve (AUC) for all spectral vegetation curves for 2022 and 2023. In order to acquire the AUC, the curves were divided into the following sections: 400-700nm (visible), 700-1100nm (NIR) and 1100-2500nm (SWIR).
Variables
- Plot: unique identifier for each plot.
- Treatment: numerical identifier for rainfall conditions (0% (control), +50% (addition) and -100% (severe drought)).
- Month: designates the month that the data was collected.
- Year: designates the year the data was collected.
- Area under the curve_NIR: AUC for the NIR region (700-1100nm).
File: AUC_SWIR.csv
Description: KaleidaGraph (KaleidaGraph, Version 4.5.4 for Windows. Synergy Software, Reading, PA, USA. www.synergy.com) was used to acquire the area under the curve (AUC) for all spectral vegetation curves for 2022 and 2023. In order to acquire the AUC, the curves were divided into the following sections: 400-700nm (visible), 700-1100nm (NIR) and 1100-2500nm (SWIR).
Variables
- Plot: unique identifier for each plot.
- Treatment: numerical identifier for rainfall conditions (0% (control), +50% (addition) and -100% (severe drought)).
- Month: designates the month that the data was collected.
- Year: designates the year the data was collected.
- Area under the curve_SWIR: AUC for the SWIR region (1100-2500nm).
File: AUC_VIS.csv
Description: KaleidaGraph (KaleidaGraph, Version 4.5.4 for Windows. Synergy Software, Reading, PA, USA. www.synergy.com) was used to acquire the area under the curve (AUC) for all spectral vegetation curves for 2022 and 2023. In order to acquire the AUC, the curves were divided into the following sections: 400-700nm (visible), 700-1100nm (NIR) and 1100-2500nm (SWIR).
Variables
- Plot: unique identifier for each plot.
- Treatment: numerical identifier for rainfall conditions (0% (control), +50% (addition) and -100% (severe drought)).
- Month: designates the month that the data was collected.
- Year: designates the year the data was collected.
- Area under the curve_VIS: AUC for the VIS region (400-700nm).
File: AUC_VIS___NIR.csv
Description: KaleidaGraph (KaleidaGraph, Version 4.5.4 for Windows. Synergy Software, Reading, PA, USA. www.synergy.com) was used to acquire the area under the curve (AUC) for all spectral vegetation curves for 2022 and 2023. In order to acquire the AUC, the curves were divided into the following sections: 400-700nm (visible), 700-1100nm (NIR) and 1100-2500nm (SWIR).
Variables
- Plot: unique identifier for each plot.
- Treatment: numerical identifier for rainfall conditions (0% (control), +50% (addition) and -100% (severe drought)).
- Month: designates the month that the data was collected.
- Year: designates the year the data was collected.
- Area under the curve_VIS_NIR: AUC for VIS + NIR region (400-1100nm).
File: Treatment_Extremes_Indices_Soil_Moisture.csv
Description: This contains NDVI, NDWI, LAI and Soil Moisture for the extreme treatment plots (0% (control), +50% (addition) and -100% (severe drought)).
Variables
- Plot: unique identifier for each plot.
- Treatment: numerical identifier for rainfall conditions (0% (control), +50% (addition) and -100% (severe drought)).
- Spectral NDVI: Normalized difference vegetation index calculated from canopy spectral reflectance. Typical values range from -1 to 1.
- NDWI: Normalized difference water index calculated from canopy spectral reflectance. Typical values range from -1 to 1.
- Avg LAI: Averaged Leaf Area Index. We had two measurements per plot and this is the average value of those two measurements.
- Soil Moisture 4 in: Soil Moisture(%) collected at 4 inch depth.
- Soil Moisture 8 in: Soil Moisture(%) collected at 8 inch depth.
File: Codesoftware_Mariela_Encarnacion.md
Description: This contains all code used in R.
