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The effect of drought conditions on irrigated vs. non-irrigated row crops in Alabama were investigated using NASA Earth Observations.
Authors:
Emilene Sivagnanam, Olivia Callaway, Chris Ploetz, Maggi Klug
Over the past decade, drought in Alabama has had a major impact on agriculture, causing crop yields to fall well below normal levels. According to the most current agricultural census led by the National Agricultural Statistics Service (NASS) in 2012, of the 21,283 farms with harvested cropland in the state, only 1,584 farms (7.44 percent) were irrigated, leaving most crops vulnerable to impact from drought conditions. Depending on the duration and type of drought, impacts can range from significant crop yield loss and impacted local and global economies to lower reservoir levels and depleted groundwater levels (Otkin et al. 2013).
Image A. The study area map for this project, including the area that is row crops. Image Credit: NASA DEVELOP
Under the Alabama Water Resource Act, the Office of Water Resources (OWR) is responsible for drought monitoring publishing a new drought plan at least every five years under the advice of the Alabama Drought Assessment and Planning Team (ADAPT). ADAPT is comprised of several state representatives, including the Alabama Office of the State Climatologist (AOSC). The AOSC also contributes a weekly map of drought conditions to the United States Drought Monitor (USDM). The USDM is the current drought monitoring system that policymakers and the media use to allocate drought relief, covering the entire country at a county-by-county level (National Drought Mitigation Center (NDMC) 2017). It is a composite index that uses both quantitative and qualitative data, including community observations, from more than 350 contributors. The USDM then distributes the drought condition maps to policymakers and the media.
Our team collaborated with the AOSC and the Earth System Science Center (ESSC) at the University of Alabama in Huntsville (UAH). The ESSC releases a daily product, called the Gridded Decision Support System for Agrotechnology Transfer (GriDSSAT) that provides information on a crop’s water stress level for Alabama, Florida, Georgia, and South Carolina. The project’s remotely sensed indices are compared to GriDSSAT’s output to determine the accumulated vegetative stress in crops and to potentially add value to GriDSSAT’s daily output. Additionally, the AOSC is interested in this project because this research may provide an enhanced understanding for the output of drought conditions, allowing farmers, policymakers, and the media to have a more accurate and thorough understanding of droughts and their impact on the state.
Moderate resolution satellite remote sensing can enhance current drought monitoring practices led by the AOSC. The incorporation of remotely sensed data in the USDM is useful for monitoring drought because it provides complete, up-to-date, and comprehensive coverage of drought conditions (Peters et al. 2002). This project explored two vegetation indices, the Green Normalized Difference Vegetation Index (GNDVI) and the Normalized Difference Vegetation Index (NDVI), and one water index, the Normalized Difference Water Index (NDWI), from moderate-resolution satellites, Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI), to increase the level of detail in drought depiction for the USDM and to provide near-real time vegetation drought assessments. Additionally, this project considered whether farms with center pivot irrigation fared better than farms relying solely on precipitation during the 2011 and 2016 droughts using data provided by the ESSC. The results were compared to the USDM output and the ESSC’s GriDSSAT crop model.
The area this project looked at was a subset of the HUC-8 Wheeler Lake watershed (Image A).
We computed NDVI, GNDVI, and NDWI, from Landsat 5 TM and Landsat 8 OLI, and then created a time series of these indices for the 2009, 2011, and 2016 growing seasons (Image B).
Image B. Series of indices created by this project to monitor crop stress. Image Credit: NASA DEVELOP
We then overlaid the center pivot irrigation data from the ESSC and cropland data obtained from the U.S. Department of Agriculture’s Cropscape to compare how the areas under the pivots fared during the drought as to the areas of rainfed farmland (Image C). After reviewing these time series and comparing them to each other, we found there was little correlation between the health of crops and the areas they were grown in.
We encountered several challenges that are important to note when remotely monitoring droughts in Alabama. The first was the 16-day temporal resolution of Landsat. This relatively low temporal resolution, coupled with the frequency of cloud cover during the growing season, made it difficult to obtain usable data over a large area that aligned with our study period. The 30-meter spatial resolution impeded the ability to identify crop health, as row crops typically have a larger amount of visible barren soil that affects the amount of green per pixel. Additionally, the coniferous vegetation surrounding agricultural fields remain green year-round, despite dry seasons, which skew the appearance of fields.
The second challenge arose from uncertainties with correctly identifying fields and crop types. CropScape uses the maximum likelihood classifier using an in-house software package, as well as ground truth surveying to classify crops (NASS 2017). While this is reliable at a large scale, assumptions are made at a smaller scale that had a potentially negative impact on our results.
Due to the limited data available, the Center Pivot Irrigation Survey provided by the ESSC became the sole identifier to classify a field as irrigated or non-irrigated. Since other forms of irrigation exist, it is possible that a misclassification happened. Additionally, the planting and harvesting times of the crops were not precisely known, which may have affected the indices’ values as well.
The project resolved that although moderate-resolution satellites provide information about a crop that is under vegetative stress before the USDM depicts drought conditions, the resolution of Landsat 5 TM and Landsat 8 OLI was not high enough to analyze crops at a field level scale.
Image C. Charts showing average GNDVI, NDVI, NDWI values over 2011 and 2016 growing seasons for both rainfed and irrigated (under pivot) crops. Image Credit: NASA DEVELOP
Additional research on this topic includes adding evapotranspiration remote sensing indices to the USDM weekly map. The Evaporative Stress Index (ESI) or the Evaporative Demand Drought Index (EDDI) would be useful to incorporate because both indices provide early warning of drought impacts (Otkin et al. 2013; McEvoy et al. 2016). Additionally, the Soil Moisture Active Passive (SMAP) and WorldView-2 satellites would provide soil moisture data and higher temporal and spatial resolution that lead to further developed conclusions.
References
[1] A. J. Peters, E. A. Walter-Shea, L. Ji, A. VIna, M. Hayes, and M. D. Svoboda. (2002) ÛÏDrought monitoring with NDVI-based Standardized Vegetation Index.Û Photogrammetric Engineering & Remote Sensing. [Online]. 68(1). Available: http://info.asprs.org/publications/pers/2002journal/january/2002_jan_71-75.pdf [Mar. 29 2017]. [2] D.J. McEvoy, J. L. Huntington, M. T. Hobbins, A. Wood, C. Morton, M. Anderson, and C.Hain. (2016). ÛÏThe Evaporative Demand Drought Index. Part II: CONUS-Wide Assessment against Common Drought IndicatorsÛ. American Meteorological Society. [Online]. 17(6). Available: http://journals.ametsoc.org/doi/abs/10.1175/JHM-D-15-0122.1 [Mar. 29 2017]. [3] J. A. Otkin, M. C. Anderson, C. Hain, I. E. Mladenova, J. B. Basara, and M. Svobada. (2013). ÛÏExamining rapid onset drought development using the thermal infraredÛÒbased evaporative stress index.Û Journal of Hydrometeorology. [Online]. 14, pp. 1057-1074. doi: 10.1175/JHM-D-12-0144.1. [Mar. 29 2017]. [4] National Drought Mitigation Center [NDMC]. ÛÏU.S. Drought Monitor Background.Û Available: http://droughtmonitor.unl.edu/AboutUSDM/Background.aspx, 2017 [Mar. 29 2017]. [5] United States Department of Agriculture National Agricultural Statistics Service [USDA NASS]. ÛÏHistorical Highlights: 2012 and Earlier Census Years.Û Available: https://www.agcensus.usda.gov/Publications/2012/Full_Report/Volume_1,_Chapter_1_State_Level/Alabama/st01_1_001_001.pdf, 2012 [Mar. 29 2017].Emilene Sivagnanam is a recent graduate from Columbia University. She received her master’s in climate and society and is working with DEVELOP at Marshall Space Flight Center as an independent research consultant on the Alabama Agriculture project.
Olivia Callaway is an undergraduate student at the University of Alabama in Huntsville working with DEVELOP at Marshall Space Flight Center in Huntsville, Alabama, as an independent research consultant on the Alabama Agriculture project.
Chris Ploetz received his bachelor’s degrees in anthropology and geography from Auburn University before receiving a master’s in geography from the University of Georgia. He is working with DEVELOP at Marshall Space Flight Center in Huntsville, Alabama, as an independent research consultant on the Alabama Agriculture project.
Maggi Klug is a student from the University of Alabama in Huntsville working with DEVELOP at NASA Marshall Space Flight Center as the center lead and an independent research consultant on the Alabama Agriculture project.