Highlights from the Literature: July to September 2019

Oct 28, 2019

Data products distributed by the Land Processes Distributed Active Archive Center (LP DAAC) are used in many different Earth Science applications. LP DAAC products play an important role in modeling, detecting changes to the landscape, and assessing ecosystem variables, to name a few. Three of those applications, published between July and September 2019, are highlighted below. A more comprehensive list is available on the LP DAAC Publications webpage.

Richetti, J., Boote, K.J., Hoogenboom, G., Judge, J., Johann, J.A., and Uribe-Opazo, M.A., 2019, Remotely sensed vegetation index and LAI for parameter determination of the CSM-CROPGRO-Soybean model when in situ data are not available: International Journal of Applied Earth Observation and Geoinformation, v. 79, p. 110–115. [Also available at https://doi.org/10.1016/j.jag.2019.03.007.]

Leaf Area Index data bear Toledo, Brazil.

Combined Terra and Aqua MODIS LAI data over Richetti and others’ (2019) study area during the peak LAI period for soybeans, as identified by the authors. This data was downloaded using AρρEEARS.

Granule ID:

Data Citation:
Myneni, R., Knyazikhin, Y., and Park, T., 2015, MCD15A3H MODIS/Terra+Aqua Leaf Area Index/FPAR 4-day L4 Global 500m SIN Grid V006: NASA EOSDIS Land Processes DAAC, accessed 2019-09-24, at https://doi.org/10.5067/MODIS/MCD15A3H.006.

Richetti and others (2019) set out to investigate if remote sensing data can be used to calibrate genetic specific parameters (GSPs) for crop growth models to improve predicting soybean crop yields. The authors use Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) data from the Terra and Aqua MODIS vegetation indices products (MOD13Q1 and MYD13Q1) and Leaf Area Index (LAI) data from the Combined Terra and Aqua MODIS data product (MCD15A3H) during the growing season of 2016-2017 over commercial farms in Paraná, Brazil, and Iowa, United States. The MODIS data were retrieved using the LP DAAC’s Application for Extracting and Exploring Analysis Ready Samples (AρρEEARS) web-based application. These data were used to calibrate cultivar parameters for the CSM-CROPGRO-Soybean model. The authors found that these data can be used to calibrate GSPs for crop growth models when in-situ data are not available. In addition, the authors found that MODIS LAI and light interception data provided results on pod weight and biomass prediction that were considered as good as the in-situ data.

De Beurs, K.M., McThompson, N.S., Owsley, B.C., and Henebry, G.M., 2019, Hurricane damage detection on four major Caribbean islands: Remote Sensing of Environment, v. 229, p. 1–13. [Also available at https://doi.org/10.1016/j.rse.2019.04.028.]

BRDF data over the Caribbean Islands, highlight Cuba, Jamaica, Hispaniola, and Puerto Rico.

True color composite of Combined Terra and Aqua MODIS normalized BRDF-adjusted reflectance data over the Caribbean region. Data from this date was used to validate De Beurs and others’ (2019) results with other commercial data.

Granule IDs:

Data Citation:
Schaaf, C., and Wang, Z., 2015, MCD43A4 MODIS/Terra+Aqua BRDF/Albedo Nadir BRDF Adjusted Ref Daily L3 Global - 500m V006: NASA EOSDIS Land Processes DAAC, accessed 2019-09-24, at https://doi.org/10.5067/MODIS/MCD43A4.006.

In the past, MODIS Vegetation Indices data have been used to study hurricanes after individual hurricane events. It is believed that as sea surface temperatures continue to rise, the potential for more intense and destructive hurricanes with longer lifetimes will also increase. With land temperature increases occurring as well, the rate of drying is also expected to increase. This could cause more intense droughts in a shorter time period, as seen in the Caribbean region. In June 2015 alone, 95 percent of the four major islands in the area experienced drought. Since a lot of research focuses on single hurricane events, scientists are still unsure about the impact these rising temperatures will have on the intensity and timing of hurricanes and how the higher likelihood for intense droughts will impact areas that are hit by hurricanes. This is what De Beurs and others (2019) wanted to learn more about. Originally De Beurs and others (2016) created a MODIS-derived disturbance index (DI) based on standardized tasseled cap brightness, greenness, and wetness data. The authors found that the MODIS record, 2000 to present, offers a sufficiently long time series to provide a standardized comparison for their DI. In the paper the authors use MODIS Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) (MCD43A4) data from 2000 to 2017 to study the impacts of hurricanes and droughts on the four largest Caribbean islands: Cuba, Hispaniola (location of Haiti and the Dominican Republic), Puerto Rico, and Jamaica. First, they calculate the MODIS tasseled cap brightness, greenness, and wetness values for the time series of data, standardize it, and then combine this information into a DI. In this index, higher DI values (3 or above) suggest significant disturbance. The authors found the disturbed land in the area varied between 0 and 50 percent, with the highest percentages corresponding to major droughts in Cuba and damage from Hurricane Maria in Puerto Rico. In the case of Hurricane Maria, they found 50 percent of Puerto Rico or 4,549 km2 (454,900 hectares) had a significant disturbance, and that recovery on the island did not begin until 2.5 months after the hurricane made landfall. Prior to Hurricane Maria the DI value for Puerto Rico was 0; after Hurricane Maria the DI value for developed land on the island was 2.40 and the DI value for vegetated land was 3.37. The authors believe their approach enables a better understanding of the combined effects of hurricanes and droughts across island landscapes.

Noori, A.M., Pradhan, B., and Ajaj, Q.M., 2019, Dam site suitability assessment at the Greater Zab River in northern Iraq using remote sensing data and GIS: Journal of Hydrology, v. 574, p. 964–979. [Also available at https://doi.org/10.1016/j.jhydrol.2019.05.001.]

Elevation data over Iraq near the Great Zab River.

An ASTER GDEM image of a portion of the Great Zab River in northern Iraq, the study area of Noori and others (2019).

Granule IDs:

Data Citation:
NASA/METI/AIST/Japan Spacesystems, and U.S./Japan ASTER Science Team, 2019, ASTER Global Digital Elevation Model V003: NASA EOSDIS Land Processes DAAC, accessed 2019-09-24, at https://doi.org/10.5067/ASTER/ASTGTM.003 .

Within the last few decades northern Iraq has seen a lot of long-term droughts and water shortages, but also occasional flooding that relates to the changing climate. According to Noori and others (2019) one strategy to combat the issues relating to droughts and floods in the area is to construct dams. Dams can help control flooding, increase the water supply for irrigation and drinking water, and provide the area with hydroelectric power. The authors set out to find suitable areas for future dam construction along the Great Zab river using Landsat 8, Terra ASTER Global Digital Elevation Model (GDEM) (ASTGTM) remote sensing data, and local data on soil, climate, and rainfall, in combination with GIS software and multi-criteria decision making techniques. During their research the authors decided to compare the traditionally used analytic hierarchy process (AHP) against Fuzzy Logic for determining site suitability. The authors specifically use the Terra ASTER GDEM data to extract information on drainage networks, stream flow, altitude, and slope. They found Fuzzy Logic to be more accurate than AHP. Maps of dam locations, created by the Fuzzy Logic model, were then combined with data generated by ASTER GDEM (drainage network, contour lines, and triangulated irregular network data) to create a ranking system based on storage and dam length. Four suitable dam sites were discovered, one of which was the best site for water harvesting. The next step will be for field visits to confirm the final location for a future dam. The authors believe their methods can be replicated in other areas around the world for similar applications.

Material written by Danielle Golon​1

1 Innovate!, Inc., contractor to the U.S. Geological Survey, Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota, USA. Work performed under USGS contract G15PC00012 for LP DAAC2.

2 LP DAAC Work performed under NASA contract NNG14HH33I.