Introduction

Data products from the Land Processes Distributed Active Archive Center (LP DAAC) are used in many different applications. They play an important role in modeling, help to detect changes to the landscape, and are a way to assess ecosystem variables, among others. A few of these applications that were published between January and March 2015 are highlighted below. The studies highlight how data collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instruments can be applied to a diverse set of land remote sensing studies.

Informing Crop Decisions with Water Productivity Data

In many areas of the world, farming, industrial pursuits, and residential applications increase the demand for fresh water, while the availability of fresh water decreases. According to Patil and others (2015) in their paper "Assessing agricultural water productivity in desert farming system of Saudi Arabia" published in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, this trend is observed in places like Saudi Arabia where demand for food and subsequent demand for fresh water is increasing.

Science Objectives

This study examines water productivity, the ratio between water use and crop growth. It focuses on wheat, corn, Rhodes grass, and alfalfa grown with center-pivot irrigation at the Todhia Arable Farm in the desert area of Al-Kharj, Saudi Arabia using remote sensing data from 2012 to 2013.

Instruments Used

Data used in the study include: 14 ASTER Registered Radiance at the Sensor (AST_L1B) images, independent ground-level Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI) data, and meteorological data collected from the farm’s weather station. The authors use an ASTER Thermal Infrared (TIR) band (13) to computer the surface temperature of the farm. They also use ASTER data for measuring daily water use and evapotranspiration rates. These measurements are then applied to the Surface Energy Balance Algorithm for Land (SEBAL) model.

Major Findings

After conducting the study, the authors were able to determine that wheat was the best crop to grow when considering water productivity. The study also found that corn also has high water productivity. As a result of these findings, the Todhia Arable Farm increased corn production and decreased Rhodes grass production. The authors conclude that SEBAL and ASTER images can provide realistic estimates of water productivity for crops grown with center-pivot irrigation in Saudi Arabia.

References

Publication Reference

Patil, V., Al-Gaadi, K., Madugundu, R., Tola, E., Marey, S., Aldosari, A., Biradar, C., and Gowda, P., 2015, Assessing agricultural water productivity in desert farming system of Saudi Arabia: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, v. 8, no. 1, p. 284-297. doi:10.1109/JSTARS.2014.2320592

Image Reference

Granule ID: AST14OTH_0030519201307285 3_20150413081437_6129

Helping Farmers Predict Frost Events

Accounting for frost damage is one element of agricultural planning. A crop’s location may increase the risk of frost. At certain temperatures, an entire crop could be lost to frost damage.

Science Objectives

The results of this study, described in the paper "Application of GIS and remote sensing techniques in frost risk mapping for mitigating agricultural losses in the Aberdare ecosystem, Kenya" published in Geocarto International, could help farmers in the Aberdare ecosystem of Kenya understand specific conditions that increase the risk of frost. The results could also help farmers predict future frost events by planning their crop planting locations accordingly to mitigate frost damages.

Instruments Used

The authors use several remote sensing data types to identify factors explaining frost location trends from 2000 to 2013: MODIS nighttime land surface temperature (LST), ASTER Digital Elevation Model (DEM), and land cover types derived from Landsat imagery. The authors delineate a frost map from the nighttime LST data to correlate with ASTER’s 30 meter DEM to understand the relationship between frost, LST, elevation, and land surface curvature.

Major Findings

They found that frost risk hotspots are concentrated in areas with elevations of ~1,700 meters (~5,577 feet) above sea level, and frost primarily occurs in low-lying valleys and western slopes. In the Aberdare Forest and National Park, the authors conclude that 48.35% of the area is at risk for frost occurrences during colder nighttime temperatures in the months of April, May, and November.

References

Publication Reference

Kotikot, S., and Onywere, S., 2015, Application of GIS and remote sensing techniques in frost risk mapping for mitigating agricultural losses in the Aberdare ecosystem, Kenya: Geocarto International, v. 30, no. 1, p. 104-121. 
doi:10.1080/10106049.2014.965758

Image Reference

Granule IDs

• MOD11A1.A2012318.h21v08.005.20123210604541
• MOD11A1.A2012318.h21v09.005.2012321060525

ASTER GDEM is a product of Japan’s Ministry of Economy, Trade, and Industry (METI) and NASA.

Understanding Land Change Trends with MODIS

In a country as vast as Mongolia, MODIS data can be an essential tool for monitoring the various types of land cover due to the relatively large imaging area and frequent revisit time.

Science Objectives

In this study, described in the paper "Trend analysis of MODIS NDVI time series for detecting land degradation and regeneration in Mongolia" published in Journal of Arid Environments, Eckert and others (2015) use MODIS data to better understand why certain land change trends occur in Mongolia and can predict areas where future land cover change may occur.

Instruments Used

The authors use annual, 500 meter MODIS land cover data (MCD12Q1) and 16-day, 250 meter MODIS Normalized Difference Vegetation Index (NDVI) data (MOD13Q1) from MODIS to detect land degradation and land regeneration in Mongolia between 2001 and 2009.

Major Findings

The authors assess how land classification changes between 2001 and 2009 for each pixel within the study area. Depending on the changes observed, the authors classify the change as either a positive or negative land change, based on the NDVI value. For example, the authors define an area that changed from grassland to forest as a positive change and an area that changed from forest to barren as a negative change.

The authors found little-to-no land change in the majority of northern Mongolia; positive land changes in the northeastern part of the country and parts of north central; and negative land changes in the far west, in some of the previously forested areas of the north and the east, and near the capital of Ulaanbaatar. The authors note some of the land changes typically occur along transition zones between land cover types, which could be due to the transition areas being more sensitive to changes in temperature, precipitation, or natural and human impacts on the area.

Note: In this study, the authors use the MODIS Land Cover Version 5.1 data product. The Version 6 product has since been released.

References

Publication Reference

Eckert, S., Hüsler, F., Liniger, H., and Hodel, E., 2015, Trend analysis of MODIS NDVI time series for detecting land degradation and regeneration in Mongolia: Journal of Arid Environments, v. 113, p. 16-28. doi:10.1016/j.jaridenv.2014.09.001

Image Reference

Granule IDs

• MCD12Q1.A2009001.h25v04.051.2014288181622
• MCD12Q1.A2001001.h25v04.051.201487164506