Document Type

Dissertation - Open Access

Award Date


Degree Name

Doctor of Philosophy (PhD)

Department / School

Geospatial Science and Engineering

First Advisor

Geoffrey M Henebry


AMSR passive microwave, convex quadratic model, cropland phenology, land surface phenology, remote sensing, synergistic


In today’s world of increasing food insecurity due to more frequent and extreme events (droughts, floods), a comprehensive understanding of global cropland dynamics is critically needed. Land surface parameters derived from the passive microwave Advanced Microwave Scanning Radiometer on EOS (AMSR-E) and AMSR2 data enable monitoring of cropland dynamics and they can complement visible to near infrared (VNIR) and thermal infrared (TIR) data. Passive microwave data are less sensitive to atmospheric effects, cloud contamination, and solar illumination constraints resulting in finer temporal resolution suitable to track the temporal progression of cropland cover development compared to the VNIR data that has coarser temporal resolution due to compositing to lessen the atmospheric effects. Both VNIR and TIR data have moderate to fine spatial resolution compared to passive microwaves, due to the faint microwave flux from the planetary surface. I used AMSR, MODIS, TRMM, and simplified surface energy balance (SSEB) data to study cropland dynamics from 2003-2015 in North Dakota, USA, the Canadian Prairie Provinces, Northern Eurasia, and East Africa: a contrast between crop exporting regions and a food insecure region. Croplands in the temperate region are better studied compared to that of the tropics. The objective of this research was to characterize cropland dynamics in the tropics based on the knowledge gained about the microwave products in the temperate croplands. This study also aimed at assessing the utility of passive microwave data for cropland dynamics study, especially for tropical cropland regions that are often cloud-obscured during the growing season and have sparse in situ data networks. Using MODIS land cover data, I identified 162 AMSR grid cells (25km*25km=625km2) dominated by croplands within the study regions. To fit the passive microwave time series data to environmental forcings, I used the convex quadratic (CxQ) model fit that has been successfully applied with the VNIR and TIR data to herbaceous vegetation in temperate and boreal ecoregions. Land surface dynamics in the thermally-limited temperate croplands were characterized as a function of temperature; whereas, a function of moisture to model land surface dynamics in the tropical croplands. In the temperate croplands, growing degree-day (GDD), NDVI, and vegetation optical depth (VOD) were modeled as a convex quadratic function of accumulated GDD (AGDD) derived from AMSR air temperature data, yielding high coefficients of determination (0.88≤ r2≤0.98) Deviations of GDD from the long term average CxQ model by site corresponded to peak VI producing negative residuals (arising from higher latent heat flux) and low VI at beginning and end of growing season producing positive residuals (arising from higher sensible heat flux). In Northern Eurasia, sites at lower latitude (44° - 48° N) that grow winter grains showed either a longer unimodal growing season or a bimodal growing season; whereas, sites at higher latitude (48° - 56° N) where spring grains are cultivated showed shorter, unimodal growing seasons. Peak VOD showed strong linear correspondence with peak greenness (NDVI) with r2>0.8, but with a one-week lag. The AMSR data were able to capture the effects of the 2010 and 2007 heat waves that devastated grain production in southwestern Russia and Northern Kazakhstan, and Ukraine, respectively, better than the MODIS data. In East African croplands, the AMSR, TRMM, and SSEB datasets modeled as a convex quadratic function of cumulative water-vapor-days displayed distinct cropland dynamics in space and time, including unimodal and bimodal growing seasons. Interannual moisture variability is at its highest at the beginning of the growing season affecting planting times of crops. Moisture time to peak from AMSR and TRMM land surface parameters displayed strong correspondence (r2 > 0.80) and logical lags among variables. Characterizing cropland dynamics based on the synergistic use of complementary remote sensing data should help to advance and improve agricultural monitoring in tropical croplands that are often associated with food insecurity.

Library of Congress Subject Headings

Crops -- Phenology.
Crops -- Seasonal variations.
Tropical crops -- Phenology.
Tropical crops -- Seasonal variations.
Microwave remote sensing.
Land use -- Remote sensing.


Includes bibliographical references



Number of Pages



South Dakota State University



Rights Statement

In Copyright