Monitoring biophysical parameters of irrigated rice production in the lowlands of the Aral Sea Basin from space

Abstract

Rice production in the lowlands of the Aral Sea Basin (ASB) contributes to the national food baskets of Kazakhstan and Uzbekistan. However, due to the exposure to water scarcity in the downstream location of the river systems and enormous land degradation problems, a high risk of crop failure exist. Early and area-wide information on irregularities in rice crop growth through remote sensing could help to reduce this risk. In this study, statistical relations between vegetation indices (VIs) from Landsat 8 surface reflectance and biophysical in situ measurements (plant height, crop density, green biomass, fraction of absorbed photosynthetically active radiation and leaf area index) of broadcast sown and transplanted rice were investigated. Special attention laid on the accurate derivation of the LAI. Field experiments conducted in three different irrigation subsystems of the ASB during the growing period of rice in 2015 revealed enormous spatial variations in observed rice biophysical parameters, both among the fields and sites owing to different land, water and crop management. Linear regression analysis techniques of paired variables comprising each one biophysical parameter and one out of six VIs showed diverse coefficients of determination. Tasseled Cap Greenness (TCG) was superior to all other indices in its explanatory power. Multivariate linear regression and particularly Classification and Regression Trees exhibited stronger statistical relations between four VIs (TCG, Normalized Difference Vegetation Index - NDVI, Transformed Vegetation Index - TVI and Green Chlorophyll Index - GCI) and LAI than the univariate assessments. The multi-location field experiments can be concluded to be useful for estimating certain effects of crop management on modeling biophysical parameters of rice. The results suggest furthering multivariate assessments of rice biophysical parameters and promoting remote sensing techniques to support local and regional policies and planning approaches in the irrigated lowland of the ASB.