Estimation of grassland height using optical and SAR remote sensing data

Abstract

Grassland height is an important indicator used to evaluate ecological environments in grasslands. The study explored the potential of optical and SAR (synthetic aperture radar) remote sensing data in estimating grassland height with machine learning methods and constructed the grassland height remote sensing inversion model. Then the mean grassland height in August 2015 was estimated with the inversion model in Inner Mongolia Autonomous Region, China. Among classification and regression tree (CART), random forest (RF), and gradient boosting regression tree (GBRT) models, the GBRT model had the highest estimation accuracy. There was little correlation between SAR data and grassland height, and SAR data produced poor accuracy for grassland height estimation. The grassland height could be better estimated by optical remote sensing data. The best estimation accuracy (GBRT model: for training data: R2 = 0.71, RMSE = 3.58 cm, P < 0.01; for test data: R2 = 0.58, RMSE = 3.94 cm, P < 0.01) was achieved by the combination of optical and SAR remote sensing data. However, SAR data played an auxiliary role, and the estimation of grassland height was mainly realized by optical data. The average vegetation height of the grasslands in Inner Mongolia in August 2015 was 19.15 cm, gradually decreasing from northeast to southwest. The study proposed a high-precision method for estimating grassland height, which provided a basis for studying grassland environments and conditions.