New Textural Indicators for Assessing Above-Ground Cotton Biomass Extracted from Optical Imagery Obtained via Unmanned Aerial Vehicle

Pengfei Chen,Fangyong Wang

doi:10.3390/rs12244170

Abstract

Although textural information can be used to estimate vegetation biomass, its use for estimating crop biomass is rare, and previous methods lacked a mechanistic explanation for the relationship to biomass. The objective of the present study was to develop mechanistic textural indices for estimating cotton biomass and solving saturation problems at medium and high biomass levels. A nitrogen (N) fertilization experiment was established, and unmanned aerial vehicle optical images and field measured biomass data were obtained during critical cotton growth stages. Based on these data, two textural indices, namely the normalized difference texture index combining contrast and the inverse difference moment of the green band (NBTI (CON, IDM)g) and normalized difference texture index combining entropy and the inverse difference moment of the green band (NBTI (ENT, IDM)g), were proposed by analyzing the mechanism of texture parameters for biomass prediction and the law of texture parameters changing with biomass. These indices were compared with spectral indices commonly used for biomass estimation using independent validation data, such as the normalized difference vegetation index (NDVI). The results showed that the proposed textural indices performed better than the spectral indices with no saturation problems occurring. The combination of spectral and textural indices using a stepwise regression method performed better for biomass estimation than using only spectral or textural indices. This method has considerable potential for improving the accuracy of biomass estimations for the subsequent delineation of precise cotton management zones.

Highlights

Estimations of aboveground biomass act as an important indicator of crop vigor [1]
The objectives of this study were as follows: (i) to examine the possibility of estimating the biomass of cotton based on textural features within images obtained from a Unmanned aerial vehicles (UAVs); (ii) to design a meaningful textural index that can be used to estimate the biomass of cotton; and (iii) to develop a biomass estimation model using a combination of spectral and textural information
The normalized difference vegetation index (NDVI), GNDVI, MSAVI, OSAVI, EVI, and MTVI2 had saturation problems

Summary

Introduction

Estimations of aboveground biomass act as an important indicator of crop vigor [1] This indicator can be used to conduct crop yield forecasts [2], delineate management zones, and determine areas that require higher or lower amounts of fertilizer and herbicide (thereby increasing the income of farmers and reducing environmental pollution) [3]. For precision farming, these estimations must be made in a non-destructive manner, such that many studies have documented the use of remote sensing technology as an ideal tool for use in obtaining such information [4,5,6]. New data acquisition platforms must be developed to support the acquisition of relevant data

Objectives

Methods

Results

Conclusion