Development of regional wheat VI-LAI models using Resourcesat-1 AWiFS data

Abstract

The time of forcing of spatial LAI to crop models at single or multiple stages is important to simulate crop biomass and yield in varying agro-climatic conditions and scales. The high temporal resolution (5-day) by Advanced Wide Field Sensor (AWiFS) on-board Resourcesat-1 Satellite IRS-P6 with 56 m spatial resolution and large swath (740 km) has substantially increased the availability of regional clear sky optical remote sensing data. The present study aimed at developing empirical vegetation index VI-LAI models for wheat using AWiFS optical data in four bands and in-situ measurements sampled over five different agro-climatic regions (ACRs) during 2005-2006 followed by validation during 2006-2007. While nonlinear relations exist for all the three normalized indices such as normalized difference vegetation index (NDVI), normalized difference water index (NDWI) and Green NDVI, linear relation was the best fit for ratio vegetation index (RVI). Both NDVI and RVI models generally showed better correlation ranges (0.65-0.84 for NDVI and 0.37-0.76 for RVI) than other indices. The common NDVI-LAI model was found to produce lower root mean square errors (RMSE) between 0.5 and 1.1 from pooled model than those between 0.5 and 1.32 from regional models. The rate of substantial increase in errors from NDVI-LAI model (RMSE of modelled LAI: 0.85 to 1.28) as compared to RVI-LAI model (RMSE of modelled LAI: 1.12 to 1.17) at LAI greater than 3, than below 3 revealed the early saturation of NDVI than RVI. It is therefore recommended that LAI estimates can be used to force crop simulation model upto early vegetative stage based on NDVI and maximum vegetative to reproductive stages based on RVI.

Vegetation indices mapping for Bhiwani district of Haryana (India) through LANDSAT-7ETM+ and remote sensing techniques

“…In which, northern and southern parts of Bhiwani indicated NDVI between 0.48 -0.6, however, eastern part showed NDVI from 0.2 to 0.3, because of mustard crop toward its harvesting. Similar type of study have been done by Chaurasia et al (2011) using AWiFS data set and found NDVI pattern as per the our result, therefore, NDVI is the most promising VI for simulating biomass and crop A. Bala et al / J. Appl.…”

Using a Portable Active Sensor to Monitor Growth Parameters and Predict Grain Yield of Winter Wheat

“…Vegetation index, like normalized difference vegetation index (NDVI) and ratio vegetation index (RVI), can help enhance the interpretation ability of remote sensing data, and has been widely used as a remote sensing means in land use cover detection [22], vegetation cover density evaluation [23], crop identification [24] and crop growth monitoring [25,26,27]. Among various vegetation indices, NDVI and RVI have been proposed and used for decades, and were most widely used in monitoring plant chlorophyll [28], N content [26], and disease [29,30].…”

High Spatial Resolution Leaf Area Index Estimation for Woodland in Saihanba Forestry Center, China

“…In 2015 and 2017, the European Space Agency (ESA) launched two Sentinel-2 satellites to establish the European Operational earth monitoring system. Compared with other multispectral missions such as MODIS and Landsat, the Sentinel-2 multispectral instrument (MSI) has finer spatial resolution (10,20, and 60 m), more comprehensive spectral sampling (including the red edge band), and a more frequent revisit period (5 days). S2 further improves the temporal, spatial, and spectral resolution of remote sensing data, which has great potential in vegetation monitoring.…”

“…The most common method of parameter regression is to establish parameterized expressions between several spectral bands and vegetation indexes (VIs) [9]. The VI method is very popular in LAI inversion because of its simplicity and fast calculation speed [10][11][12].…”