Field Scale Assessment of the TsHARP Technique for Thermal Sharpening of MODIS Satellite Images Using VENµS and Sentinel-2-Derived NDVI

Lorena N Lacerda,Yafit Cohen,John Snider,Hanna Huryna,George Vellidis,Vasileios Liakos

doi:10.3390/rs13061155

Abstract

Remotely sensed-based surface temperature is an important tool for crop monitoring and has great potential for improving irrigation management. However, current thermal satellite platforms do not display the fine spatial resolution required for identifying crop water status patterns at the field scale. The thermal sharpening (TsHARP) utility provides a technique for downscaling coarse thermal images to match the finer resolution of images acquired in the visible and near infrared bandwidths. This sharpening method is based on the inverse linear relationship between vegetation fraction calculated from the normalized difference vegetation index (NDVI) and land surface temperature (LST). The current study used the TsHARP method to sharpen low-resolution thermal data from the Moderate Resolution Imaging Spectrometer MODIS (1 km) to the finer resolution of Sentinel-2 (10 m) and Vegetation and Environment New micro-Spacecraft (VENµS) (5 m) visible-near infrared images. The sharpening methodology was evaluated at scene and field scales in southern Georgia and northern Mississippi, USA. A comparison of sharpened temperature was made with reference temperatures from Landsat-8 Operational Land Imager (OLI) in four different spatial resolutions (30, 60, 120, and 240 m) for method validation. Coarse resolution comparison on the dates in which imagery from both sensors were acquired on the same day resulted in average observed mean absolute error (MAE) of 1.63 °C, and R2 variation from 0.34 to 0.74. Temperature errors at the field scale ranged from 0.25 to 3.11 °C using both Sentinel-2 and VENµS. Sharpened maps at 120 and 60 m resolution showed the highest consistency for all fields and dates. Maps sharpened using VENµS images showed comparable or higher accuracy than maps sharpened using Sentinel-2. The superior performance coupled with the better revisit time indicates that the VENµS platform has high potential for frequent in-season crop monitoring. Further research with ground data collection is needed to explore field use limitations of this methodology, but these results give useful insights of potential benefits of implementing the TsHARP technique as a tool for crop stress monitoring.

Highlights

Thermal remote sensing uses thermal infrared (TIR) bands in the 8 to 14 μm region of the electromagnetic spectrum in which atmospheric absorption effects are attenuated [1].At the molecular level, plant absorption in the TIR bands is influenced by the absorption properties of leaf tissues driven by molecular vibration [2]
The thermal sharpening validation was performed for a total of 14 dates using the MODIS/Sentinel-2 and MODIS/VENμS brightness temperature (BT)-normalized difference vegetation index (NDVI) regressions (Table 1)
Results showed that the correlations between the BT of the two sensors were similar towards the end of the season when average temperatures were in the low 20 ◦ C or below, with coefficients of determination (R2 ) ranging from 0.68 in scene 1 to 0.74 in scene

Summary

Introduction

Thermal remote sensing uses thermal infrared (TIR) bands in the 8 to 14 μm region of the electromagnetic spectrum in which atmospheric absorption effects are attenuated [1]. Plant absorption in the TIR bands is influenced by the absorption properties of leaf tissues driven by molecular vibration [2]. 2021, 13, 1155 that exhibit isohydric behavior tend to close their stomata to maintain leaf water potential (LWP). This decreases their transpiration rate significantly, leading to an increase in leaf temperature [4]. The direct relationship between changes in leaf temperature resulting from changes in LWP makes TIR bands more sensitive to detecting water stress than other regions of the electromagnetic spectrum. Thermal imagery has become increasingly important in monitoring of water status for irrigation management [5] in crops such as coffee [6], cereals [7,8], and soybean and cotton [9]

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