Downscaling land surface temperature on multi-scale image for drought monitoring

Abstract

Land Surface Temperature (LST) is an important indicator of environmental changes, especially related drought monitoring. It is necessary to accurately detect drought events using advanced technology proved information regarding the drought areas. Remote sensing images have proven to be efficient in detecting drought events. MODIS Terra and Landsat 7 ETM+ (Enhanced Thematic Mapper Plus) and Landsat 8 OLI/TIRS (The Operational Land Imager and the Thermal Infrared Scanner) represent remote imaging images with different spatial resolutions that enable us to provide drought information. However, proper methods are needed to optimize these images for monitoring drought events. The purpose of this study is to find out the ability of multi-scale images provide information about drought monitoring using LST methods. The method used in LST is a Temperature Condition Index (TCI), the Crop Water Stress Index (CWSI), and Principal Component Analysis (PCA). All three equations are selected because they represent a modification of the method for LST input. The results suggest that the three equations used in multi-level imagery have a critical alignment of information regarding the drought. The results show that the drought pattern identified by MODIS Terra image was similar to the one detected by Landsat ETM+ and OLI/TIRS images. However, we found a temperature difference in the dry season (especially in October) between Landsat ETM+ and OLI/TIRS. The degree of LST estimation accuracy between MODIS Terra and Landsat (ETM+ and OLI/TIRS) is indicated by the average difference between the results of those images, which was 1 degree Celsius (1°C). The use of these three equations for drought monitoring with multi-level imagery suggests that there is a positive relationship. This relationship manifests the same pattern, shape, and association that are produced, thus using a common equation for drought monitoring is more focused.

Thermal index effect in forest canopy density (FCD) methods based on remote sensing imagery

“…While at certain altitudes, the land surface temperature is still able to identify the residential area (Figure 3-d). The selection of the SWA method as the acquisition of land surface temperature information on the FCD model based on research by Nugraha et al [12,37]. The research proves that the SWA method has a difference of 1 degree in the field.…”

“…Now the development of the FCD model uses a method known as Principal Component Analysis (PCA). PCA is a technique by combining the number of channels/information on the imagery to create new information with much information derived from the original data [8][9][10][11][12]. PCA method chose because it can change and select variables on the FCD model compared to the FCD Mapper application, and that application can only operate on 32 bits system cannot for 64 bits system.…”

Modification of Temperature Vegetation Dryness Index (TVDI) Method for Detecting Drought with Multi-Scale Image

“…The surface temperature has optimal value because the algorithm used is very specific such as Li and Becker for MODIS Terra image [45], USGS for Landsat 7 [17], and SWA for Landsat 8 [27], [29]. All algorithms for the surface temperature used have a small difference in field temperature of 0.9 ᵒK [11], [13].…”

Pemanfaatan Citra Penginderan Jauh Multi-Temporal Untuk Deteksi Urban Heat Island (Uhi) Terhadap Perubahan Penggunaan Lahan Di Kabupaten Buleleng

“…Metode pengolahan suhu permukaan yang dilakukan untuk Landsat 7 ETM+ dan Landsat 8 OLI/TIRS memiliki perbedaan terhadap algoritma yang digunakan (Nugraha et al, 2019). Seperti pada Landsat 8 OLI/TIRS menggunakan dua band dalam pengolahan dan berbeda dengan Landsat 7 ETM+ yang hanya menggunakan single band sebagai input untuk suhu permukaan Perolehan hasil suhu permukaan untuk kedua citra Landsat dengan menggunakan Persamaan 4 dan Persamaan 5 memiliki input serupa yang berasal dari penerapan indeks vegetasi sebagai nilai emisivitas.…”