scholarly journals Land Surface Temperature Retrieval for Agricultural Areas Using a Novel UAV Platform Equipped with a Thermal Infrared and Multispectral Sensor

2020 ◽  
Vol 12 (7) ◽  
pp. 1075
Author(s):  
Sascha Heinemann ◽  
Bastian Siegmann ◽  
Frank Thonfeld ◽  
Javier Muro ◽  
Christoph Jedmowski ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Crop Yields ◽  
Plant Diseases ◽  
Retrieval Algorithm ◽  
Fundamental Parameter ◽  
Agricultural Crop ◽  
Ground Truth Data ◽  
Dual Sensor

Land surface temperature (LST) is a fundamental parameter within the system of the Earth’s surface and atmosphere, which can be used to describe the inherent physical processes of energy and water exchange. The need for LST has been increasingly recognised in agriculture, as it affects the growth phases of crops and crop yields. However, challenges in overcoming the large discrepancies between the retrieved LST and ground truth data still exist. Precise LST measurement depends mainly on accurately deriving the surface emissivity, which is very dynamic due to changing states of land cover and plant development. In this study, we present an LST retrieval algorithm for the combined use of multispectral optical and thermal UAV images, which has been optimised for operational applications in agriculture to map the heterogeneous and diverse agricultural crop systems of a research campus in Germany (April 2018). We constrain the emissivity using certain NDVI thresholds to distinguish different land surface types. The algorithm includes atmospheric corrections and environmental thermal emissions to minimise the uncertainties. In the analysis, we emphasise that the omission of crucial meteorological parameters and inaccurately determined emissivities can lead to a considerably underestimated LST; however, if the emissivity is underestimated, the LST can be overestimated. The retrieved LST is validated by reference temperatures from nearby ponds and weather stations. The validation of the thermal measurements indicates a mean absolute error of about 0.5 K. The novelty of the dual sensor system is that it simultaneously captures highly spatially resolved optical and thermal images, in order to construct the precise LST ortho-mosaics required to monitor plant diseases and drought stress and validate airborne and satellite data.

Estimation of Wheat Yield using Remotely Sensed and Modeled Data over Turkey

2020 ◽  
Author(s):  
Burak Bulut ◽  
M. Tugrul Yilmaz ◽  
Mehdi H. Afshar
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Crop Yields ◽  
Wheat Yield ◽  
Growing Season ◽  
Remotely Sensed ◽  
Agricultural Crop ◽  
Early Assessment ◽  
Yield Data

<p>Monitoring agricultural crop conditions during the growing season and estimating potential crop yields are important for evaluating seasonal production. The accurate and timely assessment of the losses in crop yields caused by a natural disaster, such as drought, may be critical for countries where their economies are reliant on their agricultural productivity. Early assessment of the reduction in crop yields can prevent a catastrophic situation and help meet the demands of strategic planning.</p><p>In this study, the Multiple Linear Regression model was used to estimate the wheat yields in Turkey. Remotely sensed-, model-, and in-situ-based measurements of affecting variables of crop productivity (i.e., precipitation, land surface temperature, soil moisture, wind, and Normalized Vegetation Difference Index) were extracted over selected areas in which yield data were available on them. The datasets are collected using different time scales (e.g., before/during sowing period, growing season, one/two months before harvest, etc.).</p><p>The cross-validation of more than 700 different model combinations over more than total 700 different administrative divisions (i.e., districts, provinces, and regions) showed that by using the best model selected for each district, on average, a correlation value of 0.65 and a mean absolute error of 35 kg/da can be obtained between estimated and observed yield values. While, this consistency is more pronounced over the districts located in the Central Anatolia region where the average production of the wheat in them is more than the rest of districts in the country. Overall, regional differences of the selected predictors of observed yield data, suggest that the land surface temperature can provide a useful exploratory and predictive tool for wheat yield estimation across the country.</p>

scholarly journals Land Surface Temperature Retrieval from LANDSAT-8 Thermal Infrared Sensor Data and Validation with Infrared Thermometer Camera

2018 ◽  
Vol 7 (4.20) ◽  
pp. 608 ◽  
Author(s):  
Muhammad Mejbel Salih ◽  
Oday Zakariya Jasim ◽  
Khalid I. Hassoon ◽  
Aysar Jameel Abdalkadhum
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Thermal Infrared ◽  
Landsat 8 ◽  
Infrared Sensor ◽  
Infrared Thermometer ◽  
Ground Truth Data ◽  
Land Cover Types

This paper illustrates a proposed method for the retrieval of land surface temperature (LST) from the two thermal bands of the LANDSAT-8 data. LANDSAT-8, the latest satellite from Landsat series, launched on 11 February 2013, using LANDSAT-8 Operational Line Imager and Thermal Infrared Sensor (OLI & TIRS) satellite data. LANDSAT-8 medium spatial resolution multispectral imagery presents particular interest in extracting land cover, because of the fine spectral resolution, the radiometric quantization of 12 bits. In this search a trial has been made to estimate LST over Al-Hashimiya district, south of Babylon province, middle of Iraq. Two dates images acquired on 2nd &18th of March 2018 to retrieve LST and compare them with ground truth data from infrared thermometer camera (all the measurements contacted with target by using type-k thermocouple) at the same time of images capture. The results showed that the rivers had a higher LST which is different to the other land cover types, of less than 3.47 C ◦, and the LST different for vegetation and residential area were less than 0.4 C ◦ with correlation coefficient of the two bands 10 and 11 Rbnad10= 0.70, Rband11 = 0.89 respectively, for the imaged acquired on the 2nd of march 2018 and Rband10= 0.70 and Rband11 = 0.72 on the 18th of march 2018. These results confirm that the proposed approach is effective for the retrieval of LST from the LANDSAT-8 Thermal bands, and the IR thermometer camera data which is an effective way to validate and improve the performance of LST retrieval. Generally the results show that the closer measurement taken from the scene center time, a better quality to classify the land cover. The purpose of this study is to assess the use of LANDSAT-8 data to specify temperature differences in land cover and compare the relationship between land surface temperature and land cover types.   

scholarly journals Deep Learning Convolutional Neural Network for the Retrieval of Land Surface Temperature from AMSR2 Data in China

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2987 ◽  
Author(s):  
Jiancan Tan ◽  
Nusseiba NourEldeen ◽  
Kebiao Mao ◽  
Jiancheng Shi ◽  
Zhaoliang Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Surface Temperature ◽  
Convolutional Neural Network ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Measurement Data ◽  
Retrieval Algorithm ◽  
Observation Data ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Better Than

A convolutional neural network (CNN) algorithm was developed to retrieve the land surface temperature (LST) from Advanced Microwave Scanning Radiometer 2 (AMSR2) data in China. Reference data were selected using the Moderate Resolution Imaging Spectroradiometer (MODIS) LST product to overcome the problem related to the need for synchronous ground observation data. The AMSR2 brightness temperature (TB) data and MODIS surface temperature data were randomly divided into training and test datasets, and a CNN was constructed to simulate passive microwave radiation transmission to invert the surface temperature. The twelve V/H channel combinations (7.3, 10.65, 18.7, 23.8, 36.5, 89 GHz) resulted in the most stable and accurate CNN retrieval model. Vertical polarizations performed better than horizontal polarizations; however, because CNNs rely heavily on large amounts of data, the combination of vertical and horizontal polarizations performed better than a single polarization. The retrievals in different regions indicated that the CNN accuracy was highest over large bare land areas. A comparison of the retrieval results with ground measurement data from meteorological stations yielded R2 = 0.987, RMSE = 2.69 K, and an average relative error of 2.57 K, which indicated that the accuracy of the CNN LST retrieval algorithm was high and the retrieval results can be applied to long-term LST sequence analysis in China.

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