Evaluation of Landsat-8 TIRS data recalibrations and land surface temperature split-window algorithms over a homogeneous crop area with different phenological land covers

Niclòs, Raquel; Puchades, Jesús; Coll, César; Barberà, María Jesús; Pérez-Planells, Lluís; Valiente, José; Sánchez, Juan Manuel

doi:10.1016/j.isprsjprs.2021.02.005

Cited by 27 publications

(11 citation statements)

References 48 publications

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“…These values were averaged and the interpolation technique IDW was applied on 31 locations to map the trend of humidity as shown in Figure 2 as below, 21-30 April 2020 PMD generated rainfall maps which were scanned, geo-referenced and digitized to extract actionable information regarding spatial distribution of rain. Landsat 8 datasets are comprised of two thermal bands including Band 10 and Band 11 [21] which provide very precise and reliable information regarding spatial distribution of temperature [22]. We downloaded these thermal bands for the complete wheat growth period and computed pixel-based temperature values.…”

Section: Figure 1 Flow Of Methodologymentioning

confidence: 99%

Detecting Aphid Concentration in Wheat Leaf Using Remote Sensing and GIS

Khan

Batool

Rana

et al. 2022

IJIST

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Wheat lies among the most prominent cereal crop of Pakistan which has a significant role in the stability of Pakistan’s economy. Certain biotic and a biotic factors including agro-climatic conditions, rainfall, lack of irrigation infrastructure and conventional agricultural methods are raising the risk of aphid attacks. The current study utilized satellite imagery for obtaining thermal datasets of complete wheat growth. Results revealed that rainfall is a significant parameter for the determination of aphid growth on wheat plant. A region receiving 0-10 mm rainfall, supported the growth of aphid. Moreover, the aphid survival was highly supported at a moderate temperature ranging between 20-25 oC with relative humidity ranging from 70-75 %. The study also revealed the production of weed in wheat crop acted as a moistrizing agent which consequently provided favorable conditions to the aphid population for growth. Inapropriate usage of fertilizers increased the nitrogen content in soil which turned to be favourable for the aphid attack. Thus, the study concludes that agro-climatic conditions must be considered before the application of pesticides.

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Section: Figure 1 Flow Of Methodologymentioning

confidence: 99%

Detecting Aphid Concentration in Wheat Leaf Using Remote Sensing and GIS

Khan

Batool

Rana

et al. 2022

IJIST

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“…This site has been extensively used for LST validation purposes [15,[25][26][27][28]. Previous studies demonstrated a high thermal homogeneity for this site at different spatial resolutions [27,[29][30][31] and concluded that it is suitable for validating satellite LST with in-situ measurements.…”

Section: Study Site and Ground Data 21 Sitementioning

confidence: 98%

“…In [30], the authors analyzed the variability of 11 × 11 ASTER pixels (1 km 2 ) centered on the study area, and obtained a SD < 0.3 K. In [27], the thermal variability of the area was studied for the three land covers present at the site with hand-held radiometer measurements along transects (~300 m long) through the station parcel on different dates: the SD values obtained were 0.5 K, 0.4 K, and 0.9 K for full vegetation, flooded soil, and bare soil, respectively. This site has been extensively used for LST validation purposes [15,[25][26][27][28]. Previous studies demonstrated a high thermal homogeneity for this site at different spatial resolutions [27,[29][30][31] and concluded that it is suitable for validating satellite LST with in-situ measurements.…”

Section: Study Site and Ground Data 21 Sitementioning

confidence: 98%

Validation of Sentinel-3 SLSTR Land Surface Temperature Retrieved by the Operational Product and Comparison with Explicitly Emissivity-Dependent Algorithms

et al. 2021

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Land surface temperature (LST) is an essential climate variable (ECV) for monitoring the Earth climate system. To ensure accurate retrieval from satellite data, it is important to validate satellite derived LSTs and ensure that they are within the required accuracy and precision thresholds. An emissivity-dependent split-window algorithm with viewing angle dependence and two dual-angle algorithms are proposed for the Sentinel-3 SLSTR sensor. Furthermore, these algorithms are validated together with the Sentinel-3 SLSTR operational LST product as well as several emissivity-dependent split-window algorithms with in-situ data from a rice paddy site. The LST retrieval algorithms were validated over three different land covers: flooded soil, bare soil, and full vegetation cover. Ground measurements were performed with a wide band thermal infrared radiometer at a permanent station. The coefficients of the proposed split-window algorithm were estimated using the Cloudless Land Atmosphere Radiosounding (CLAR) database: for the three surface types an overall systematic uncertainty (median) of –0.4 K and a precision (robust standard deviation) 1.1 K were obtained. For the Sentinel-3A SLSTR operational LST product, a systematic uncertainty of 1.3 K and a precision of 1.3 K were obtained. A first evaluation of the Sentinel-3B SLSTR operational LST product was also performed: systematic uncertainty was 1.5 K and precision 1.2 K. The results obtained over the three land covers found at the rice paddy site show that the emissivity-dependent split-window algorithms, i.e., the ones proposed here as well as previously proposed algorithms without angular dependence, provide more accurate and precise LSTs than the current version of the operational SLSTR product.

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“…Similar with Landsat 8 TIRS-1, Landsat 9 TIRS-2 has two TIR bands, i.e., band 10 ranged from 9.8 to 11.8 μm and band 11 ranged from 11 to 13 μm. Some previous studies retrieve LST from Landsat 8 TIRS-1 data using multiple single channel methods [20], [21], [22], [23], [24], [25], [26], [27]. In addition, some scholars have recently tried to use split window (SW) algorithm to retrieve LST from Landsat 8 TIR data after stray light correction and achieved comparable LST retrieval accuracy with single-channel algorithm [26], [27], [28].…”

Section: Introductionmentioning

confidence: 99%

“…Some previous studies retrieve LST from Landsat 8 TIRS-1 data using multiple single channel methods [20], [21], [22], [23], [24], [25], [26], [27]. In addition, some scholars have recently tried to use split window (SW) algorithm to retrieve LST from Landsat 8 TIR data after stray light correction and achieved comparable LST retrieval accuracy with single-channel algorithm [26], [27], [28]. Following Landsat 8 TIRS-1, USGS recently released the analysis ready data (ARD) LST product developed from Landsat 9 TIRS-2 data using the operational single-channel method [23].…”

Section: Introductionmentioning

confidence: 99%

Land Surface Temperature Retrieval From Landsat 9 TIRS-2 Data Using Radiance-Based Split-Window Algorithm

Wang

Zhang

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The thermal infrared sensor-2 (TIRS-2) carried on Landsat 9 is the newest thermal infrared (TIR) sensor for the Landsat project and provides two adjacent TIR bands, which greatly benefits the land surface temperature (LST) retrieval at high spatial resolution. In this article, a radiance based split window (RBSW) algorithm for retrieving LST from Landsat 9 TIRS-2 data was proposed. In addition, the split-window covariance-variance ratio (SWCVR) algorithm was improved and applied to Landsat 9 TIRS-2 data for estimating atmospheric water vapor (AWV) that is required for accurate LST retrieval. The performance of the proposed method was assessed using the simulation data and satellite observations. Results reveal that the retrieved LST using the RBSW algorithm has a bias of 0.06 K and root-mean-square error (RMSE) of 0.51 K based on validation with the simulation data. The sensitivity analysis exhibited a LST error of <1.75 K using the RBSW algorithm when the uncertainties in input parameters (i.e., AWV, emissivity, and at-sensor radiance) were considered. There is a marginal discrepancy between LST retrievals using the estimated AWV and moderate resolution imaging spectroradiometer AWV, with a difference in bias of −0.14 K and RMSE of 0.22 K, which indicates that the improved SWCVR method can provide an optional means to obtain AWV inputted in LST retrieval. With regard to the validation using the in situ measurements, the retrieved LST

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Evaluation of Landsat-8 TIRS data recalibrations and land surface temperature split-window algorithms over a homogeneous crop area with different phenological land covers

Cited by 27 publications

References 48 publications

Detecting Aphid Concentration in Wheat Leaf Using Remote Sensing and GIS

Detecting Aphid Concentration in Wheat Leaf Using Remote Sensing and GIS

Validation of Sentinel-3 SLSTR Land Surface Temperature Retrieved by the Operational Product and Comparison with Explicitly Emissivity-Dependent Algorithms

Land Surface Temperature Retrieval From Landsat 9 TIRS-2 Data Using Radiance-Based Split-Window Algorithm

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