OPTRAM-ET: A novel approach to remote sensing of actual evapotranspiration applied to Sentinel-2 and Landsat-8 observations

Mokhtari, Ali; Sadeghi, Morteza; Afrasiabian, Yasamin; Kang, Yu

doi:10.1016/j.rse.2022.113443

Cited by 27 publications

(7 citation statements)

References 75 publications

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“…This model uses vegetation index (VI) and the Shortwave Transformed Infrared Reflectance (STR), calculated from short wave infrared (SWIR) band to map soil moisture in the STR-VI space., STR has been tested successfully for its sensitivity to soil moisture over vegetated surfaces, assuming a linear relationship between soil and vegetation water content. Though, originally developed over arid and semi-arid climatic conditions, in recent years, OPTRAM has been successfully applied over a variety of land cover including wetlands and peatlands (Burdun et al, 2020;Burdun et al, 2023;Mokhtari et al, 2023). Similarly, Basu et al, 2024 also applied OPTRAM to estimate soil moisture in the same study site as in this paper, which is also dominated by wet conditions.…”

Section: Optrammentioning

confidence: 93%

Identifying favourable conditions for farm scale trafficability and grass growth using a combined Sentinel-2 and soil moisture deficit approach

Basu,

Fenton,

Daly

et al. 2024

Front. Environ. Sci.

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In Atlantic Europe, on poorly drained grasslands soils, compaction negatively affects soil health when trafficked in wet conditions, while optimum grass growth cannot be achieved in excessively dry conditions. In Ireland, daily soil moisture deficit (SMD) information is forecasted at regional scale for all soil drainage classes. Optimal paddock conditions can occur between trafficking (10 mm) and optimum grass growth (50 mm) SMD thresholds for an identified drainage class. The objective of this farm scale study is to improve the identification of optimum conditions in time and space by combining high resolution spatial soil moisture estimates with soil drainage class specific SMD data. For that purpose, Sentinel- 2 (S-2) data was used in a modified Optical Trapezoid Model (OPTRAM) to derive normalised surface soil moisture (nSSM) estimates at farm level. In-situ soil moisture sensors providing daily estimates of volumetric soil moisture were used for validation of OPTRAM with an RMSE of 0.05. Cumulative 7-day SMD prior to the date of each S-2 image was analysed for each year from 2017-2021 to select nSSM maps corresponding to negative, 0 or −0 and positive SMD. Results established a relationship between nSSM and SMD indicating optimal conditions changed spatially and temporally. The months of April, May, August and September always presented at least 35% of the farm area available for optimum management operations. Future refinement of this methodology utilising daily high resolution remote sensing data could provide near real-time information for farmers.

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Section: Optrammentioning

confidence: 93%

Identifying favourable conditions for farm scale trafficability and grass growth using a combined Sentinel-2 and soil moisture deficit approach

Basu,

Fenton,

Daly

et al. 2024

Front. Environ. Sci.

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“…In the calculation of STR, commonly used spectral bands include Landsat-8 Band 7, Sentinel-2 Band 12 and MODIS Band 7 [40][41][42][43][44]. For this study, SWIR in Equation ( 5) refers to Landsat-8 Band 7 (2200 nm).…”

Section: Calculation Of Shortwave Infrared-transformed Reflectance (Str)mentioning

confidence: 99%

Feasibility of Using SWIR-Transformed Reflectance (STR) in Place of Surface Temperature (Ts) for the Mapping of Irrigated Landcover

Abuzar,

Sheffield,

McAllister

2024

Land

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(1) Background: A simple approach to map irrigated landcover has been introduced by using measures derived from the optical spectral range as an alternative to the thermal range. It has been demonstrated that substituting surface temperature (Ts, ‘thermal approach’) with SWIR-transformed reflectance (STR, ‘optical approach’) to detect surface moisture is feasible with comparable results. (2) Methods: Using an iterative thresholding procedure to minimize within-class variance, the bilevel segmentation of variables derived from Landsat-8 representing surface moisture and vegetation cover was achieved for the 2020–2021 summer for a key irrigation district in Australia. (3) Results: The results of irrigated landcover by the optical approach were found to be comparable with those obtained by the thermal approach. The classification accuracy was assessed using water delivery records at the farm level. Although the overall accuracy was high in both cases, the optical approach (97.6%) performed slightly better than the thermal approach (93.9%). (4) Conclusions: The feasibility of using STR to map irrigated landcover has been confirmed by a high-level overall accuracy assessment. This has broader implications in terms of irrigated landcover assessment, as the use of satellite imagery in these applications may not necessarily be limited to microwave or thermal sensors.

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“…The OPTRAM model was mainly used to estimate soil moisture and has been used in various climatic conditions (e.g., United States [30,44], Italy [32], China [45], Hungary [46], Iran [47], Ethiopia [48], etc.). OPTRAM has also been used successfully in other applications, i.e., for estimating actual evapotranspiration [49], for water table depth monitoring in bogs [50], or for estimation of dust emission probability in the lake basin [51]. In this study, the optical trapezoidal model method was used as a new method, which has not been used so far in Poland for determining SSM in three different agricultural areas with irrigation and drainage systems.…”

Section: Optram Model Applicationmentioning

confidence: 99%

Surface Soil Moisture Determination of Irrigated and Drained Agricultural Lands with the OPTRAM Method and Sentinel-2 Observations

Stańczyk,

Kasperska-Wołowicz,

Szatyłowicz

et al. 2023

Remote Sensing

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Surface soil moisture (SSM) is one of the factors affecting plant growth. Methods involving direct soil moisture measurement in the field or requiring laboratory tests are commonly used. These methods, however, are laborious and time-consuming and often give only point-by-point results. In contrast, SSM can vary across a field due to uneven precipitation, soil variability, etc. An alternative is using satellite data, for example, optical data from Sentinel-2 (S2). The main objective of this study was to assess the accuracy of SSM determination based on S2 data versus standard measurement techniques in three different agricultural areas (with irrigation and drainage systems). In the field, we measured SSM manually using non-destructive techniques. Based on S2 data, we estimated SSM using the optical trapezoid model (OPTRAM) and calculated eighteen vegetation indices. Using the OPTRAM model gave a high SSM estimating accuracy (R2 = 0.67, RMSE = 0.06). The use of soil porosity in the OPTRAM model significantly improved the results. Among the vegetation indices, at the NDVI ≤ 0.2, the highest value of R2 was obtained for the STR to OPTRAM index, while at the NDVI > 0.2, the shadow index had the highest R2 comparable with OPTRAM.

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OPTRAM-ET: A novel approach to remote sensing of actual evapotranspiration applied to Sentinel-2 and Landsat-8 observations

Cited by 27 publications

References 75 publications

Identifying favourable conditions for farm scale trafficability and grass growth using a combined Sentinel-2 and soil moisture deficit approach

Identifying favourable conditions for farm scale trafficability and grass growth using a combined Sentinel-2 and soil moisture deficit approach

Feasibility of Using SWIR-Transformed Reflectance (STR) in Place of Surface Temperature (Ts) for the Mapping of Irrigated Landcover

Surface Soil Moisture Determination of Irrigated and Drained Agricultural Lands with the OPTRAM Method and Sentinel-2 Observations

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