Optical Remote Sensing Indexes of Soil Moisture: Evaluation and Improvement Based on Aircraft Experiment Observations

Sun, Hao; Líu, Hao; Yan-hui, MA; Xia, Qunbo

doi:10.3390/rs13224638

Cited by 11 publications

(4 citation statements)

References 41 publications

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“…Acharya et al [52] obtained a weak relationship between soil moisture and vegetation indices (NDVI, NDMI, NDWI, EVI, ARVI, SIPI) with R 2 ranging from 2 × 10 −6 to 0.072. Sun et al [53] obtained the best performance among the thirteen optical soil moisture indices as compared with aircraft and in situ observed soil moisture for the visible and short-wave infrared drought index VSDI, which was comparable with the OPTRAM result. Further research is required to validate the OPTRAM model and to check the accuracy of the vegetation indices.…”

Section: Vegetation Indices Accuracymentioning

confidence: 58%

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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Section: Vegetation Indices Accuracymentioning

confidence: 58%

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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“…The smaller difference between the aggregated SM and original SM demonstrates the better performance of the downscaling method. Additionally, four metrics were used in the evaluation including unbiased root-mean-square error (ubRMSE), root-mean-square error (RMSE), bias (defined as estimated data minus reference data), and R. The equations of these metrics can be found in [12].…”

Section: Evaluation Methodsmentioning

confidence: 99%

“…Therefore, the remote sensing methods of SM can be roughly classified into optical data-dominated methods, thermal data-dominated methods, and microwave data-dominated methods. The optical datadominated methods are chiefly based on the spectral reflectance characteristics of soil and vegetation to identify varied SM statuses [12]. The thermal data-dominated methods are primarily based on the thermal radiation or thermal inertia of soil and vegetation to describe the variation of SM [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Series or Parallel? An Exploration in Coupling Physical Model and Machine Learning Method for Disaggregating Satellite Microwave Soil Moisture

Sun

Zhang

Zhao

2022

IEEE Trans. Geosci. Remote Sensing

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Remotely sensed soil moisture (SM) dataset with well accuracy and fine spatiotemporal resolution is very valuable in various fields. Downscaling is a promising way to obtain such an SM dataset. There are currently two basic methodologies in the downscaling with satellite datasets, i.e., the machine learning (ML) methods and the physical or semi-physical (PH) models. This study focuses on exploring feasible ways to integrate them for boosting the performance of downscaling. Here, three parallel modes i.e., arithmetic average (Ari), geometric average (Geo), and weighted average (Wei), and two series modes i.e., first PH then ML (PH-ML) and first ML then PH (ML-PH) were developed and evaluated. A representative of PH, DSCALE_mod16, and three ML algorithms Random Forest, XGBoost, and LightGBM were used in this study. Microwave SM datasets from Soil Moisture Active and Passive Mission (SMAP), satellite datasets from MODIS, and in-situ SM observations spanning from April 2015 to December 2018 were employed in the evaluation. Spatial dynamic range, energy conservation, and precision preservation analyses were conducted as evaluation methods. Results demonstrated the PH-ML series mode outperformed the other coupling modes, which was even better than the better one of the PH and ML. The PH can first generate a valuable initial estimate, and the initial estimate's advantages can be preserved while its errors can be suppressed by the ML. Resultantly, the ideology of first using the PH to obtain an initial estimate and then putting the initial estimate into the ML is suggested for further microwave SM downscaling.

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“…The sites are in Ireland, a country which typifies a temperate, high-rainfall climate [29] with intensive agriculture. OPTRAM has been used to estimate soil moisture from Landsat [16], S-2 [30,31] and MODIS [32][33][34]. These studies were conducted in crop fields in arid or semi-arid climates, which are quite different from the climatic conditions in this study.…”

Section: Introductionmentioning

confidence: 99%

Temporal Stability of Grassland Soil Moisture Utilising Sentinel-2 Satellites and Sparse Ground-Based Sensor Networks

Basu,

Daly,

Brown

et al. 2024

Remote Sensing

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Soil moisture is important for understanding climate, water resources, water storage, and land use management. This study used Sentinel-2 (S-2) satellite optical data to retrieve surface soil moisture at a 10 m scale on grassland sites with low hydraulic conductivity soil in a climate dominated by heavy rainfall. Soil moisture was estimated after modifying the Optical Trapezoidal Model to account for mixed land cover in such conditions. The method uses data from a short-wave infra-red band, which is sensitive to soil moisture, and four vegetation indices from optical bands, which are sensitive to overlying vegetation. Scatter plots of these data from multiple, infrequent satellite passes are used to define the range of surface moisture conditions. The saturated and dry edges are clearly non-linear, regardless of the choice of vegetation index. Land cover masks are used to generate scatter plots from data only over grassland sites. The Enhanced Vegetation Index demonstrated advantages over other vegetation indices for surface moisture estimation over the entire range of grassland conditions. In poorly drained soils, the time lag between satellite surface moisture retrievals and in situ sensor soil moisture at depth must be part of the validation process. This was achieved by combining an approximate solution to the Richards’ Equation, along with measurements of saturated and residual moisture from soil samples, to optimise the correlations between measurements from satellites and sensors at a 15 cm depth. Time lags of 2–4 days resulted in a reduction of the root mean square errors between volumetric soil moisture predicted from S-2 data and that measured by in situ sensors, from ~0.1 m3/m3 to <0.06 m3/m3. The surface moisture results for two grassland sites were analysed using statistical concepts based upon the temporal stability of soil water content, an ideal framework for the intermittent Sentinel-2 data in conditions of persistent cloud cover. The analysis could discriminate between different natural drainages and surface soil textures in grassland areas and could identify sub-surface artificial drainage channels. The techniques are transferable for land-use and agricultural management in diverse environmental conditions without the need for extensive and expensive in situ sensor networks.

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Optical Remote Sensing Indexes of Soil Moisture: Evaluation and Improvement Based on Aircraft Experiment Observations

Cited by 11 publications

References 41 publications

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

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

Series or Parallel? An Exploration in Coupling Physical Model and Machine Learning Method for Disaggregating Satellite Microwave Soil Moisture

Temporal Stability of Grassland Soil Moisture Utilising Sentinel-2 Satellites and Sparse Ground-Based Sensor Networks

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