Soil Moisture Monitoring Based on Land Surface Temperature-Vegetation Index Space Derived from MODIS Data

Feng, Zhang; Zhang, Liwen; Shi, Jingjing; Huang, Jingfeng

doi:10.1016/s1002-0160(14)60031-x

Cited by 56 publications

(35 citation statements)

References 28 publications

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“…The scatter plot of LST and NDVI results in a trapezoidal space [29,47,48]. As Figure 3 shows, each part of this space shows information about canopy water status and soil moisture i.e., the dry and wet edges can be applied to obtain information about water status of the orchard.…”

Section: Lst-ndvi Space and Tvdi Calculationmentioning

confidence: 99%

Tree Water Status in Apple Orchards Measured by Means of Land Surface Temperature and Vegetation Index (LST–NDVI) Trapezoidal Space Derived from Landsat 8 Satellite Images

2019

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In this study, the split window (SW) method was applied for land surface temperature (LST) retrieval using Landsat 8 in two apple orchards (Glindow, Altlandsberg). Four images were acquired during high demand of irrigation water from July to August 2018. After pre-processing images, the normalized difference vegetation index (NDVI) and LST were calculated by red, NIR, and thermal bands. The results were validated by interpolated infrared thermometer (IRT) measurements using the inverse distance weighting (IDW) method. In the next step, the temperature vegetation index (TVDI) was calculated based on the trapezoidal NDVI/LST space to determine the water status of apple trees in the case studies. Results show good agreement between interpolated LST using IRT measurements and remotely sensed LST calculation using SW in all satellite overpasses, where the absolute mean error was between 0.08 to 4.00 K and root mean square error (RMSE) values ranged between 0.71 and 4.23 K. The TVDI spatial distribution indicated that the trees suffered from water stress on 7 and 23 July and 8 August 2018 in Glindow apple orchard with the mean value of 0.69, 0.57, and 0.73, whereas in the Altlandsberg orchard on 17 August, the irrigation system compensated the water deficit as indicated by the TVDI value of 0.34. Moreover, a negative correlation between TVDI and vegetation water content (VWC) with correlation coefficient (r) of −0.81 was observed. The corresponding r for LST and VWC was equal to −0.89, which shows the inverse relation between water status and temperature-based indices. The results indicate that the LST and/or TVDI calculation using the proposed methods can be effectively applied for monitoring tree water status and support irrigation management in orchards using Landsat 8 satellite images without requiring ground measurements.

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Section: Lst-ndvi Space and Tvdi Calculationmentioning

confidence: 99%

Tree Water Status in Apple Orchards Measured by Means of Land Surface Temperature and Vegetation Index (LST–NDVI) Trapezoidal Space Derived from Landsat 8 Satellite Images

2019

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“…The coefficients e and f of the regression equation (see Equation (4) Earlier studies used TVDI to estimate soil moisture from different remote sensing data and produced different R 2 values. These different remote sensing data are NOAA-AVHRR images (R 2 = 0.23-0.81) [10,16] and Terra/Aqua MODIS images (R 2 = 0.12-0.83) [7,8,[17][18][19][20]. Gao et al [21] used LANDSAT-5 TM images, but they did not calculate R 2 values.…”

Section: Comparison Of Tvdi With In Situ Measurementsmentioning

confidence: 99%

“…Following this, different satellite images have been used to demonstrate the potential of the TVDI for soil moisture estimation, such as the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) images [10,16], Terra/Aqua Moderate-Resolution Imaging Spectroradiometer (MODIS) images [7,8,[17][18][19][20] and Thematic Mapper (TM) images [21]. The comparisons with soil moisture in situ measurements or model simulations show that the TVDI is feasible for monitoring soil moisture.…”

Section: Introductionmentioning

confidence: 99%

Temperature Vegetation Dryness Index Estimation of Soil Moisture under Different Tree Species

et al. 2015

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Abstract:The Laoshan forest is the largest forest in Nanjing, and it plays an important role in water resource management in Nanjing. The objectives of this study are to determine if the temperature vegetation dryness index (TVDI) is suitable to estimate the soil moisture and if soil moisture is significantly affected by tree species in the Laoshan forest. This paper calculated the spatial distribution of TVDI using LANDSAT-5 TM data. Sixty-two observation points of in situ soil moisture measurements were selected to validate the effectiveness of the TVDI as an index for assessing soil moisture in the Laoshan forest. With the aid of the three different temporal patterns, which are 10 January 2011, 18 May 2011 and 23 September 2011, this paper used the TVDI to investigate the differences of soil moisture under four kinds of mono-species forests and two kinds of mixed forests. The results showed that there is a strong and significant negative correlation between the TVDI and the in situ measured soil moisture (R ). This means that the TVDI can reflect the soil moisture status under different tree species in the Laoshan forest. The soil moisture under these six types of land cover from low to high is listed in the following order: Eucommia ulmoides, Quercus acutissima, broadleaf mixed forest, Cunninghamia lanceolata, coniferous and broadleaf mixed forest and Pinus massoniana. OPEN ACCESSSustainability 2015, 7 11402

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“…These variables and their related indicators are defined in this work as microclimate indicators (MCIs). MCIs, which are related to vegetation, temperature, and humidity levels, are considered critical indicators [18][19][20][21][22][23][24] and are used for the prediction and management of agricultural practices. They are the main input variables of models used to estimate other MCIs [2,7,25], models of growth and yield forecasting [25][26][27][28], models of disease and pest predictions [10,23,29], and models of climate prediction and adaptation to climate change [30,31].…”

Section: Introductionmentioning

confidence: 99%

“…Variations of surface moisture in the short and long term and its impact on vegetation can be monitored using stress indices based on ST and IVs derived from satellite images [56]. The TVDI is one of the most used indices to estimate surface moisture [21,24,62,63]. Chen et al [64] used the TVDI estimated using MODIS images to characterize the spatial variability of surface moisture and to link it with rice farming systems in the Mekong Delta, Vietnam.…”

Section: Introductionmentioning

confidence: 99%

Estimation and Uncertainty Assessment of Surface Microclimate Indicators at Local Scale Using Airborne Infrared Thermography and Multispectral Imagery

Kotchi¹,

Barrette²,

Viau³

et al. 2016

Geospatial Technology - Environmental and Social Applications

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A precise estimation and the characterization of the spatial variability of microclimate conditions (MCCs) are essential for risk assessment and site-specific management of vector-borne diseases and crop pests. The objective of this study was to estimate at local scale, and assess the uncertainties of Surface Microclimate Indicators (SMIs) derived from airborne infrared thermography and multispectral imaging. SMIs including Surface Temperature (ST) were estimated in southern Quebec, Canada. The formulation of their uncertainties was based on in-situ observations and the law of propagation of uncertainty. SMIs showed strong local variability and intra-plot variability of MCCs in the study area. The ST values ranged from 290 K to 331 K. They varied more than 17 K on vegetable crop fields. The correlation between ST and in-situ observations was very high (r = 0.99, p = 0.010). The uncertainty and the bias of ST compared to in-situ observations were 0.73 K and ±1.42 K respectively. This study demonstrated that very high spatial resolution multispectral imaging and infrared thermography present a good potential for the characterization of the MCCs that govern the abundance and the behavior of disease vectors and crop pests in a given area.

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Soil Moisture Monitoring Based on Land Surface Temperature-Vegetation Index Space Derived from MODIS Data

Cited by 56 publications

References 28 publications

Tree Water Status in Apple Orchards Measured by Means of Land Surface Temperature and Vegetation Index (LST–NDVI) Trapezoidal Space Derived from Landsat 8 Satellite Images

Tree Water Status in Apple Orchards Measured by Means of Land Surface Temperature and Vegetation Index (LST–NDVI) Trapezoidal Space Derived from Landsat 8 Satellite Images

Temperature Vegetation Dryness Index Estimation of Soil Moisture under Different Tree Species

Estimation and Uncertainty Assessment of Surface Microclimate Indicators at Local Scale Using Airborne Infrared Thermography and Multispectral Imagery

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