Vegetation Temperature Condition Index Research Articles

Spatial Downscaling of Satellite Soil Moisture Data Using a Vegetation Temperature Condition Index

Microwave remote sensing has been largely applied to retrieve soil moisture (SM) from active and passive sensors. The obvious advantage of microwave sensors is that SM can be obtained regardless of atmospheric conditions. However, existing global SM products only provide observations at coarse spatial resolutions, which often hamper their application in regional hydrological studies. On the other hand, the vegetation temperature condition index (VTCI) has been widely used to monitor the SM status. It is based on high-spatial-resolution visible and infrared satellite observations. The aim of this study is to develop a simple and efficient downscaling approach for estimating accurate SM at higher spatial resolution. The VTCI calculated from the Moderate Resolution Imaging Spectroradiometer is used to downscale the coarse-resolution SM product that has been developed under the framework of the European Space Agency's Climate Change Initiative (CCI) projects. The original and downscaled SM estimates are further validated against the in situ SM observations collected in the Yunnan province (southwest China). It is found that the accuracy level of CCI SM is similar to the results from previously published validation studies. The downscaled SM can maintain the accuracy of CCI SM and, at the same time, present more spatial details, demonstrating the feasibility of the proposed method. Overall, the notable advantages of the proposed method are simplicity, limited data requirements and purely relying on satellite measurements, and comparable accuracy level to other complex downscaling schemes. It will facilitate local hydrological applications, particularly in data-scarce regions, where the above-listed characteristics are important and useful.

Evaluation of soil moisture downscaling using a simple thermal-based proxy – the REMEDHUS network (Spain) example

Abstract. Soil moisture retrieved from satellite microwave remote sensing normally has spatial resolution on the order of tens of kilometers, which are too coarse for many regional hydrological applications such as agriculture monitoring and drought prediction. Therefore, various downscaling methods have been proposed to enhance the spatial resolution of satellite soil moisture products. The aim of this study is to investigate the validity and robustness of the simple vegetation temperature condition index (VTCI) downscaling scheme over a dense soil moisture observational network (REMEDHUS) in Spain. First, the optimized VTCI was determined through sensitivity analyses of VTCI to surface temperature, vegetation index, cloud, topography, and land cover heterogeneity, using data from Moderate Resolution Imaging Spectroradiometer~(MODIS) and MSG SEVIRI (METEOSAT Second Generation – Spinning Enhanced Visible and Infrared Imager). Then the downscaling scheme was applied to improve the spatial resolution of the European Space Agency's Water Cycle Multi-mission Observation Strategy and Climate Change Initiative (ESA CCI) soil moisture, which is a merged product based on both active and passive microwave observations. The results from direct validation against soil moisture observations, spatial pattern comparison, as well as seasonal and land use analyses show that the downscaling method can significantly improve the spatial details of CCI soil moisture while maintaining the accuracy of CCI soil moisture. The accuracy level is comparable to other downscaling methods that were also validated against the REMEDHUS network. Furthermore, slightly better performance of MSG SEVIRI over MODIS was observed, which suggests the high potential of applying a geostationary satellite for downscaling soil moisture in the future. Overall, considering the simplicity, limited data requirements and comparable accuracy level to other complex methods, the VTCI downscaling method can facilitate relevant hydrological applications that require high spatial and temporal resolution soil moisture.

A spatiotemporal analysis of droughts and the influence of North Atlantic Oscillation in the Iberian Peninsula based on MODIS imagery

Drought is among the least understood natural hazards and requires particular notice in the context of climate change. While the Mediterranean climate is by itself prone to droughts, a rise of temperatures and alteration of rainfall patterns already render the southern parts of continental Portugal and Spain highly susceptible to desertification. Precipitation in the Iberian Peninsula is mainly controlled by the large-scale mode of North Atlantic Oscillation (NAO) and is distributed with elevated variability over the cold months. Most drought studies of this region rely on meteorological data or apply information on vegetation dynamics, such as the Normalised Differenced Vegetation Index (NDVI), to indirectly investigate droughts. This paper evaluates the influence of the NAO winter index on the spatiotemporal occurrence of droughts in the Iberian Peninsula during the spring and summer seasons (March to August) for the years 2001–2005, 2007 and 2010. We applied the Vegetation Temperature Condition Index (VTCI) to identify local droughts. VTCI is a remote sensing drought index developed for reflecting soil moisture conditions in agricultural areas and combines information on land surface temperature (LST) and NDVI. As such, VTCI overcomes the shortcomings of NDVI in terms of drought monitoring. We derived biweekly information on LST and NDVI from MODIS/Terra and produced VTCI–NAO correlation maps at a confidence level of at least 90 % based on the VTCI time series. The results reflect a typical Mediterranean pattern in most parts of Iberia that is highly influenced by relief. Spring seasons are marked by great variability of precipitation, while summers persistently become dry, particularly in the south. NAO exerts its greatest influence in April and June, clearly delineating high correlation areas in the northwest and southeast with reverse patterns between the spring and early summer months. Due to the impact on water availability, the spring months are important for plant growth. At the same time, agricultural lands were found with types of land cover less resilient to droughts. The knowledge acquired in studies like the one reported here is therefore likely to be used in drought warning models for agriculture in spring.

Ground sampling methods for surface soil moisture in heterogeneous pixels

Reasonable ground sampling methods for the development of soil moisture remote sensing retrieval algorithm models and product validation are very important. Soil moisture has obvious variability at different spatial and temporal scales, especially for surface soil moisture. It makes the surface soil moisture sampling at medium or coarse resolution even more challenging. The study reviewed the current common sampling methods and further described the calculation process and limitation in application. Field experiments were carried out in the sampling domain of 165 × 165 m2 to compare different sampling methods. The results indicated that the corresponding values of necessary sampling size under a standard error of 0.0076 cm3 cm−3 (equivalent to relative error = 5 % and confidence level = 95 %) were about 55, 52, 37, 35 and 26 for the conventional statistical sampling, bootstrap sampling, random combination sampling, geostatistical sampling and stratified sampling, respectively. However, they all required data collection of soil moisture observation in advance to generating an optimal sampling strategy. Even the temporal stability sampling method also needed more numbers of long-term observational data to obtain less representative stable points. When the area of sampling pixels increases in practice, especially when the sub-pixel variation is larger, it is particularly difficult to conduct pre-sampling or real-time sampling at a medium or coarse spatial resolution because of the limited manpower. Therefore, the soil moisture stratified sampling based on vegetation temperature condition index prior knowledge was put forward and applied in the regional scale. With the rapid development of global change research, many kinds of data can become the prior knowledge, including land use data, soil texture data, DEM meteorological data, etc.

A simple Landsat–MODIS fusion approach for monitoring seasonal evapotranspiration at 30 m spatial resolution

Persistent cloud-cover in the humid southeastern USA and the low temporal resolution of Landsat sensors limit the derivation of seasonal evapotranspiration (ET) maps at moderate spatial resolution. This article introduces a Landsat Moderate Resolution Imaging Spectroradiometer (Landsat–MODIS) ET fusion model that uses simple linear regression to integrate Landsat-derived reference ET fraction (ETrF) from mapping ET at high resolution with internalized calibration (METRIC) model and the vegetation temperature condition index (VTCI) derived from MODIS images. For a study site in Florida, model-estimated ET and ET estimated using energy budget eddy covariance at a US Geological Survey (USGS) station in Ferris Farm, Florida, were found to be in a good agreement with a root mean squared error of 0.44 mm day–1, coefficient of determination (R2) of 0.80, Nash–Sutcliffe efficiency of 0.79 for daily ET (ETd), and 2% relative error for cumulative seasonal ET during the growing season of 2001. At another study site in Alabama, the model underestimated 2008 annual water balance ET for the Fish River Watershed by 39 mm or 4%. Comparisons of model-estimated ET with that obtained using a non-fusion Landsat-only approach at both sites indicated that the fusion of Landsat and MODIS ET values reduces potential errors in ET estimation that would otherwise arise due to insufficient availability of cloud-free Landsat images for METRIC processing. Validation results and application of the model in deriving seasonal/annual ET for different land-cover classes in the Fish River Watershed suggested that the fusion model has the potential to be used in continuously monitoring ET for field- to watershed-level agricultural and hydrological applications in the southeastern USA.

미얀마 건조지 토지피복 변화와 식생온도조건지수간의 관계분석

본 연구에서는 미얀마 내 Mandalay, Magway 지역에 대해 Landsat 4-5 TM 위성영상을 이용하여 토지피복 변화와 식생온도조건지수(Vegetation Temperature Condition Index; VTCI)의 관계를 분석함으로써, 미얀마 토지피복 변화와 건조 정도의 관계를 밝히고자 하였다. 토지피복 변화를 분석한 결과, 연구 대상 지역에서 산림은 감소하는 것으로 나타난 반면, 주거지와 농경지의 면적은 증가하였다. VTCI 분석을 수행한 결과, (a)지역에서는 매우 건조한 지역의 면적이 점차 감소하고, 건조한 지역 및 보통, 습윤한 지역의 면적이 증가하여 건조의 정도가 다소 감소되는 것으로 나타났다. (b)지역 또한 매우 건조한 지역의 면적이 증가하여 건조 정도가 심각해지는 것을 파악할 수 있었다. VTCI와 토지피복 변화의 관계 분석결과는 두 지역의 매우 건조한 지역에서 산림 및 주거지의 농경지로의 변화 비율이 비교적 높게 나타나고, 변화된 지역의 VTCI 평균이 감소하여 토지피복의 변화와 건조 정도의 변화의 관련성을 찾을 수 있었으며, 건조지역이 점차 증가하고 있음을 확인하였다. The purpose of this study is to investigate the cause of increasing dry zones through analyzing relationships between land cover and Vegetation Temperature Condition Index(VTCI) using Landsat 4-5 TM satellite images in Central Dry Zones of Myanmar. As a result of land cover classifications, while vegetation areas gradually decrease, residential area and cropland were increased. VTCI analysis shows that region (a) showed a gradual decrease in the area of severely arid, and increase in the area of moderate dry and wet, which sums up to a slight decrease in aridity. Region (b) also showed to increase in dry areas and severe aridity. The result of relational analysis between VTCI and land cover change showed high ratio of land cover change, from severe arid area to forest and residential farmland. The average VTCI decreased in the changed land covers, which indicates the relationship between aridity and land cover change and a gradual increase in the arid area was identified.

Feasibility of Vegetation Temperature Condition Index for monitoring desertification in Bulgan, Mongolia

Desertification monitoring as a main portion for understand desertification, have been conducted by many scientists. However, the stage of research remains still in the level of comparison of the past and current situation. In other words, monitoring need to focus on finding methods of how to take precautions against desertification. In this study, Vegetation Temperature Condition Index (VTCI), derived from Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST), was utilized to observe the distribution change of vegetation. The index can be used to monitor drought occurrences at a regional level for a special period of a year, and it can also be used to study the spatial distribution of drought within the region. Techniques of remote sensing and Geographic Information System (GIS) were combined to detect the distribution change of vegetation with VTCI. As a result, assuming that the moisture condition is the only main factor that affects desertification, we found that the distribution of vegetation in Bulgan, Mongolia could be predicted in a certain degree, using VTCI. Although desertification is a complicated process and many factors could affect the result. This study is helpful to provide a strategic guidance for combating desertification and allocating the use of the labor force.

Estimation of regional evapotranspiration in alpine area and its response to land use change: A case study in Three-River Headwaters region of Qinghai-Tibet Plateau, China

Three-River Headwaters (TRH) region involved in this paper refers to the source region of the Changjiang (Yangtze) River, the Huanghe (Yellow) River and the Lancang River in China. Taking the TRH region of the Qinghai-Tibet Plateau as a case, the annual evapotranspiration (ET) model developed by Zhang et al. (2001) was applied to evaluate mean annual ET in the alpine area, and the response of annual ET to land use change was analyzed. The plant-available water coefficient (w) of Zhang’s model was revised by using vegetation-temperature condition index (VTCI) before annual ET was calculated in alpine area. The future land use scenario, an input of ET model, was spatially simulated by using the conversion of land use and its effects at small regional extent (CLUE-S) to study the response of ET to land use change. Results show that the relative errors between the simulated ET and that calculated by using water balance equation were 3.81% and the index of agreement was 0.69. This indicates that Zhang’s ET model based on revised plant-available water coefficient is a scientific and practical tool to estimate the annual ET in the alpine area. The annual ET in 2000 in the study area was 221.2 mm, 11.6 mm more than that in 1980. Average annual ET decreased from southeast to northwest, but the change of annual ET between 1980 and 2000 increased from southeast to northwest. As a vast and sparsely populated area, the population in the TRH region was extremely unbalanced and land use change was concentrated in very small regions. Thus, land use change had little effect on total annual ET in the study area but a great impact on its spatial distribution, and the effect of land use change on ET decreased with increasing precipitation. ET was most sensitive to the interconversion between forest and unused land, and was least sensitive to the interconversion between cropland and low-covered grassland.

Analysis of agricultural drought using vegetation temperature condition index (VTCI) from Terra/MODIS satellite data

The most commonly used normalized difference vegetation index (NDVI) from remote sensing often fall short in real-time drought monitoring due to a lagged vegetation response to drought. Therefore, research recently emphasized on the use of combination of surface temperature and NDVI which provides vegetation and moisture conditions simultaneously. Since drought stress effects on agriculture are closely linked to actual evapotranspiration, we used a vegetation temperature condition index (VTCI) which is more closely related to crop water status and holds a key place in real-time drought monitoring and assessment. In this study, NDVI and land surface temperature (T (s)) from MODIS 8-day composite data during cloud-free period (September-October) were adopted to construct an NDVI-T (s) space, from which the VTCI was computed. The crop moisture index (based on estimates of potential evapotranspiration and soil moisture depletion) was calculated to represent soil moisture stress on weekly basis for 20 weather monitoring stations. Correlation and regression analysis were attempted to relate VTCI with crop moisture status and crop performance. VTCI was found to accurately access the degree and spatial extent of drought stress in all years (2000, 2002, and 2004). The temporal variation of VTCI also provides drought pattern changes over space and time. Results showed significant and positive relations between CMI (crop moisture index) and VTCI observed particularly during prominent drought periods which proved VTCI as an ideal index to monitor terminal drought at regional scale. VTCI had significant positive relationship with yield but weakly related to crop anomalies. Duration of terminal drought stress derived from VTCI has a significant negative relationship with yields of major grain and oilseeds crops, particularly, groundnut.

Vegetation, water and thermal stress index for study of drought in Nepal and central northeastern India

Drought is the degradation of land in arid, semi-arid and dry sub-humid regions caused primarily by human activity and climatic variations. The present study is the first attempt to identify and monitor drought using a vegetation index, a vegetation-water index and land surface temperature (LST) data for Nepal and central northeastern India. We propose a Vegetation Water Temperature Condition Index (VWTCI) for monitoring drought on a regional scale. The VWTCI includes the Normalized Difference Water Index (NDWI), which measures the water status in vegetation, the Normalized Difference Vegetation Index (NDVI) and LST data. To validate the approach, the VWTCI was compared with the Vegetation Temperature Condition Index (VTCI) and Tropical Rainfall Measuring Mission (TRMM) 3B31 Precipitation Radar (PR) data. The study revealed a gradual increase in the extent of drought in the central part of the study area from 2000 to 2004. Certain constant drought areas were also identified and the results indicate that these areas are spreading slowly towards the northeast into the central part of the study area. Comparison of the drought areas also shows a decrease in rainfall in June and July from 2000 to 2004.

Using the vegetation temperature condition index for time series drought occurrence monitoring in the Guanzhong Plain, PR China

The aim of this study was to develop and validate a method of determining the warm and cold edges of the Vegetation Temperature Condition Index (VTCI) drought monitoring approach, and to analyse the time series profiles of the VTCI in croplands under both rainfed and irrigated conditions. The linear correlation coefficients between the VTCI and the cumulative precipitation at one or two periods of 10‐day intervals are the highest. There are significant linear correlations between the VTCI and soil moisture at the 0–10‐cm layer for each 10‐day interval during the winter wheat growing seasons. These results indicate that the VTCI is an effective approach for monitoring drought occurrence after the crops turn green. The time series analysis of the VTCI in the 10‐day intervals shows that the VTCI profiles are different for irrigated and rainfed conditions. The time series VTCI values under rainfed conditions have a good response to recent precipitation, while those under irrigated conditions have less agreement with recent precipitation due to irrigation practices. The results show that the VTCI is a better indicator of droughts than indices developed from precipitation data.

Designing of the perpendicular drought index

In this paper, a simple, effective drought monitoring method is developed using two dimensional spectral space obtained from reflectance of near-infrared (NIR) and Red wavelengths. First, NIR–Red reflectance space is established using atmospheric and geometric corrected ETM+ data, which is manifested by a triangle shape and in which different surface targets possess certain spatial distribution rules. Second, perpendicular drought index (PDI) is developed on the basis of spatial characteristics of moisture distribution in NIR–Red space, as well as the relationships between PDI and soil moisture is examined. Validation work includes: comparison of PDI with in-situ drought index obtained from field measured data in the study area which includes bulk soil moisture content at different soil depths, field moisture capacity and wilting coefficient, etc.; and comparison of PDI with other recognized drought monitoring methods such as LST/NDVI and vegetation temperature condition index (VTCI). It is evident from the results that graph of PDI of field measured plots demonstrates very similar trends with ground truth drought data, LST/NDVI and VTCI. PDI is highly correlated with in-situ drought values calculated from 0 to 20 cm mean soil moisture with correlation coefficients of R2 = 0.49 (r = 0.75). This paper concludes that PDI has a potential in remote estimation of drought phenomenon as a simple, effective drought monitoring index.

Using MODIS Land Surface Temperature and Normalized Difference Vegetation Index products for monitoring drought in the southern Great Plains, USA

A near-real time drought monitoring approach is developed using Terra–Moderate Resolution Imaging Spectoradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) products. The approach is called Vegetation Temperature Condition Index (VTCI), which integrates land surface reflectance and thermal properties. VTCI is defined as the ratio of LST differences among pixels with a specific NDVI value in a sufficiently large study area; the numerator is the difference between maximum LST of the pixels and LST of one pixel; and the denominator is the difference between maximum and minimum LSTs of the pixels. VTCI is lower for drought and higher for wet conditions. The ground-measured precipitation data from a study area covering parts of the states of Texas and Oklahoma in the southern Great Plains, USA, are used to validate the drought monitoring approach. Linear correlation analysis between VTCI, and total monthly precipitation and departure from normal monthly precipitation shows that VTCI is not only closely related to recent rainfall events but also related to past rainfall amounts, and indicates that VTCI might be a better and a near-real time drought monitoring approach.

Discussion

Although remote sensing technology has been used to evaluate regional ecosystem health for a long time, it is still necessary to find a suitable index system to better evaluate ecosystem health. This study aims to improve the ecosystem health measurement ability of remote sensing technology. This research was carried out in Fuzhou under the traditional Vigor-Organization-Resilience (VOR) framework by optimizing and improving the construction method of sub-indexes. Sub-indexes were constructed using spectral index analysis, landscape theoretical ecology model and spatial measurement. Three remote sensing datasets were used (1996, 2008 and 2021) to carry out remote sensing diagnosis of regional ecosystem health in the Fuzhou administrative region. The main research findings and conclusions were as follows. A new comprehensive vigor index (CVI) was developed by the principal component analysis (PCA) based on the four indicators: fractional vegetation cover (FVC), global vegetation moisture index (GVMI), vegetation temperature condition index (VTCI), normalized differential build-up and bare soil index (NDBSI). A new organizational index was constructed based on the landscape index. Four types of indexes, namely landscape heterogeneity (LH), landscape connectivity (LC), the shape characteristics of forest patches (CS) and the connectivity of forest patches (CC) were used as the main factors for calculating the organizational index. A resilience index calculation framework was proposed based on the habitat quality model. The temporal and spatial characteristics of ecosystem health were evaluated and analyzed. The regional ecosystem health value of the whole region reduced gradually, with average values of 0.3521 (1996), 0.3445 (2008) and 0.3345 (2021) respectively. The average reduction rate was 0.0007 per year (1996–2021). The proposed remote sensing diagnosis method provides a complete framework for solving the problems of measuring the dynamic evolution process and characteristics of regional ecosystem health.