Development of real-time reference evapotranspiration at the regional scale using satellite-based observations

Real-time data of reference evapotranspiration (ET₀) at different space-time scales are essential to regional agricultural drought assessment, water accounting at the watershed to basin scale, and provide irrigation advisory to farmers. Here, we present a data-fusion approach that integrates satellite-based insolation product (8 km) from an Indian geostationary satellite (Kalpana-1) sensor (VHRR; Very High Resolution Radiometer) and high-resolution (~ 5 km) short-range weather forecast into an FAO56 model based on the classical Penman–Monteith (P-M) formulation. Five year (2009–2013) mean monthly estimates from the daily ET₀ product over the Indian landmass were found to vary between 10 and 350 mm. It increased from January to May (70–350 mm), followed by a decrease to reach the lowest in November (10–140 mm), thus typically showing unimodal distribution. The comparison of daily space-based and station-based estimates (at six ground stations) produced a root mean square deviation (RMSD) ranging from 21% to 38% for 977 paired data sets with the correlation coefficient (r) varying from 0.32 to 0.82. The error was reduced from 25% to 10% with an increase in ‘r’ from 0.43 to 0.98 for daily to 10 day summation period. Spatial grid-to-grid comparison of monthly ET₀ estimates with Global Data Assimilation System (GDAS) potential evapotranspiration (PET) showed RMSD within a range of 1.4–18.4% for most of the months, except for two. Further ET₀ analysis over normal and drought years showed that it could be used for comprehensive drought assessment with other existing indicators.     

Tracking seasonal drought effects on ecosystem light use efficiency with satellite-based PRI in a Mediterranean forest

Gross primary productivity (GPP) changes occur at different time-scales and due to various mechanisms such as variations in leaf area, chlorophyll content, rubisco activity, and stomatal conductance. Diagnostic estimates of primary productivity are obviously error prone when these changes are not accounted for. Additional complications arise when factors inuencing a biome-specific maximum light use efficiency (LUE) must be estimated over a large area. In these cases a direct estimation of ecosystem LUE could reduce uncertainty of GPP estimates. Here, we analyse whether a MODIS-based photochemical reectance index (PRI) is a useful proxy for the light use efficiency of a Mediterranean Quercus ilex forest. As the originally proposed reference band for PRI is not available on MODIS, we tested the reference bands 1 (620-670 nm), 4 (545-565 nm), 12 (546-556 nm), 13 (662-672 nm), and 14 (673-683 nm) using different atmospheric correction algorithms. We repeated the analysis with different temporal resolutions of LUE (half-hourly to daily). The strongest correlation between LUE and PRI was found when considering only a narrow range of viewing angles at a time (especially 0-10° and 30-40°). We found that the MODIS-based PRI was able to track ecosystem LUE even during severe summer time water limitation. For this Mediterranean-type ecosystem we could show that a GPP estimation based on PRI is a huge improvement compared to the MODIS GPP algorithm. In this study, MODIS spectral band 1 turned out to be the most suitable reference band for PRI, followed by the narrow red bands 13 and 14. As to date no universally applicable reference band was identified in MODIS-based PRI studies, we advocate thorough testing for the optimal band combination in future studies.     

An investigation of drought prediction using various remote-sensing vegetation indices for different time spans

Iran is a country in a dry part of the world and extensively suffers from drought. Drought is a natural and repeatable phenomenon definable at specified time and area. In addition, social and economic issues can be affected by drought. Information such as intensity, duration, and spatial coverage of drought can help decision makers to reduce the vulnerability of the drought-affected areas, therefore lessen the risks associated with drought episodes. Lack of long-term meteorological data for many parts of the country is one of the most important problems for drought monitoring in Iran. One of the useful ways for gathering information about soil and vegetation conditions is using satellite-based imagery. In this study, remotely sensed image data were applied in order to forecast and model the drought. To this end, SPI (standardized precipitation index) drought indicator was used to represent the drought and its intensity in different time spans (1, 3, 6, 9, 12, and 24 months). Some vegetation indices (VIs) including normalized difference vegetation index, temperature condition index, vegetation condition index, and normalized difference vegetation index deviation were extracted using Advanced Very High Resolution Radiometer sensor imagery. These indices were plugged into the model to calculate the SPI. A unique Support Vector Machine classifier improved for all types of the SPI by applying various remotely sensed VIs. The best vegetation index for each kind of SPI was determined. In this framework, meteorological stations were clustered based on their land cover extracted from satellite-based indices before insertion to the model.     

Comparison of remotely sensed and meteorological data-derived drought indices in mid-eastern China

Numerous drought indices have been developed and applied to monitor the severity of drought. It has been demonstrated that the evaluation of the indices is very important for further utilization of remotely sensed and meteorological information. The objective of this article is to investigate and compare the different methods derived from satellite/meteorological data for drought monitoring during the typical dry year (2006) in mid-eastern China. The compared six drought indices include the vegetation condition index (VCI), percent of average seasonal greenness (PASG), temperature condition index (TCI), vegetation supply water index (VSWI), percentage of precipitation anomalies (PPA) and standardized precipitation index (SPI). These indices are calculated based on different data sources including reflective data, thermal data, the combination of reflective and thermal data and meteorological data. The correlation matrix and regression relationships among the integrals under all drought indices, the integral under the relative air humidity (RAH) curve and cumulative rainfall at the location of 11 agro-meteorological stations for 2006 were calculated. Spatial comparison analysis among the drought indices reveals that all the indices have certain coincidence in the detected regional-scale distribution of drought especially those derived from the same data set, while obviously local-scale distribution differences were found among the different groups of indices. Compared to curves of the reflective and thermal indices, the overall trend of VSWI series has better consistence with the PPA curve. Based on correlation and regression analysis, it is demonstrated that VSWI can better reflect both the amount of precipitation and the severity of drought due to lack of rainfall. Furthermore, land surface temperature (LST) contributes more to the result of hybrid index (VSWI) than reflective information. There is logarithmic relationship between integral of VSWI and cumulative precipitation, while obvious linear correlations were found between integral under VSWI curve and integral under the RAH/TCI/PASG curves. According to the filed observation of droughts from agro-meteorological stations in the study area, it can be concluded that any single index is not sufficient to precisely depicting drought characteristics. The combined use of different indices at the same time or indices which integrate various sources of information may obtain more consistent results with the actual situation.     

A comparative study of NOAA-AVHRR derived drought indices using change vector analysis

The objective of this study was to compare the spatial occurrences of droughts, detected by remotely sensed drought-indices over the desert-steppe and desert geo-botanical zones of Mongolia. All indices were derived from reflectance and thermal data sets, obtained from the NOAA-AVHRR data between 1982 and 1999. One group of the drought-indices is based on vegetation state derived from the reflective channels. This group includes the Normalized Difference Vegetation Index (NDVI), Anomaly of Normalized Difference Vegetation Index (NDVIA), Standardized Vegetation Index (SVI), and Vegetation Condition Index (VCI). Another group, based on surface brightness temperature derived from the thermal channel of NOAA-AVHRR, includes the Temperature Condition Index (TCI). The third group is based on combination between the reflective and thermal channels includes the ratio between Land Surface Temperature (LST) and NDVI (LST/NDVI), the Vegetation Health Index (VH), and the Drought Severity Index (DSI). Change detection procedure was performed by using the Change Vector Analysis in the temporal domain. Comparison analysis among the drought-indices reveals that there is no spatial coincidence between them, even when the vegetation growing period was divided into 2-month sub-periods — beginning, middle, and end. Based on the statistical analysis, higher correlations were found among the reflective indices while lesser or no relationships were found between the thermal and combination of the thermal and reflective indices. Furthermore, no agreement was found between the spatial extent of the satellite-derived drought-indices and the meteorological-based Palmer Drought Severity Index (PDSI) and also between the traditional ground-observed drought-affected-areas (DAA) maps. It was found that the combination of satellite-derived drought-indices can identify wider drought-occurred areas rather than the PDSI and the DAA maps. In summary, this study concludes that it is difficult to point out the most reliable drought index, and that the ground observations cannot provide sufficient information for validation of satellite derived drought indices.     

Performance evaluation of spectral vegetation indices using a statistical sensitivity function

A great number of spectral vegetation indices (VIs) have been developed to estimate biophysical parameters of vegetation. Traditional techniques for evaluating the performance of VIs are regression-based statistics, such as the coefficient of determination and root mean square error. These statistics, however, are not capable of quantifying the detailed relationship between VIs and biophysical parameters because the sensitivity of a VI is usually a function of the biophysical parameter instead of a constant. To better quantify this relationship, we developed a “sensitivity function” for measuring the sensitivity of a VI to biophysical parameters. The sensitivity function is defined as the first derivative of the regression function, divided by the standard error of the dependent variable prediction. The function elucidates the change in sensitivity over the range of the biophysical parameter. The Student's t- or z-statistic can be used to test the significance of VI sensitivity. Additionally, we developed a “relative sensitivity function” that compares the sensitivities of two VIs when the biophysical parameters are unavailable.     

Verification of the analysis method for extracting the spatial continuity of the vegetation distribution on a regional scale

Vegetation plays a key role in not only improving urban environments, but also conserving ecosystems. The spatial continuity of vegetation distributions can be expected to make green corridors for landscape management, wind paths against heat island phenomena. In this paper, we develop a spatial analysis method of vegetation distributions using remotely sensed data on a regional scale. The method consists of a spatial autocorrelation analysis, an overlay analysis, and a hydrological analysis with the Normalized Difference Vegetation Index (NDVI) adopted as the proxy of vegetation abundance. Application of the method leads to the extraction of the lines between the core areas and sparse areas of vegetation. The purpose of this study is to verify our method through applying a vegetation map digitized from aerial photographs. The map contained three vegetation types of land cover: grasslands, agricultural fields, and tree-covered areas. We use remotely sensed data collected at four different time periods at the regional scale, along with information on the seasonal fluctuations of the vegetation. As a result, the exclusion of seasonal land-cover changes, as in the reaping of agricultural fields, in the process of applying the proposed method produces an effect. The analysis reveals steady areas unaffected by the seasonal fluctuation of vegetation along the lines extracted by applying the proposed method.     

An integrated PC environment for assessment of drought

Abstract

The design of a versatile software environment to support routine activities of a satellite based operational drought monitoring system is presented in this article. In addition, software provides the assistance to analyse and interpret drought conditions based on satellite derived normalised difference vegetation index (NDVI) statistics and ground information pertaining to a given area. The environment currently under development deals with district-wise drought assessment for nine states, taluk-wise for Karnataka State and operates on a standard PC with EGA graphics.     

Recent advancement on estimation of blue carbon biomass using satellite-based approach

History revealed that people had been discharging a large proportion of carbon into the atmosphere through fossil fuel consumption and the marine environment. These have prompted atmospheric carbon fixations that have proved to be larger than at any other point throughout the human survival. Due to the critical role of blue carbon in the ocean carbon cycle, it is essential to pay extra attention to these habitats (mangrove, seagrass meadows, salt marshes, and coral reefs). Hence, this article reviews the recent developments in blue carbon biomass estimation using a geospatial approach and highlighted the blue carbon components achievements and gaps. Biomass and soil carbon estimation, using change detection analysis, were reviewed. Analysis of the carbon conversion factors, used in converting biomass to carbon, was demonstrated. The review shall act as support for the realization of the target 14.2 and 14.5 of the 14^th sustainable development goal established by the United Nations, to fast track the achievement of the 2020 agenda.     

Performance evaluation of some clustering algorithms and validity indices

In this article, we evaluate the performance of three clustering algorithms, hard K-Means, single linkage, and a simulated annealing (SA) based technique, in conjunction with four cluster validity indices, namely Davies-Bouldin index, Dunn's index, Calinski-Harabasz index, and a recently developed index I. Based on a relation between the index I and the Dunn's index, a lower bound of the value of the former is theoretically estimated in order to get unique hard K-partition when the data set has distinct substructures. The effectiveness of the different validity indices and clustering methods in automatically evolving the appropriate number of clusters is demonstrated experimentally for both artificial and real-life data sets with the number of clusters varying from two to ten. Once the appropriate number of clusters is determined, the SA-based clustering technique is used for proper partitioning of the data into the said number of clusters.     

Uncertainty analysis of five satellite-based precipitation products and evaluation of three optimally merged multi-algorithm products over the Tibetan Plateau

This study is the first comprehensive examination of uncertainty with respect to region, season, rain rate, topography, and snow cover of five mainstream satellite-based precipitation products over the Tibetan Plateau (TP) for the period 2005–2007. It further investigates three merging approaches in order to provide the best possible products for climate and hydrology research studies. Spatial distribution of uncertainty varies from higher uncertainty in the eastern and southern TP and relatively smaller uncertainty in the western and northern TP. The uncertainty is highly seasonal, temporally varying with a decreasing trend from January to April and then remaining relatively low and increasing after October, with an obvious winter peak and summer valley. Overall, the uncertainty also shows an exponentially decreasing trend with higher rainfall rates. The effect of topography on the uncertainty tends to rapidly increase when elevation exceeds 4000 m, while the impact slowly decreases in areas lower than that topography. The influence of the elevation on the uncertainty is significant for all seasons except for the summer. Further cross-investigation found that the uncertainty trend is highly correlated with the MODIS-derived snow cover fraction (SCF) time series over the TP (e.g. correlation coefficient ≥0.75). Finally, to reduce the still relatively large and complex uncertainty over the TP, three data merging methods are examined to provide the best possible satellite precipitation data by optimally combining the five products. The three merging methods – arithmetic mean, inverse-error-square weight, and one-outlier-removed arithmetic mean – show insignificant yet subtle differences. The Bias and RMSE of the three merging methods is dependent on the seasons, but the one-outlier-removed method is more robust and its result outperforms the five individual products in all the seasons except for the winter. The correlation coefficient of the three merging methods is consistently higher than any of five individual satellite estimates, indicating the superiority of the method. This optimally merging multi-algorithm method is a cost-effective way to provide satellite precipitation data of better quality with less uncertainty over the TP in the present era prior to the Global Precipitaton Measurement Mission.     

Responses of the reflectance indices PRI and NDVI to experimental warming and drought in European shrublands along a north-south climatic gradient

The aim of this study was to evaluate the use of ground-based canopy reflectance measurements to detect changes in physiology and structure of vegetation in response to experimental warming and drought treatment at six European shrublands located along a North-South climatic gradient. We measured canopy reflectance, effective green leaf area index (green LAIe) and chlorophyll fluorescence of dominant species. The treatment effects on green LAIe varied among sites. We calculated three reflectance indices: photochemical reflectance index PRI [531 nm; 570 nm], normalized difference vegetation index NDVI₆₈₀ [780 nm; 680 nm] using red spectral region, and NDVI₅₇₀ [780 nm; 570 nm] using the same green spectral region as PRI. All three reflectance indices were significantly related to green LAIe and were able to detect changes in shrubland vegetation among treatments. In general warming treatment increased PRI and drought treatment reduced NDVI values. The significant treatment effect on photochemical efficiency of plants detected with PRI could not be detected by fluorescence measurements. However, we found canopy level measured PRI to be very sensitive to soil reflectance properties especially in vegetation areas with low green LAIe. As both soil reflectance and LAI varied between northern and southern sites it is problematic to draw universal conclusions of climate-derived changes in all vegetation types based merely on PRI measurements. We propose that canopy level PRI measurements can be more useful in areas of dense vegetation and dark soils.     

Effects of spatial variability in light use efficiency on satellite-based NPP monitoring

Light use efficiency (LUE) algorithms are a potentially effective approach to monitoring global net primary production (NPP) using satellite-borne sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS). However, these algorithms are applied at relatively coarse spatial resolutions (≥1 km), which may subsume significant heterogeneity in vegetation LUE (ε_n, g MJ⁻¹) and, hence, introduce error. To examine the effects of spatial heterogeneity on a LUE algorithm, imagery from the Advanced Very High Resolution Radiometer (AVHRR) at ≈1-km resolution was used to implement a LUE approach for NPP estimation over a 25-km² area of corn (Zea mays L.) and soybean (Glycine max Merr.) in central Illinois, USA. Results from several ε_n formulations were compared with a NPP reference surface based on measured NPPs and a high spatial resolution land cover surface derived from Landsat ETM+. Determination of ε_n based on measurements of biomass production and monitoring of absorbed photosynthetically active radiation (APAR) revealed that ε_n of soybean was 68% of that for corn. When a LUE algorithm for estimating NPP was implemented in the study area using the assumption of homogeneous cropland and the ε_n for corn, the estimate for total biomass production was 126% of that from the NPP reference surface. Because of counteracting errors, total biomass production using the soybean ε_n was closer (86%) to that from the NPP reference surface. Retention of high spatial resolution land cover to assign ε_n resulted in a total NPP very similar to the reference NPP because differences in leaf phenology between the crop types were small except early in the growing season. These results suggest several alternative approaches to accounting for land cover heterogeneity in ε_n when implementing LUE algorithms at coarse resolution.     

An evaluation of comfort of a bus seat

The aim of this research was to evaluate the comfort of a passenger seat for a new type of bus. A fuzzy set model of a multistage comfort scale (MCS) was adopted for the assessment of comfort, together with the techniques of human back shape and EMG measurements as well as posture analysis. The subjects were 30 university students. It is concluded that MCS is a rapid but comprehensive evaluation method for single chair evaluation. The overall rating of MCS is 0.532, which is acceptable under the conditions of the prototype evaluation. The seat profile fits better with the back curve of subjects who had higher comfort rating in the way that the upper profile of the seat coincides with the human back curve and the lower part of the profile intersects the human back curve in the lumbar region; here the human back curves were measured in the slumped sitting posture. There was a significant difference in the EMGs of back muscles between the two sitting postures (sitting upright and the slumped sitting posture) at all the seat heights.     

An evaluation of radar as a crop classifier

Results of an investigation to specify the parameters of a space-borne imaging radar for use in crop identification are discussed. The study relied upon scaltering data acquired with a groundbased radar which were degraded to simulate the performance of a system similar to the proposed Space Shuttle Orbiter Imaging Radar. Data acquired from fields sown in corn, milo, soybeans, wheat and alfalfa were employed. The results of this study suggest that for best classification accuracy, a K-band (approximately 14 GHz), dual polarized system viewing fields at an off nadir angle in the 40° to 60° range should be employed. However it is emphasized that to attain classification accuracies exceeding 90%, multi-date acquisition is required. As best as can be determined, four target revisits at an interval of ten days is adequate for 90% accuracy.