Modified Perpendicular Drought Index Research Articles

A novel ecological evaluation index based on geospatial principles and remote sensing techniques

ABSTRACT The evaluation of regional ecological status has far-reaching significance for understanding regional ecological conditions and promoting sustainable development. Herein, a geospatial ecological index (GEI) was developed on the basis of Landsat data and the principles of soil lines and spatial geometry. Specifically, the GEI integrates four remote sensing indicators: Perpendicular Vegetation Index (PVI) representing greenness, Modified Perpendicular Drought Index (MPDI) representing drought, Normalized Difference Built-up and Soil Index (NDSI) representing the dryness of land surface, and Land Surface Temperature (LST) representing the hotness of land surface. Two typical regions, Fuzhou City and Zijin mining area, in Fujian Province, China, were selected to evaluate regional ecological quality via the proposed GEI. The results show an improvement in the overall ecological quality of Fuzhou City, with an increase in the average GEI value from 0.49 in 2001 to 0.53 in 2020. In the case of the Zijin mining area, regions with poor ecological status are concentrated in the main mining areas. However, the average GEI value rose from 0.51 in 1992 to 0.57 in 2020, illustrating an improvement in its ecological conditions. The study demonstrates the robustness and effectiveness of GEI, objectively revealing the spatial distribution and ecological status.

Ratio Drought Index (RDI): A soil moisture index based on new NIR-red triangle space

ABSTRACT Accurate monitoring of soil moisture and the development of timely interventions are important to reduce the social and economic losses caused by drought. Compared to short-wave infrared (SWIR) and thermal infrared (TIR), near-infrared (NIR) and visible bands are widely used in almost all optical satellites. Drought monitoring using NIR and visible bands is therefore more relevant for optical satellites. Among the visible bands, the red band is often used in combination with the NIR band for drought monitoring due to its sensitivity to vegetation. However, current drought indexes based on the NIR and the red band applied to areas of high vegetation suffer from insufficient accuracy or tedious calculations. In this study, the ratio drought index (RDI) was developed after constructing a new feature space by examining the spectral properties of soil and vegetation at different water levels in the NIR and red bands. The accuracy of soil moisture inversion under two types of bare soil and vegetation was evaluated using in situ data from Tai’an City, Shandong Province. The perpendicular drought index (PDI) and modified perpendicular drought index (MPDI) were also used to compare for the RDI. The results showed that the RDI correlation coefficients (R2) of 0.653 and 0.641 were better than the MPDI of 0.616 and 0.594 and the PDI of 0.602 and 0.546 for soil moisture measurements from vegetation and bare soil cover. The RDI attenuates the effect of vegetation on soil moisture inversion, as its root mean square error (RMSE) in vegetated areas is lower than that of the PDI and MPDI. The RDI calculation can be used as a theoretical guide for large-scale soil moisture estimation because it is fast, accurate and does not require additional quantitative remote sensing inversion factors.

Generating Daily Soil Moisture at 16 m Spatial Resolution Using a Spatiotemporal Fusion Model and Modified Perpendicular Drought Index.

Soil moisture (SM) is an important parameter in land surface processes and the global water cycle. Remote sensing technologies are widely used to produce global-scale SM products (e.g., European Space Agency’s Climate Change Initiative (ESA CCI)). However, the current spatial resolutions of such products are low (e.g., >3 km). In recent years, using auxiliary data to downscale the spatial resolutions of SM products has been a hot research topic in the remote sensing research area. A new method, which spatially downscalesan SM product to generate a daily SM dataset at a 16 m spatial resolution based on a spatiotemporal fusion model (STFM) and modified perpendicular drought index (MPDI), was proposed in this paper. (1) First, a daily surface reflectance dataset with a 16 m spatial resolution was produced based on an STFM. (2) Then, a spatial scale conversion factor (SSCF) dataset was obtained by an MPDI dataset, which was calculated based on the dataset fused in the first step. (3) Third, a downscaled daily SM product with a 16 m spatial resolution was generated by combining the SSCF dataset and the original SM product. Five cities in southern Hebei Province were selected as study areas. Two 16 m GF6 images and nine 500 m MOD09GA images were used as auxiliary data to downscale a timeseries 25 km CCI SM dataset for nine dates from May to June 2019. A total of 151 in situ SM observations collected on 1 May, 21 May, 1 June, and 11 June were used for verification. The results indicated that the downscaled SM data with a 16 m spatial resolution had higher correlation coefficients and lower RMSE values compared with the original CCI SM data. The correlation coefficients between the downscaled SM data and in situ data ranged from 0.45 to 0.67 versus 0.33 to 0.54 for the original CCI SM data; the RMSE values ranged from 0.023 to 0.031 cm3/cm3 versus 0.027 to 0.032 cm3/cm3 for the original CCI SM data. The findings described in this paper can ensure effective farmland management and other practical production applications.

Comprehensive Evaluation of Sentinel-2 Red Edge and Shortwave-Infrared Bands to Estimate Soil Moisture

This article aims to explore the applicability of SMMI (soil moisture monitoring index), MSMMI (modified soil moisture monitoring index), PDI (perpendicular drought index), and MPDI (modified perpendicular drought index) in estimating soil moisture (SM) in farmland. The random forest classifier was used to obtain two-stage land cover types maps. The sensitivity of Sentinel-2 spectral bands to the measured SM at a depth of 0-5 cm was optimized by random forest regression. According to the sensitive bands, SMMI and PDI from different feature spaces were constructed to explore their feasibility for monitoring SM under different land cover types. Second, fractional vegetation cover (FVC) in the study area was estimated by nine kinds of FVC estimation models and compared with the measured FVC. The effects of different FVC methods on estimating SM by MSMMI and MPDI were evaluated. The results show that red edge and short-wave infrared (SWIR) bands of Sentinel-2 had irreplaceable effects on the land cover classification. In terms of monitoring SM in bare soil areas, the SM indices with SWIR bands had high correlations with measured SM. For vegetation-covered areas, MSMMI from the FVCgr model (dimidiate pixel model with red edge bands) and the Short wave infrared1-Short wave infrared2 feature space had the highest correlation with the measured 0-5 cm depth SM. Whether vegetation-covered areas or bare soil areas, the combination of red edge and SWIR bands can effectively improve the estimation accuracy of SM. MSMMI can be used as the best SMMI in the study area. Sentinel-2 images, with great potential, can effectively estimate SM at a depth of 0-5 cm in farmland with complex environments.

A New Drought Index for Soil Moisture Monitoring Based on MPDI-NDVI Trapezoid Space Using MODIS Data

The temperature vegetation dryness index (TVDI) has been commonly implemented to estimate regional soil moisture in arid and semi-arid regions. However, the parameterization of the dry edge in the TVDI model is performed with a constraint to define the maximum water stress conditions. Mismatch of the spatial scale between visible and thermal bands retrieved from remotely sensed data and terrain variations also affect the effectiveness of the TVDI. Therefore, this study proposed a new drought index named the condition vegetation drought index (CVDI) to monitor the temporal and spatial variations of soil moisture status by substituting the land surface temperature (LST) with the modified perpendicular drought index (MPDI). In situ soil moisture observations at crop and pasture sites in Victoria were used to validate the effectiveness of the CVDI. The results indicate that the dry and wet edges in the parameterization scheme of the CVDI formed a better-defined trapezoid shape than that of the TVDI. Compared with the MPDI and TVDI for soil moisture monitoring at crop sites, the CVDI exhibited a performance superior to the MPDI and TVDI in most days where the coefficients of determination (R2) achieved can reach to 0.67 on DOY023, 137, 274 and 0.71 on DOY 322 and reproduced more accurate spatial and seasonal variation of soil moisture. Moreover, the CVDI showed higher correlation with the Australian Water Resource Assessment Landscape (AWRA-L) soil moisture product on temporal scales. The R2 can reach to 0.69 and the root mean square error (RMSE) is also much better than that of the MPDI and TVDI. Overall, it can be concluded that the CVDI appears to be a feasible method and can be successfully used in regional soil moisture monitoring.

Combined Sentinel-1A With Sentinel-2A to Estimate Soil Moisture in Farmland

In this article, seven filter algorithms were compared. The Lee sigma method was more suitable for estimating soil moisture (SM) than the other filtering methods under different land cover types. First, we used a combination of roughness and the dual-polarized Sentinel-1A backscattering coefficients (VV and VH) to estimate SM in bare soil areas. Second, we employed water cloud model (WCM) to remove the influence of vegetation signals on the land surface backscattering and estimate SM in vegetation-covered areas. SM was also retrieved by modified soil moisture monitoring index (MSMMI) and modified perpendicular drought index (MPDI) of Sentinel-2A images. The results show that MSMMI can more accurately monitor SM in bare soil areas, which was slightly better than synthetic aperture radar (SAR) results. The SAR backscattering coefficients after the removal of vegetation influence by WCM can more precisely estimate SM in vegetation-covered areas, which is significantly better than MSMMI and MPDI, especially in high vegetation-covered areas. Optics and SAR differ in their abilities to estimate SM under different land cover, but the powerful fitting ability of machine learning can make full use of their advantages. We employed the generalized regression neural network (GRNN), support vector regression (SVR), random forest regression (RFR), and deep neural network (DNN) algorithms to estimate SM combining Sentinel-1A with Sentinel-2A images. The estimation accuracies of SM by regression algorithms were higher than those by the semiempirical SAR and optical models. The accuracy of estimated SM by DNN was higher than that of GRNN and RFR, which were better than SVR.

Suitability Evaluation of Typical Drought Index in Soil Moisture Retrieval and Monitoring Based on Optical Images

As a basic agricultural parameter in the formation, transformation, and consumption of surface water resources, soil moisture has a very important influence on the vegetation growth, agricultural production, and healthy operation of regional ecosystems. The Aksu river basin is a typical semi-arid agricultural area which seasonally suffers from water shortage. Due to the lack of knowledge on soil moisture change, the water management and decision-making processes have been a difficult issue for local government. Therefore, soil moisture monitoring by remote sensing became a reasonable way to schedule crop irrigation and evaluate the irrigation efficiency. Compared to in situ measurements, the use of remote sensing for the monitoring of soil water content is convenient and can be repetitively applied over a large area. To verify the applicability of the typical drought index to the rapid acquisition of soil moisture in arid and semi-arid regions, this study simulated, compared, and validated the effectiveness of soil moisture inversion. GF-1 WFV images, Landsat 8 OLI images, and the measured soil moisture data were used to determine the Perpendicular Drought Index (PDI), the Modified Perpendicular Drought Index (MPDI), and the Vegetation Adjusted Perpendicular Drought Index (VAPDI). First, the determination coefficients of the correlation analyses on the PDI, MPDI, VAPDI, and measured soil moisture in the 0–10, 10–20, and 20–30 cm depth layers based on the GF-1 WFV and Landsat 8 OLI images were good. Notably, in the 0–10 cm depth layers, the average determination coefficient was 0.68; all models met the accuracy requirements of soil moisture inversion. Both indicated that the drought indices based on the Near Infrared (NIR)-Red spectral space derived from the optical remote sensing images are more sensitive to soil moisture near the surface layer; however, the accuracy of retrieving the soil moisture in deep layers was slightly lower in the study area. Second, in areas of vegetation coverage, MPDI and VAPDI had a higher inversion accuracy than PDI. To a certain extent, they overcame the influence of mixed pixels on the soil moisture spectral information. VAPDI modified by Perpendicular Vegetation Index (PVI) was not susceptible to vegetation saturation and, thus, had a higher inversion accuracy, which makes it performs better than MPDI’s in vegetated areas. Third, the spatial heterogeneity of the soil moisture retrieved by the GF-1 WFV and Landsat 8 OLI image were similar. However, the GF-1 WFV images were more sensitive to changes in the soil moisture, which reflected the actual soil moisture level covered by different vegetation. These results provide a practical reference for the dynamic monitoring of surface soil moisture, obtaining agricultural information and agricultural condition parameters in arid and semi-arid regions.

Performance evaluation of remote sensing data with machine learning technique to determine soil color

<p>The aim of the present research is the determination of soil color by spectral bands and indices obtained from MODIS images. For this purpose, soil samples were collected from East Azerbaijan Province (Iran) and their color and texture were investigated through Munsell color system and hydrometer method, respectively. Stepwise regression, principle component analysis and sensitivity function methods were employed to find the dominant indices and bands using artificial neural network (ANN) as one of the machine learning techniques. The improved indices as the model input had better performance, for example, the calculation of correlation coefficient between indices and hue showed 51.48% increase of correlation coefficient with comparison of the normalized difference vegetation index (NDVI) to modified soil adjustment vegetation index (MSAVI) and 54.54% correlation enhancement of soil adjustment vegetation index (SAVI) compared to MSAVI. Stepwise regression method along with error criteria decline may enhance the performance of soil color model. In comparison with multivariate regression, ANN model exhibited better performance (with a 12.61% mean absolute error [MAE] decline). Temporal variation of modified perpendicular drought index (MPDI) as well as band 31 could justify the Munsell soil color components variations specifically chroma and hue. MPDI and thermal bands could be employed as a precise indicator in soil color analysis. Thus, remote sensing data combined with machine learning technique can clarify the procedure potential for soil color determination.</p>

Daily Evapotranspiration Estimation at the Field Scale: Using the Modified SEBS Model and HJ-1 Data in a Desert-Oasis Area, Northwestern China

Accurate continuous daily evapotranspiration (ET) at the field scale is crucial for allocating and managing water resources in irrigation areas, particularly in arid and semi-arid regions. The authors integrated the modified perpendicular drought index (MPDI) as an indicator of water stress into surface energy balance system (SEBS) to improve ET estimation under water-limited conditions. The new approach fed with Chinese satellite HJ-1 (environmental and disaster monitoring and forecasting with a small satellite constellation) images was used to map daily ET on the desert-oasis irrigation fields in the middle of the Heihe River Basin. The outputs, including instantaneous sensible heat flux (H) and daily ET from the MPDI-integrated SEBS and the original SEBS model, were compared with the eddy covariance observations. The results indicate that the MPDI-integrated SEBS significantly improved the surface turbulent fluxes in water-limited regions, especially for sparsely vegetated areas. The new approach only uses one optical satellite data and meteorological data as inputs, providing a considerable operational improvement for ET mapping. Moreover, HJ-1 high-resolution data promised continuous daily ET at the field scale, which helps in understanding the corresponding relationships among field, crop, and water consumption. Such detailed ET information can greatly serve water resources management in the study area as well as other arid and semi-arid regions.

A Refined Crop Drought Monitoring Method Based on the Chinese GF-1 Wide Field View Data

In this study, modified perpendicular drought index (MPDI) models based on the red-near infrared spectral space are established for the first time through the analysis of the spectral characteristics of GF-1 wide field view (WFV) data, with a high spatial resolution of 16 m and the highest frequency as high as once every 4 days. GF-1 data was from the Chinese-made, new-generation high-resolution GF-1 remote sensing satellites. Soil-type spatial data are introduced for simulating soil lines in different soil types for reducing errors of using same soil line. Multiple vegetation indices are employed to analyze the response to the MPDI models. Relative soil moisture content (RSMC) and precipitation data acquired at selected stations are used to optimize the drought models, and the best one is the Two-band enhanced vegetation index (EVI2)-based MPDI model. The crop area that was statistically significantly affected by drought from a local governmental department, and used for validation. High correlations and small differences in drought-affected crop area was detected between the field observation data from the local governmental department and the EVI2-based MPDI results. The percentage of bias is between −21.8% and 14.7% in five sub-areas, with an accuracy above 95% when evaluating the performance via the data for the whole study region. Generally the proposed EVI2-based MPDI for GF-1 WFV data has great potential for reliably monitoring crop drought at a relatively high frequency and spatial scale. Currently there is almost no drought model based on GF-1 data, a full exploitation of the advantages of GF-1 satellite data and further improvement of the capacity to observe ground surface objects can provide high temporal and spatial resolution data source for refined monitoring of crop droughts.

Temperature-Vegetation-soil Moisture Dryness Index (TVMDI)

While Soil Moisture (SM) can be used as a direct indictor of dryness, the Land Surface Temperature (LST) and vegetation status are indirectly related to SM, and consequently, affect the degree of dryness. Each of these three variables has been individually used to assess the dryness. Furthermore, a combination of the LST and vegetation information has also been applied for dryness estimation through the scatter-plot of the LST and Normalized Difference Vegetation Index (NDVI). However, a combination of these three variables has not been used to estimate the dryness. In this study, a 3 Dimensional (3D) space of the LST, Perpendicular Vegetation Index (PVI), and SM was developed to define a new dryness index named the Temperature-Vegetation-soil Moisture Dryness Index (TVMDI). The TVMDI was evaluated against in situ SM and soil temperature data, as well as the NDVI. The results demonstrated that the TVMDI values were highly correlated with the field soil temperature data and the NDVI (R=0.78 and R=−0.87, respectively). However, even though the dryness values estimated from the TVMDI were not highly correlated with the in situ SM data, the correlation was still acceptable (R=−0.65). Furthermore, a comparison between the proposed index and several satellite-based dryness indices, including the Perpendicular Drought Index (PDI), Modified Perpendicular Drought Index (MPDI), Second Modified Perpendicular Drought Index (MPDI1), and Temperature-Vegetation Dryness Index (TVDI), concluded that the TVMDI was the most accurate index. Moreover, using the TVMDI and multi-temporal Landsat 8 images two dryness maps were produced for the Yanco area to show the effectiveness of the proposed index in depicting the temporal and spatial variation of dryness values for medium spatial resolution images. To show the effectiveness of the TVMDI in large scale applications, two dryness maps from the all of Australia and Iran were also produced using the MODerate Resolution Imaging Spectrometer (MODIS) data. Overall, it was concluded that the TVMDI had a high potential to map dryness over different regions, and is a promising index for early awareness of drought risk management.

Studying drought phenomena in the Continental United States in 2011 and 2012 using various drought indices

Numerous drought indices have been developed to monitor drought conditions. However, different drought indices have differing characteristics, and are suitable for specific environments. The aim of this study was to compare the occurrence of drought, as detected by remote sensing across the Continental United States (CONUS). We used drought events during 2011 and 2012 to compare various indices developed for the study of drought phenomena. Three in situ drought indices, the Palmer drought severity index (PDSI), Z-index and standardized precipitation indices (SPI) with different time scales were used to evaluate drought conditions in different climate divisions. The drought indices compared in this study include the vegetation condition index (VCI), the temperature condition index (TCI), the perpendicular drought index (PDI) and modified PDI (MPDI) derived from moderate-resolution imaging spectroradiometer (MODIS) data, the precipitation condition index (PCI) derived from tropical rainfall measuring mission (TRMM) data, and the soil moisture condition index (SMCI) derived from advanced microwave scanning radiometer - earth observing system (AMSR-E). Other synthesized drought indices, which combine indices such as VCI, TCI, SMCI and PCI, were also compared in this study. These included the vegetation health index (VHI), temperature vegetation dryness index (TVDI), scaled drought condition index (SDCI), the microwave integrated drought index (MIDI), the synthesized drought index (SDI), the optimized meteorological drought index (OMDI), and the optimized vegetation drought index (OVDI). The results include a wide variety of drought conditions based on different drought indices. Meteorological drought indices indicated that more regions were under severe drought than agricultural drought indices did. Drought indices appear to be more similar to standard drought indices than PDI, MPDI and TVDI. The results also indicate that different indices have strengths and weaknesses in different climates across CONUS. SMCI has a good correlation with short-term SPI, and the sensitivity of SMCI is strongly dependent on terrain, as it performs worse in regions with heavy tree cover than in regions with a low density of vegetation. TCI, VCI, PDI and MPDI are more similar to 3-month SPI data, but correlate weakly with station-based indices located in areas of high precipitation, higher soil permeability, large-scale agriculture and forests. PCI is more strongly correlated with short-term drought conditions in almost all climate divisions than other single indices. VCI would be more reliable if there were only red and NIR surface reflectance bands available, while VHI would be a better choice if only NDVI and LST data were available. Condition index-based drought indices (PCI, TCI, VCI, SMCI, VHI, SDCI, SDCI, MIDI, OVDI, OMDI) performed better than other categories of drought indices, and the use of time series analysis may be a contributing factor to this difference in performance.

Assessment of PDI, MPDI and TVDI drought indices derived from MODIS Aqua/Terra Level 1B data in natural lands

The present study aimed to evaluate the potential of remote sensing indices including the perpendicular drought index (PDI), modified perpendicular drought index (MPDI) and temperature vegetation dryness index (TVDI) in drought monitoring in the northeast of Iran during 2003–2013. The mentioned indices were extracted from 254 atmospherically corrected MODIS images, and their accuracy was determined against soil moisture data and also standardized precipitation index (SPI) at monthly and yearly scales. The 1-, 3-, 6-, 9- and 12-month SPI was obtained from 42 weather stations. According to the results, although the MODIS-derived drought indices had different efficiencies at different time scales, they could generally better reflect drought conditions at the yearly scale compared to the monthly scale. At the monthly scale, the PDI, MPDI and TVDI had the greatest correlations with the SPI during the 6-, 9- and 3-month periods ending in February, September and April (−0.60, −0.69 and −0.43, respectively). At the yearly scale, the PDI, MPDI and TVDI had the strongest correlation coefficients with the 6-, 6-, 3-month SPI in 2012 (−0.95), 2012 (−0.96) and 2007 (−0.72), respectively. Moreover, the high correlations (>80%) between the MODIS-derived drought indices and soil moisture data reflected their potential in the mapping of soil moisture in the study area. Considering the constant performance of the MPDI in different time scales, this index can be used as a suitable alternative to the SPI in agricultural drought mapping and monitoring in arid and semiarid areas where the SPI faces certain limitations.

Validating the Modified Perpendicular Drought Index in the North China Region Using <italic>In Situ</italic> Soil Moisture Measurement

Soil moisture content is one of the most important variables for monitoring and assessing the drought condition. In this letter, a modified perpendicular drought index (MPDI) derived from the Moderate Resolution Imaging Spectroradiometer satellite data was validated using in situ soil moisture measurements in Henan province of North China. The soil moisture at different depths of a soil layer and time lag on usefulness of the MPDI for estimating soil moisture content was analyzed; the effectiveness of the MPDI was evaluated with the observed soil moisture content and the comprehensive drought index K for different soil types. The results showed that the MPDI was significantly correlated with soil moisture content for the top soil layer with 10 cm depth. For a time lag ranging from 0 to 4 days, the correlation of MPDI to soil moisture was significant with no time lag in the depth of top 10 cm (r = -0.867). For the stations of the same soil texture type of loam, the correlation coefficient between MPDI and soil moisture is higher than that of all soil texture types. In a regional scale, the MPDI reflected an obvious spatial pattern of drought under different growing stages in the drought years over the study area.

Quantitative evaluation of observation capability of GF-1 wide field of view sensors for soil moisture inversion

Currently, many satellite data are used to invert soil moisture. However, there is no study about quantitative evaluation of observation capability of GF-1 wide field of view (WFV) sensors for soil moisture inversion. Therefore, we proposed a method to evaluate it. We used WFV, Landsat8 Operational Land Imager (OLI), and Moderate-resolution Imaging Spectroradiometer (MODIS) data to invert soil moistures in Wuhan from September 2013 to September 2014 based on the Perpendicular Drought Index (PDI) and modified PDI (MPDI). From the estimated results, the R 2 values, and standard error, we found that both the PDI and MPDI had a significantly negative linear correlation with soil moisture (P < 0.01). Through the values of R, mean absolute error, mean relative error, and root mean square error, we found that a strong relativity existed between the estimated and observed soil mois- tures. It was evident from the results for the WFV, OLI, and MODIS that the performances of WFV and OLI were consistent and that WFV performed better than MODIS. All the results indicated that WFV sensors had a high observation capability for soil moisture inversion in Wuhan. The comprehensive evaluation results for the performance of the PDI and MPDI proved that the MPDI performed better for soil moisture inversion than did the PDI. © 2015 Society of Photo-Optical Instrumentation Engineers (SPIE) (DOI: 10.1117/1.JRS.9.097097)

A modified perpendicular drought index in NIR-Red reflectance space

Soil moisture and vegetation index provides valuable information for surface water content and drought assessment with remotely sensed data. In this paper, a new drought monitoring method (MPDI1) with soil moisture and vegetation index is constructed in NIR-Red reflectance space. The relationship between MPDI1 and soil moisture is explored using satellite image and field measure data, and a comparison among MPDI1, the Perpendicular Drought Index (PDI), and the Modified Perpendicular Drought Index (MPDI) is also evaluated. Results indicate that the MPDI1 is highly accordant with the in-situ ground observation with the coefficient of determination (R2=0.4905) between MPDI1 and 5-20 cm mean soil moisture. Moreover, PDI, MPDI and MPDI1 provide quite similar spatial patterns for bare soil or lower vegetated surface, but MPDI1 demonstrates a better performance in measuring densely vegetated surface. This paper concludes that MPDI1 is a useful tool for surface drought estimation under complex underlying conditions.

Understanding Hydrological Repartitioning and Shifts in Drought Regimes in Central and South-West Asia Using MODIS Derived Perpendicular Drought Index and TRMM Data

In this paper, we use perpendicular drought indices (PDIs) and precipitation patterns to identify hydrological repartitioning in agricultural regions located in central and south-west Asia over the last decade. The spatio-temporal patterns of surface drought derived from perpendicular drought index (PDI) and modified perpendicular drought index (MPDI) from 250-m MODerate Resolution Imaging Spectroradiometer (MODIS) data in 8-day time steps are compared against two other drought indices: the Standardized Precipitation Index (SPI) as a meteorological drought index and the potential evapotranspiration (ET 0 ) as an agro-meteorological drought index, along with 8-day MODIS Normalized Difference Vegetation Index (NDVI). The coefficient of variation as the ratio of standard deviation over inter-annual mean drought indices, precipitation from Tropical Rainfall Measuring Mission (TRMM) data are employed to analyse trends and shifts of hydrological regimes and its connection to both climate change and anthropogenic activities in the study area. Significant correlations are found between the PDI, the MPDI and precipitation and other applied meteorological and agro-meteorological drought indices. The results show that Central Asia is one of the areas across the globe experiencing dramatic shifts in hydrological regimes, and is being severely impacted by population growth and climate change. While acknowledging previous studies that used the PDI and the MPDI over some study regions in Iran, this paper, however, confirms implementation of higher resolution data (MOD09Q1) in both spatial (250 m) and temporal (8-days) dimensions shows a greater agreement between the drought information extracted by the PDI, MPDI, and field meteorological measurements. Supported by satellite based precipitation observations, we conclude that the perpendicular drought indices can be used as a drought early warning system over arid and semi-arid climatological conditions.

The Second Modified Perpendicular Drought Index (MPDI1): A Combined Drought Monitoring Method with Soil Moisture and Vegetation Index

Since soil moisture and vegetation index are direct and important indicators for surface drought status, a new drought monitoring method (MPDI1) is developed in NIR-Red reflectance space. It is a combination of two satellite-derived variables—a soil moisture component using the Perpendicular Drought Index (PDI), and a vegetation component using the Perpendicular Vegetation Index (PVI). Enhanced Thematic Mapper Plus (ETM+) image and in-situ ground observation are introduced to validate the accuracy of the proposed method. Results indicate that MPDI1 is highly consistent to the in-situ ground observation with the coefficient of determination (R2 = 0.49) between MPDI1 and 5–20 cm mean soil moisture, which is slightly higher than the coefficient of determination (R2 = 0.42) between MPDI1 and 10 cm soil moisture. Compared with drought indices such as PDI and the Modified Perpendicular Drought Index (MPDI), MPDI1 provides quite similar trends for bare soil or lower vegetated surface, but it demonstrates a better performance in measuring densely vegetated surface. This paper concludes that MPDI1 provides correct and sufficient information on surface drought status in soil-plant continuum, which appears to have robust available and great potential for surface drought estimation in China and other countries.

Drought monitoring in Iran using the perpendicular drought indices

This paper aims at finding, evaluating and refining an appropriate drought estimation method for semi-arid regions, e.g., Iran using remote sensing. Recently developed methods, the Perpendicular Drought Index (PDI) and Modified Perpendicular Drought Index (MPDI), are selected as satellite based drought indices in this study. Time series of the Moderate Resolution Imaging Spectroradiometer (MODIS) images are collected over the region spanning the time interval from February 2000 to December 2005, and the PDI and MPDI are calculated. Then, these indices are evaluated against meteorological drought indices including Z-score (Z), China-Z Index (CZI) and Modified China-Z Index (MCZI) over 180 meteorological observing stations in Iran. The results show that there is a statistically significant correlation between the PDI and MPDI and regional surface dryness and drought conditions. It is further confirmed that the PDI is performing well for bare soil applications or early stages of vegetation growth, while the MPDI is best for vegetated surfaces yet effective for bare soils. Since Iran is characterized by semi-arid or arid climatic conditions, the perpendicular drought indices could be used as simple remote sensing-based drought indices in Iran and in other developing countries with similar climatic conditions.

Agricultural drought monitoring in semi-arid and arid areas using MODIS data

SUMMARYThe performances of two remote sensing drought indices were evaluated at selected agricultural sites in different agro-climatic zones in Iran to detect the severity of drought phenomena related to temporal variation and different climatic conditions. The indices used were the perpendicular drought index (PDI) and the modified perpendicular drought index (MPDI), which are derived from moderate resolution imaging spectroradiometer (MODIS) satellite images (MOD13A3 V005). The correlations between these perpendicular indices and two other remote sensing indices in ten different agro-climatic zones of Iran from February 2000 to December 2005 were analysed. The additional indices evaluated were the enhanced vegetation index (EVI) and the vegetation condition index (VCI) along with five water balance parameters, including climatic water balance (CL), crop water balance (CR), monthly reference crop evapotranspiration (ET0), crop evapotranspiration (ETc) and required irrigation water (I). Winter wheat was selected as the reference crop because it is grown in the majority of climatic conditions in Iran.The results show that in several climatic regions, there is a statistically significant correlation between PDI and MPDI and the water balance parameters, indicating an acceptable performance in detecting crop drought stress conditions. In all zones except at the sites located in northwest and northeast of Iran, VCI and EVI are less correlated with the applied water balance indicators compared to PDI and MPDI. In a temporal analysis, PDI and MPDI showed a greater ability to detect CR conditions than VCI and EVI in the most drought-sensitive winter wheat-growing stages. Since Iran is characterized by arid or semi-arid climatic conditions and winter wheat is a major agricultural crop, a combination of both PDI and MPDI could be used as simple remote sensing-based tool to map drought conditions for crops in Iran and in other developing countries with similar climatic conditions.