Vegetation Supply Water Index Research Articles

Drought Monitoring of Winter Wheat in Henan Province, China Based on Multi-Source Remote Sensing Data

Characterized by soil moisture content and plant growth, agricultural drought occurs when the soil moisture content is lower than the water requirement of plants. Microwave remote sensing observation has the advantages of all-weather application and sensitivity to soil moisture change. However, microwave remote sensing can only invert 0~5 cm of soil surface moisture, so it cannot effectively reflect the drought situation of farmland. Therefore, this study took Henan Province as the study area, used soil moisture active and passive (SMAP) satellite soil moisture data, employed NDVI, LST, and ET as the independent variables, and took the drought grade on the sample as the dependent variable. Using the 2017–2019 data as the training set and the 2020 data as the testing set, a random forest drought monitoring model with comprehensive influence of multiple factors was constructed based on the training set data. In the process of model training, the cross-validation method was employed to establish and verify the model. This involved allocating 80% of the sample data for model construction and reserving 20% for model verification. The results demonstrated an 85% accuracy on the training set and an 87% accuracy on the testing set. Additionally, two drought events occurring during the winter wheat growing period in Henan Province were monitored, and the validity of these droughts was confirmed using on-site soil moisture and the vegetation supply water index (VSWI). The findings indicated a high incidence of agricultural drought in the southwestern part of Henan Province, while the central and northern regions experienced a lower incidence during the jointing to heading and filling stages. Subsequently, leveraging the results from the random forest drought monitoring, this study conducted a time series analysis using the Mann–Kendall test and a spatial analysis employing Moran’s I index to examine the temporal and spatial distribution of agricultural drought in Henan Province. This analysis aimed to unveil trends in soil moisture changes affecting agricultural drought, as observed via the SMAP satellite (NASA). The results suggested a possible significant spatial auto-correlation in the occurrence of agricultural drought.

Study loss of vegetative cover and increased land surface temperature through remote sensing strategies under the inter-annual climate variability in Jinhua-Quzhou basin, China.

The Jinhua-Quzhou basin in China is one of the most susceptible areas to drought. Due to the loss of vegetation and great fluctuations in rainfall and surface temperature, global warming occurs. Timely, accurate, and effective drought monitoring is crucial for protecting local vegetation and determining which vegetation is most vulnerable to increased LST during the period 1982-2019. It assumes a strong correlation between loss of vegetation cover, changes in monsoon climate, drought, and increases in land surface temperature (LST).Due to significantly increased in LST, low precipitation and vegetation cover, NDVI, TVDI, VCI, and NAP are useful in characterizing drought mitigation strategies.The temperature vegetation drought index (TVDI), normalized difference vegetation index (NDVI), vegetation condition index (VCI), and monthly precipitation anomaly percentage (NAP) can be helped to characterize drought reduction strategies. Monthly NDVI, NAP, VCI, TVDI, normalized vegetation supply water index (NVSWI), temperature condition index (TCI), vegetation health index (VHI), and heat map analysis indicate that the Jinhua-Quzhou basin experienced drought during 1984, 1993, 2000, and 2011. Seasonal SR, WVP, WS, NDVI, VCI, and NAP charts confirm that the Jinhua-Quzhou basin was affected by severe drought in 1984, which continued and led to severe droughts in 1993, 2000, and 2011. Regression analysis showed a significant positive correlation between NDVI, TVDI, VCI, and NAP values, while NVSWI, TVDI, and VHI showed positive signs of good drought monitoring strategies. The research results confirm the correlation between loss of vegetation cover and LST, which is one of the causes of global warming. The distribution of drought changed a trend indicating that compared with the Jinhua region; the Quzhou region has more droughts. The changing trend of drought has characteristics from 1982 to 2019, and there are significant differences in drought changing trends between different Jinhua-Quzhou basin areas. Overall, from 1982 to 2019, the frequency of drought showed a downward trend. We believe that these results will provide useful tools for drought management plans and play a relevant role in mitigating the effects of drought and protecting humanity from climate hazards.

The research of common drought indexes for the application to the drought monitoring in the region of Jin Sha river

Abstract Based on MODIS data from 2010 to 2020 and precipitation, air temperature, and soil moisture data of 33 meteorological stations in Jinsha River Basin from 1990 to 2020, the applicability of different remote sensing drought indexes in Jinsha River Basin was studied. These indexes include temperature condition index (TCI) and temperature vegetation drought index (TVDI), the results of vegetation condition index (VCI), vegetation supply water index (VSWI), standardized precipitation evapotranspiration index (SPEI), and standardized precipitation index (SPI) showed that TCI and TVDI, VSWI and TCI, VSWI and TVDI, VSWI and TVDI, VSWI and TVDI, VSWI and TVDI, VSWI and TVDI, SPEI and SPI, respectively. The correlation between VSWI and VCI was significant. VCI had the lowest correlation with SPEI and SPI. The average correlation coefficient between TCI and VSWI was similar. The correlation between VSWI, SPEI, and SPI was low in January, March, and October and reached significant or above levels in other months. TVDI had the highest correlation with SPEI and SPI. TVDI was significantly correlated with soil moisture every month of the year, indicating that TVDI can be effectively used for remote sensing drought monitoring in Jinsha River Basin and has strong adaptability. According to the temporal and spatial analysis of drought monitoring in the Jinsha River Basin by TVDI, the drought areas in December and January are mainly located in the middle reaches of the Jinsha River Basin, while the light drought areas are mainly located in the upper and lower reaches of the Jinsha River Basin. From March to June, the risk of severe drought increased in the middle reaches of the Jinsha River, and the moderate drought area in the Jinsha River Basin also increased. The drought from July to November was weaker than in the previous months. The moderate drought area is mainly located in the middle and lower reaches of the Jinsha River, and the mild drought area is mainly distributed in the upper reaches of the Jinsha River Basin.

Assessing the Spatial–Temporal Pattern of Spring Maize Drought in Northeast China Using an Optimised Remote Sensing Index

Northeast China plays a pivotal role in producing commodity grains. The precipitation and temperature distribution during the growth season is impacted by geographical and climate factors, rendering the region vulnerable to drought. However, relying on a single index does not reflect the severity and extent of drought in different regions. This research utilised the random forest (RF) model for screening remote sensing indices. Relative soil moisture (RSM) was employed to compare seven commonly used indices: the temperature vegetation dryness index (TVDI), vegetation supply water index (VSWI), vegetation condition index (VCI), temperature condition index (TCI), vegetation health index (VHI), multi-band drought index (MBDI), and normalised difference drought index (NDDI). The effectiveness of these indices for monitoring drought during different developmental stages of spring maize was evaluated. Trend rates were employed to investigate the temporal changes in drought patterns of spring maize from 2003 to 2020, and the Sen + Mann–Kendall test was used to analyse spatial variations. The results showed the following: (1) The seven remote sensing indices could accurately track drought during critical growth stages with the TVDI demonstrating higher applicability than the other six indices. (2) The application periods of two TVDIs with different parameters differed for the drought monitoring of spring maize in different developmental periods. The consistency and accuracy of the normalised difference vegetation index (NDVI)-based TVDI (TVDIN) were 5.77% and 34.62% higher than those of the enhanced vegetation index (EVI)-based TVDI (TVDIE), respectively, in the early stage. In contrast, the TVDIE exhibited 13.46% higher consistency than the TVDIN in the middle stage, and the accuracy was the same. During the later stage, the TVDIE showed significantly higher consistency and accuracy than the TVDIN with consistency increases of 9.61% and 38.64%, respectively. (3) The drought trend in northeast China increased from 2003 to 2020, exhibiting severe spring drought and a weakening of the drought in summer. The southern, southwestern, and northwestern parts of northeast China showed an upward drought trend; the drought-affected areas accounted for 37.91% of the study area. This paper identified the most suitable remote sensing indices for monitoring drought in different developmental stages of spring maize. The results provide a comprehensive understanding of the spatial–temporal patterns of drought during the past 18 years. These findings can be used to develop a dynamic agricultural drought monitoring model to ensure food security.

Drought in Shanxi Province Based on Remote Sensing Drought Index Analysis of Spatial and Temporal Variation Characteristics

Drought is a natural disaster with long duration and which causes great harm. Studying the characteristics of drought evolution in Shanxi Province can grasp the regularity of drought occurrence and provide a basis for drought prevention and resistance. This study utilizes MODIS products to analyze and quantify the extent of drought in a specific area. The study calculates several indices, including the Crop Water Stress Index (CWSI), Vegetation Supply Water Index (VSWI), and Temperature Vegetation Dryness Index (TVDI), using variables such as the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), Evapotranspiration (ET), and Potential Evapotranspiration (PET). Additionally, three drought indices are analyzed for correlation with the self-calibrated Palmer Drought Severity Index (sc-PDSI), and the most suitable drought index is selected through validation with typical drought events. Finally, the selected indices are used to investigate the spatiotemporal characteristics of drought in the study area from 2001 to 2020. The results show: (1) CWSI and sc-PDSI have a strong correlation both in terms of time and spatial analysis. Furthermore, CWSI has been shown to be more effective in monitoring significant drought events. (2) The multi-year mean values of CWSI range from 0.71 to 0.85, with a significant degree of spatial heterogeneity. In the study area, the percentage of the area affected by different levels of drought is in the following order: moderate drought > severe drought > mild drought > no drought. (3) The trend of CWSI changes shows that the drought situation in Shanxi Province has been alleviated from 2001 to 2020, and the overall spatial distribution indicates that the degree of drought alleviation in the southern region is greater than that in the northern region. The turning point from drought to wetness in the study area was in 2011, showing the overall characteristic of “dry in the north and wet in the south”.

Analysis of the impact of expressway construction on soil moisture in road areas.

To reveal the effect pattern of expressways on regional soil moisture, in this study, using trend analysis and buffer zone analysis methods, the data of VSWI (vegetation supply water index) in central Zhejiang Province from 2005 to 2016 were extracted from landsat7 satellite data using a single window algorithm, and spatial analysis was used to investigate the law of its differentiation. The results show that the multi-year average is 0.01879, between 0.01035-0.02774, showing a gentle decreasing trend, and there are obvious regional variations in space. We found that the impact of the new expressway and interchange on the VSWI in the buffer zone lasted for more than two years, and the VSWI increased in space farther away from the road, and this trend returned to normal at 8 km. Finally, the development patterns of the VSWI in the buffer zone of the newly established expressway and the interchange are approximately the same.

Improving drought mitigation strategies and disaster risk reduction through MODIS and TRMM-based data in relation to climate change over Pakistan.

Drought is an extreme climatic event that mostly occurs as a result of low rainfall, which leads to lack of water in various agro-ecological conditions of Pakistan. The condition could be further exacerbated by the prevailing dry weather. Therefore, accurate, timely, and efficient drought monitoring is crucial to ensure that its adverse effects are mitigated. In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) and TRMM-based data were used together with remote sensing techniques to improve drought mitigation and disaster risk reduction strategies. In order to monitor drought mitigation and disaster risk reduction strategies in Pakistan, the crop water stress index (CWSI), vegetation condition index (VCI), normalized vegetation supply water index (NVSWI), vegetation health index (VHI), and temperature vegetation drought index (TVDI) were chosen as the instrument. Due to low rainfall and significantly low vegetation, CWSI, NDVI, TVDI, and VHI are useful in characterizing drought mitigation strategies in Pakistan. Monthly NDVI, NAP, NVSWI, TVDI, VCI, and VHI values and heat map analysis show that Pakistan suffered from drought in years 2001, 2002, and 2006. Seasonal CWSI, NDVI, VHI, and TVDI confirmed that Pakistan was affected by severe drought in 2001, which continued and led to severe drought in 2002 and 2006. We generate spatial correlation coefficients between NDVI and NVSWI, VCI, and VHI, and NVSWI and VCI and VHI, while the VCI and VHI values are significantly positively correlated. CWSI, NDVI, VHI, and TVDI show positive signs of effective climate change drought mitigation and disaster risk reduction strategies in Pakistan. Thus, these drought indices have been confirmed to be a complete drought monitoring indicator and reduce the risk of drought in Pakistan.

Assessment of meteorological and agricultural droughts using remote sensing and their impact on groundwater in an agriculturally productive part of Northwest India

Wheat is an important component of global food security, and Punjab and Haryana are the major wheat-growing areas in Northwest India, but climate change has increased the frequency of droughts in this region. Timely, precise, and effective drought monitoring is critical to ensure food security. In the present study, meteorological and agricultural droughts were assessed using standard precipitation index (SPI) and remote sensing-based indices, namely vegetation health index (VHI) and normalized vegetation supply water index (NVSWI) during the Rabi season (wheat growing season) in three different agroclimatic zones (ACZs i.e., ACZ I: Hot arid agro-climatic zone, ACZ II: Semi-arid agro-climatic zone, and ACZ III: Hot sub-humid agro-climatic zone) of Punjab and Haryana. Furthermore, correlations between NVSWI, VHI, SPI, and gross primary productivity (GPP) were investigated, as well as trend analysis of NVSWI and groundwater level to better understand their interrelationships and the impact of agricultural droughts on groundwater level. The results of SPI indicated that ACZ III witnessed more drought episodes than ACZ I and II. The driest years in ACZ I, ACZ II, and ACZ III were 2002–03, 2014–15, and 2016–17, respectively, while the year 2018–19 was observed as normal in all the ACZs. A significant high correlation was observed between the NVSWI and VHI and the NVSWI and GPP in all three ACZs, while SPI did not exhibit a significant correlation with the NVSWI in ACZ II and III. The Mann-Kendall Test and Sen’s slope indicated a declining groundwater trend in all the ACZs and an increasing trend in NVSWI values. The results underline the robustness of SPI and NVSWI in drought monitoring in semi-arid and arid zones of northwest India. Rainfall variability and increased groundwater use for irrigation practices in semi-arid and hot sub-humid areas are leading to groundwater decline.

Spatiotemporal Comparison of Drought in Shaanxi–Gansu–Ningxia from 2003 to 2020 Using Various Drought Indices in Google Earth Engine

As a common natural disaster, drought can significantly affect the agriculture productivity and human life. Compared to Southeast China, Northwest China is short of water year-round and is the most frequent drought disaster area in China. Currently, there are still many controversial issues in drought monitoring of Northwest China in recent decades. To further understand the causes of changes in drought in Northwest China, we chose Shaanxi, Gansu, and Ningxia provinces (SGN) as our study area. We compared the spatiotemporal characteristics of drought intensity and frequency in Northwest China from 2003 to 2020 showed by the Standardized Precipitation Index (SPI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Vegetation Health Index (VHI), Normalized Vegetation Supply Water Index (NVSWI), Soil Moisture Condition Index (SMCI), and Soil Moisture Agricultural Drought Index (SMADI). All of these indices showed a wetting trend in the SGN area from 2003 to 2020. The wetting trend of the VCI characterization is the most obvious (R2 = 0.9606, p < 0.05): During the period 2003–2020, the annual average value of the VCI in the SGN region increased from 28.33 to 71.61, with a growth rate of 153.57%. The TCI showed the weakest trend of wetting (R2 = 0.0087), with little change in the annual average value in the SGN region. The results of the Mann–Kendall trend test of the TCI indicated that the SGN region experienced a non-significant (p > 0.05) wetting trend between 2003 and 2020. To explore the effectiveness of different drought indices, we analyzed the Pearson correlation between each drought index and the Palmer Drought Severity Index (PDSI). The PDSI can not only consider the current water supply and demand situation but also consider the impact of the previous dry and wet conditions and their duration on the current drought situation. Using the PDSI as a reference, we can effectively verify the performance of each drought index. SPI-12 showed the best correlation with PDSI, with R values greater than 0.6 in almost all regions and p values less than 0.05 within one-half of the study area. SMADI had the weakest correlation with PDSI, with R values ranging −0.4~−0.2 and p values greater than 0.05 in almost all regions. The results of this study clarified the wetting trend in the SGN region from 2003 to 2020 and effectively analyzed the differences in each drought index. The frequency, duration, and severity of drought are continuously reduced; this helps us to have a more comprehensive understanding of the changes in recent decades and is of significance for the in-depth study of drought disasters in the future.

Crop production response to soil moisture and groundwater depletion in the Nile Basin based on multi-source data

Soil moisture (SM) and groundwater (GW) depletion triggered by anthropogenic and natural climate change are influencing food security via crop production per capita decrease in the Nile River Basin (NRB). However, to the best of our understanding, the causes and impact of SM and GW depletion have not been studied yet comprehensively in the NRB. In this study, GW is derived from the Gravity Recovery and Climate Experiment (GRACE) mission, and SM was estimated using the Triple Collocation Analysis (TCA). SM/GW depletion causes were evaluated via the Land Use Land Cover (LULC) and rainfall/temperature change analysis, whereas impact analysis focused on crop production per capita reduction (food insecurity) during SM depletion. The major findings of this study are 1) TCA analyzed SM show a decreasing trend (−0.06 mm/yr) in agricultural land while increasing (+0.21 mm/yr) in forest land, 2) LULC analysis indicated a vast increment of agricultural land (+9%) and bareland (+9%) although the decreasing pattern of forest (−1.5%) and shrubland (−6.9%) during 1990–2019; 3) the impact of SM depletion on crop production per capita caused food insecurity during a drought year, 4) agriculture drought indices and crop production per capita show high correlations (R2 = 0.86 to 0.60) demonstrated that Vegetation Supply Water Index (VSWI) could provide strategic warning of drought impacts on rainfed agricultural regions. In conclusion, SM and GW depletions are mainly caused by human-induced and climate change factors imposing food insecurity challenges in the NRB coupled with increasing temperature and excessive water extraction for irrigation. Therefore, it is highly recommended to rethink and reverse SM/GW depletion causing factors to sustain food security in NRB and similar basins.

Daily Spatial Complete Soil Moisture Mapping Over Southeast China Using CYGNSS and MODIS Data.

Daily spatial complete soil moisture (SM) mapping is important for climatic, hydrological, and agricultural applications. The Cyclone Global Navigation Satellite System (CYGNSS) is the first constellation that utilizes the L band signal transmitted by the Global Navigation Satellite System (GNSS) satellites to measure SM. Since the CYGNSS points are discontinuously distributed with a relativity low density, limiting it to map continuous SM distributions with high accuracy. The Moderate-Resolution Imaging Spectroradiometer (MODIS) product (i.e., vegetation index [VI] and land surface temperature [LST]) provides more surface SM information than other optical remote sensing data with a relatively high spatial resolution. This study proposes a point-surface fusion method to fuse the CYGNSS and MODIS data for daily spatial complete SM retrieval. First, for CYGNSS data, the surface reflectivity (SR) is proposed as a proxy to evaluate its ability to estimate daily SM. Second, the LST output from the China Meteorological Administration Land Data Assimilation System (CLDAS, 0.0625° × 0.0625°) and MODIS LST (1 × 1 km) are fused to generate spatial complete and temporally continuous LST maps. An Enhanced Normalized Vegetation Supply Water Index (E-NVSWI) model is proposed to estimate SM derived from MODIS data at high spatial resolution. Finally, the final SM estimation model is constructed from the back-propagation artificial neural network (BP-ANN) fusing the CYGNSS point, E-NWSVI data, and ancillary data, and applied to get the daily continuous SM result over southeast China. The results show that the estimation SM are comparable and promising (R = 0.723, root mean squared error [RMSE] = 0.062 m3 m−3, and MAE = 0.040 m3 m−3 vs. in situ, R = 0.714, RMSE = 0.057 m3 m−3, and MAE = 0.039 m3 m−3 vs. CLDAS). The proposed algorithm contributes from two aspects: (1) validates the CYGNSS derived SM by taking advantage of the dense in situ networks over Southeast China; (2) provides a point-surface fusion model to combine the usage of CYGNSS and MODIS to generate the temporal and spatial complete SM. The proposed approach reveals significant potential to map daily spatial complete SM using CYGNSS and MODIS data at a regional scale.

A viable drought vulnerability index for outermost small islands in Indonesia

The rate and duration of precipitation in many outermost small islands within the Indonesian region have been projected to decrease during the dry season due to prolonged droughts. Thus, drought mitigation plans for these islands must be adaptive and continually improved. As a result of recent advances, drought periods can be monitored and relevant information communicated using water requirement assessment embedded within a geographical information system (GIS)-based model. Although improved tools and models have been used to analyse data concerning water conditions, only a few studies have focused on drought in the outermost small islands in Indonesia. The main purpose of this study was to inform regulatory agencies on the drought status of these small islands. Thus, the viable drought vulnerability index (DVI) was developed to contribute to the construction of a support system to make relevant decisions about the drought status of the islands. Specific parameters were selected to develop and analyse DVI, involving three considerations: meteorological, physical, and socio-economic aspects. This study focused on the analysis of data from water resources availability (rainfall, surface water, and groundwater), population, and interpretations from GIS to establish the rainfall index (RI), water use index (WUI), water supplying vegetation index (WSVI), and critical-land rate index (CRI) to develop a viable DVI. The results provide a clear understanding for all stakeholders concerning the drought problem and use a viable index and vulnerable map as valuable tools for water management in the study area. The results also confirm that a GIS approach is effective in identifying and measuring the vulnerability index in the outermost small island area, aiding in the implementation of better adaptive drought management practices for sustainable development.

Drought characterization across agricultural regions of China using standardized precipitation and vegetation water supply indices

Understanding the spatiotemporal dynamics of drought and its potential effect on crop production is critical in decision-making processes to support sustainable food production under climate change. This is especially true for China, the largest producer of cereal crops in the world, where production losses could significantly impact the global food chain. Here, we utilized global remote sensing products to study the spatiotemporal dynamics of meteorological and agricultural droughts across all of China during 1982–2018 and their potential linkage with winter wheat and summer corn yield losses since 2000. Specifically, we derived the agricultural Standardized Precipitation Index (aSPI; at 1, 3, and 6-month scales) and the Standardized Vegetation Supply Water Index (SVSWI) and explored their regional-scale dynamics across four sub-regions of China. Results suggest that the frequency of extreme drought events has increased in China since 2000, mostly affecting the winter wheat growing season (i.e., December–May) across North China. This increased frequency of drought events followed consistent spatiotemporal patterns of 3-month aSPI, mostly affecting Northwest/North China, Qinghai-Tibet, and South China during April, December, and May, respectively. The 3-month aSPI and SVSWI anomalies showed a good correlation with winter wheat yield anomaly across the Qinghai-Tibet area and corn yield across South China. The summer corn yield across all sub-regions and winter wheat yield across some agricultural zones in North China showed significant decreasing trends since 2000. This study provides a useful understanding of the spatiotemporal dynamics of agricultural drought and its potential implications for crop production in China.

Evaluating the performance of eight drought indices for capturing soil moisture dynamics in various vegetation regions over China

Drought is pervasive global hazard and seriously impacts ecology. Particularly, vegetation drought, which is chiefly driven by soil moisture (SM) deficiency, has a direct bearing on grain production and human livelihoods. Various drought indices associated with vegetation and SM conditions have been proposed to monitor and detect vegetation drought. In this study, we evaluated the performance of eight drought indices, including Drought Severity Index (DSI), Evaporation Stress Index (ESI), Normalized Vegetation Supply Water Index (NVSWI), Temperature-Vegetation Dryness Index (TVDI), Temperature Vegetation Precipitation Dryness Index (TVPDI), Vegetation Health Index (VHI), Self-calibrating Palmer Drought Severity Index (SC-PDSI) and Standardized Precipitation Evapotranspiration Index (SPEI), for capturing SM dynamic (derived from Copernicus Climate Change Service) across the six main vegetation coverage types of China. Our results showed DSI and ESI had the best overall performance. When exploring the reasons for the uncertainty of these indices (except SC-PDSI and SPEI) in the evaluation, we found that, in the non-arable regions, the time lag effect of drought indices on SM, the average state and rangeability of corresponding variables and the climatic conditions (precipitation and temperature) all impacted the performance of DSI, ESI, NVSWI, TVPDI and VHI. In the arable region, cropland types (paddy field and non-paddy field) and the uncertainty of SM data mainly caused the uncertainties of the above five indices. With regard to the TVDI, abnormalities of dry and wet edges fitting may be the primary factor affecting its performance. These results demonstrated that these drought indices with reliable and robust performance of capturing SM dynamics can be suggested to characterize the trend of SM. Certainly, this study can provide a reference for the improvement of existing drought indices and the establishment of new drought indices.

Spatiotemporal monitoring and change detection of vegetation cover for drought management in the Middle East

The Middle East (ME), as an arid and semi-arid region, is prone to environmental risks and stresses, such as drought are inseparable phenomena of the region. In this study, an approach for identifying sustained vegetation cover (SVC) is suggested to identify the connection between SVC and drought. Normalized difference vegetation index (NDVI) and land surface temperature (LST) were used to filter zones of rich vegetation cover from poorly vegetated or non-vegetated regions of the ME. The change detection of vegetation cover was computed by the NDVI differencing technique, and the vegetation condition index (VCI) and normalized vegetation supply water index (NVSWI) were used to derive drought indices. The standardized precipitation index (SPI) and rainfall anomaly index (RAI) were used to monitor the intensity of meteorological drought events. A comparison of the estimates of vegetation change, remote sensing-based VCI, and meteorological drought indices revealed that the highest SVC is concurrent with the occurrence of drought. Moreover, it was found that the most severe meteorological drought and VCL-based drought condition occurred in 2008 and that the highest percentage of SVC was also obtained for this year. The results suggest the possibility of using the SVC instead of other spectral indices, such as the NDVI, VCI, NVSWI, and NVSWI, for the superior assessment and detection of environmental stresses such as drought.

New approach for drought assessment: A case study in the northern region of Minas Gerais

Drought poses a major threat to food security and maintenance of rural populations in dry lands, because it leads to losses in agriculture and other productive sectors. Given the intensification and recurrence of drought events and their impacts, it is necessary to deepen our knowledge as a key to subsidize mechanisms for drought preparation and mitigation plans. Therefore, this study aimed to develop and apply methodologies for drought characterization and impact assessment, considering different agro-ecosystems and the impact on smallholder farmers. The new approach developed included as dataset LULC maps, the limits of rural properties, and the Vegetation Supply Water Index (VSWI) time series. The z-score was used in order to identify areas affected by droughts and a zonal statistic was used to compare the results of VSWI in a municipal and rural property scale. Afterwards, the areas identified as significant statistics were used to determine the LCLU and rural property most affected. The results of the analysis showed that Agricultural Lands were the most affected LULC during the study period (about 50%), whereas Micro and Small Properties were the most affected by droughts, over than 65% and 15% respectively. Besides, an important finding is that the analysis at the municipality or rural property levels can affect the drought assessment. This can result in an underestimated or overestimated of drought intensity and in the expansion of impacts. Thus, these results contribute to the local drought mitigation actions and help determine the threshold to better spatialize drought impacts on smallholder farming.

Retrospective analysis and version improvement of the satellite-based drought composite index. A semi-arid Tensift-Morocco application

This paper aims to offer an improved version of the new composite drought monitoring index (CDMI) and to test its applicability in the context of Tensift watershed in Morocco. A synergistic approach incorporating the remote sensing techniques, hydrometeorological data, simulated data and agricultural statistics was used for this purpose. After assessing the performance of CDMI, estimated Soil Moisture Anomaly Indicator (ISMA) was processed, validated and incorporated to the composite model. Random Forest algorithm was used to determine the weight of composite model components. Apart from comparative mapping, Pearson's correlation statistical analysis, linear regression and dependency tests were used to assess the performance of the improved composite model (CDMIa_RF). The result show that CDMIa_RF is better correlated with several indices such as: the Standardized Precipitation Index (SPI), (R2=0.74); Hydrological Drought Index (R2=0.70); grain productivity (R2=0.70), CDMI (R2=0.95), Vegetation Health Index (VHI), (R2=0.87), and Normalized Vegetation Supply Water Index (NVSWI), (R2= 0.85).

Monitoring of agricultural drought in semi-arid ecosystem of Peninsular India through indices derived from time-series CHIRPS and MODIS datasets

The reliable and consistent remote sensing-based drought indices along with Geographic Information System (GIS) play a significant role in the mapping, and monitoring of agricultural drought. The core objective of the present study is to monitor agricultural drought dynamics over the semi-arid Rayalaseema region of Peninsular India during the year 2000 to 2018 by using the Normalized Vegetation Supply Water Index (NVSWI) derived from time-series remote sensing data products. NVSWI is the normalization of Vegetation Supply Water Index (VSWI), which was computed by integrating the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST). The analysis of Standardized Precipitation Index (SPI) shows that in dry year (2001), majority of the study area experienced extreme dry conditions (SPI < −2.0) due to the deficit rainfall during the growing season. The spatio-temporal analysis of NVSWI reveals that the western, central, and northern parts of the study area were prone to frequent droughts of moderate to severe intensity. The high temperatures and below normal rainfall are found to be the major triggering factors for the droughts in the region. During the years 2001, 2002, 2003, 2006, 2009, 2014, 2017, and 2018 worst droughts were observed in the study region. The Pearson correlation analysis between monthly NVSWI with 1-month SPI and Vegetation Health Index (VHI) for the growing season of dry (2001) and wet (2007) years clearly show a significant positive correlation. The study clearly demonstrates the potential of NVSWI as one of the robust indices in the assessment and monitoring of agricultural droughts.

Evaluation of Drought Monitoring Effect of Winter Wheat in Henan Province of China Based on Multi-Source Data

Monitoring agricultural drought is important to food security and the sustainable development of human society. In order to improve the accuracy of soil moisture and winter wheat yield estimation, drought monitoring effects of optical drought index data, meteorological drought data, and passive microwave soil moisture data were explored during individual and whole growth periods of winter wheat in 2003–2011, taking Henan Province of China as the research area. The model of drought indices and relative meteorological yield of winter wheat in individual and whole growth periods was constructed based on multiple linear regression. Results showed a higher correlation between Moderate-Resolution Imaging Spectroradiometer (MODIS) drought indices and 10 cm relative soil moisture (RSM10) than 20 cm (RSM20) and 50 cm (RSM50). In the whole growth period, the correlation coefficient (R) between vegetation supply water index (VSWI) and RSM10 had the highest correlation (R = −0.206), while in individual growth periods, the vegetation temperature condition index (VTCI) was superior to the vegetation health index (VHI) and VSWI. Among the meteorological drought indices, the 10-day, 20-day, and 30-day standard precipitation evapotranspiration indices (SPEI1, SPEI2, and SPEI3) were all most relevant to RSM10 during individual and whole growth periods. RSM50 and SPEI3 had a higher correlation, indicating that deep soil moisture was more related to drought on a long time scale. The relationship between Advanced Microwave Scanning Radiometer for EOS soil moisture (AMSR-E SM) and VTCI was stable and significantly positive in individual and whole growth periods, which was better compared to VHI and VSWI. Compared with the drought indices and the relative meteorological yield in the city, VHI had the best monitoring effect during individual and whole growth periods. Results also showed that drought occurring at the jointing–heading stage can reduce winter wheat yield, while a certain degree of drought occurring at the heading–milk ripening stage can increase the yield. In the whole growth period, the combination of SPEI1, SPEI2, and VHI had the best performance, with a coefficient of determination (R2) of 0.282 with the combination of drought indices as the independent variables and relative meteorological yield as the dependent variable. In the individual growth period, the model in the later growth period of winter wheat performed well, especially in the returning green–jointing stage (R2 = 0.212). Results show that the combination of multiple linear drought indices in the whole growth period and the model in the returning green–jointing period could improve the accuracy of winter wheat yield estimation. This study is helpful for effective agricultural drought monitoring of winter wheat in Henan Province.

Retrieval of cotton plant water content by UAV-based vegetation supply water index (VSWI)

ABSTRACTKnowing plant water content (PWC) is of great significance for precision irrigation of field crop. The aim of this study is to monitor the PWC of cotton non-destructively in situ. A six-band multispectral camera embedded on an unmanned aerial vehicle (UAV) was used to collect images at flowering and boll-forming stages of the cotton. Thirteen vegetation indices (VI) were extracted from the camera. Consequently, all the VIs were fused with canopy temperature mathematically into vegetation supply water indices (VSWI). Unary and multivariate models were used to establish the relationship between VSWIs and the water content of leaf, petiole, stalk as well as bud & boll, respectively. Results indicated significant correlations (P < 0.01) between the VSWI from green index (VSWI_GI) and leaf water content (LWC), and between the VSWI from MERIS terrestrial chlorophyll index by the second infrared band (VSWI_MTCI2) and bud & boll water content (BWC). The correlation coefficients between the stalk water content (SWC) and VSWI_MTCI2 as well as VSWI_DATT2 were both −0.895. The best retrieval model of LWC, SWC, and BWC were the multivariate linear models for the much higher estimation ability. Coefficients of determination for modelling and validation were close to or greater than 0.8, and the root-mean-square errors (RMSE) for validation were less than 0.17, and the relative errors (RE) were less than 18%. The results showed all these models have relatively high accuracy and can provide a new method to efficiently monitor water content in cotton plants.