Vegetation Drought Research Articles

Monitoring drought events and vegetation dynamics in relation to climate change over mainland China from 1983 to 2016.

Due to the present drought events and dynamics of vegetation, the circumstances in mainland China could become even more serious. Therefore, we study the impact of drought on vegetation trends in mainland China, with the aim of discovering the temporal and spatial differences in vegetation dynamics caused by seasonal drought. Our method is based on the use of data from the AVHRR Normalized Difference Vegetation Index (NDVI) from 1983 to 2016 and temperature and precipitation data from Modern Era Retrospective Analysis for Research and Applications (NASA's MERRA). Due to the sparse vegetation and low drought, NDVI is the most useful for describing drought conditions in mainland China. The NDVI, TCI, VHI, NVSWI, VCI, TVDI, and NAP from April to October increased rapidly, while the NDVI, TCI, VHI, NVSWI, NAP, TVDI, and VCI are stable every month in September, improve again in October, and then show in December a downward trend. The NDVI, TCI, VHI, NVSWI, NAP, TVDI, and VCI monthly values indicate that mainland China suffered from severe drought in 1984 and 1993, which lasted until 2007, which were the most drought years. For monitoring drought in mainland China, the NDVI, TVDI, NAP, VCI, and NVSWI values were selected as a tool for reporting drought events during different growing seasons. The seasonal values of TVDI, NDVI, NAP, NVSWI, and VCI confirmed that mainland China suffered from severe drought in 1984, 1993, and 2007 and led the durations of severe drought. Spatial correlation is generated between NDVI, TCI, VHI, NVSWI, NAP, TVDI, and VCI. The correlation between NDVI, TCI, VHI, NAP, and VCI showed a significantly positive correlation while significantly negative correlation between NVSWI and TVDI, TVDI and VHI, which showed a good indication for the assessment of drought, especially for the agricultural regions of mainland China. This shows that the positive sign to support NAP, NVSWI, and TVDI is a good monitoring of the drought indices. During the summer, it appears that compared to the southeastern part of mainland China, drought is more likely to occur in the northwestern areas. There is no doubt that these drought indices are comprehensive indicators of monitoring drought events in mainland China.

Remote sensing strategies to characterization of drought, vegetation dynamics in relation to climate change from 1983 to 2016 in Tibet and Xinjiang Province, China.

Due to various land cover changes, vegetation dynamics, and climate, drought is the most complex climate-related disaster problem in Tibet and Xinjiang, China. The purpose of the present study is to analyze the performance of the AVHRR Normalized Vegetation Index (NDVI) and the temporal and spatial differences of seasonal vegetation dynamics by correlating the results with rainfall and temperature data of NASA's MERRA to examine the vegetation dynamics and droughts in Tibet and the Xinjiang Province of China. Our method is based on the use of AVHRR NDVI data and NASA MERRA temperature and precipitation during 1983-2016. Due to the dryness and low vegetation, NDVI is more useful to describe the drought conditions in Tibet and Xinjiang of China. The NDVI, TCI, VHI, NVSWI, VCI, TVDI, and NAP from April to October increased rapidly. While the NDVI, TCI, VHI, NVSWI, NAP, TVDI, and VCI are stable every month in September, again improve in October, and then confirm downward trend in December. The NDVI, TCI, VHI, NVSWI, NAP, VCI, and TVDI monthly values indicate that Tibet and Xinjiang province of China suffered from severe drought in 2006, 2008, and 2012 which were the most drought years. For monitoring drought in Tibet and Xinjiang province of China, the NDVI, TVDI, NAP, VCI, and NVSWI values were selected as a tool for reporting drought events during different growing seasons. Seasonal values of TVDI, NDVI, NAP, NVSWI, and VCI confirmed that Tibet and Xinjiang province of China suffered from severe drought in 2006, 2008, and 2012 and led the durations of severe drought. The correlation between NDVI, TCI, VHI, NAP, TVDI, and VCI showed a significantly positive correlation, while the significantly negative correlation between NVSWI and NDVI showed a good indication for the assessment of drought, especially for the agricultural regions of Tibet and Xinjiang province of China. This shows that the positive sign to support NAP, NVSWI, and TVDI is good monitoring of the drought indexes in Tibet and the Xinjiang province of China.

Multiscale drought monitoring and comparison using remote sensing in a Mediterranean arid region: a case study from west-central Morocco

During the last few decades, the frequency of drought has significantly increased in Morocco especially for arid and semi-arid regions, leading to a rising of several environmental and economic issues. In this work, we analyse the spatial and temporal relationship between vegetation activity and drought severity at different moments of the year, across an arid area in the western Haouz plain in Morocco. Our approach is based on the use of a set of more than 30 satellite Landsat images acquired for the period from 2008 to 2017, combined with the Standardized Precipitation Index (SPI) at different time scales and Standardized Water-Level Index (SWI). The Mann-Kendall and Sen’s slope methods were used to estimate SPI trends, and the Pearson correlation between NDVI and SPI was calculated to assess the sensitivity of vegetation types to drought. Results demonstrated that SPI experienced an overall downward trend in the Chichaoua-Mejjate basin, except for SPI3 in summer. The vegetation activity is largely controlled by the drought with clear differences between seasons and time scales at which drought is assessed. Positive correlations between the NDVI and SPI are dominant across the entire study area except in June when almost half of correlations is negative. More than 80% of the study domains exhibit a correlation exceeding 0.4 for SPI3 and SPI6 in March. Importantly, this study stresses that the irrigation status of land can introduce some uncertainties on the remote sensing drought monitoring. A weak correlation between the SPI and the SWI was observed at different time scale. The fluctuations of the piezometric levels are strongly affected by the anthropogenic overexploitation of aquifers and proliferation of irrigated plots.

Selected breakpoints of net forest carbon uptake at four eddy-covariance sites

Extensive studies are available that analyse time series of carbon dioxide and water flux measurements of FLUXNET sites over many years and link these results to climate change such as changes in atmospheric carbon dioxide concentration, air temperature and growing season length and other factors. Many of the sites show trends to a larger carbon uptake. Here we analyse time series of net ecosystem exchange, gross primary production, respiration, and evapotranspiration of four forest sites with particularly long measurement periods of about 20 years. The regular trends shown are interrupted by periods with higher or lower increases of carbon uptake. These breakpoints can be of very different origin and include forest decline, increased vegetation period, drought effects, heat waves, and changes in site heterogeneity. The influence of such breakpoints should be included in long-term studies of land-atmosphere exchange processes.

A study on the evolution mechanism of agricultural landscape pattern of Dadu River Valley in Danba Region

Taking the Dadu River Basin in the Danba area of Ganzi Prefecture, Sichuan Province as the research area, based on the 2013 and 2016 Landsat8 remote sensing images, the temperature vegetation drought index (TVDI) method is used to divide the Dadu River dry valley into 6 arid gradient regions. Using ArcGIS10.5 software and Fragstats4.2 software to calculate the landscape pattern index of different arid gradient areas in different years, combined with the survey results of agricultural policies, development models, and agricultural landscape patterns in key regions, analyze the evolution of agricultural landscape patterns under different drought gradients. The results show that, except for other forestlands, the degree of landscape fragmentation is decreasing year by year on the gradient of light and moderate drought, and the degree of spatial heterogeneity is higher. On the gradient of extreme drought, the degree of landscape fragmentation is higher, and the degree of spatial heterogeneity is lower.

Comparing Farmers’ Perceptions of Climate Variability with Meteorological and Remote Sensing Data, Implications for Climate Smart Agriculture Technologies in Ghana

In-depth knowledge of smallholder farmers’ perception of changing climate variables such as recurrent and protracted droughts, late onset of rainfall, early cessation of rainfall and their coping adaptation strategies are very significant in designing climate resilient agriculture among smallholder food crop farmers in sub-Saharan Africa (SSA). This paper examines smallholder farmers’ perceptions of climate variability vis-á-vis meteorological and satellite remote sensing data and their implications for climate smart agriculture technologies. Integration of meteorological, satellite remote sensing and farm-level data were used. Multistage sampling procedure was used to select four towns, eight communities and 398 smallholder food crop farmers. Spearmans’ rank correlation coefficient and Standardized Precipitation Index were used to assess the distribution of climate variables. In addition, three vegetation drought characteristic indices, Normalized Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI) and Water Supply Vegetation Index (WSVI) were used to examine drought conditions within the basin. The results indicated that smallholder farmers in the Offin river basin perceived recurrent and prolonged droughts, rising temperatures, late onset of rainfall, early cessation of rainfall, increasing dry spells, reduction in the length of rainfall season and shorten cropping season as a main indicators of climate variability. The findings further revealed that farmers’ perceptions on climate variability strongly agrees with meteorological and satellite remote sensing data which not only demonstrated rising temperature and frequent and prolonged droughts but also late onset and early cessation of rainfall and reduction in growing season rainfall. Smallholder food crop farmers in the Offin river basin have a high awareness of variation in climate condition and have taken coping strategies to reduce the effects of climate change and climate variability. Smallholder food crop farmers in the basin have also adopted climate smart agriculture technologies such as crop management techniques, integrated soil and nutrient management practices, tillage and residue management, small scale irrigation systems, inland valleys cropping and renewable energy systems to increase agricultural productivity and build resilience to climate variability. The policy implication is that, smallholder food crop farmers’ knowledge on climate variability should be considered as a practical input in designing and planning climate variability coping adaptation and mitigation strategies.

Agroclimate assessment of vegetation drought period in the extremely insufficient low water content in the Black sea zone

Agroclimate assessment of vegetation drought period in the extremely insufficient low water content in the Black sea zone

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.

In-situ and triple-collocation based evaluations of eight global root zone soil moisture products

Root zone soil moisture (RZSM) is a vital variable for vegetation growth, drought monitoring and agricultural water management. Satellite remote sensing measures soil moisture at the surface layer, while RZSM is derived usually by model-based simulations. Here, we provide the first comprehensive evaluation of eight RZSM products at a global scale, including GLDAS NOAH, ERA-5, MERRA-2, NCEP R1, NCEP R2, JRA-55, SMAP level 4 and SMOS level 4 datasets. An in-situ validation based on the stations from the International Soil Moisture Network (ISMN) and a triple collocation (TC) evaluation are both conducted to assess the accuracy of these RZSM products. SMAP exhibits the median highest correlation and the median lowest RMSE with in-situ stations over North America. In the TC analysis, MERRA-2 shows the highest median correlation and the median lowest error standard deviation with the unknown truth, followed by GLDAS, SMAP, JRA-55 and ERA-5. A temporal pattern analysis indicates that SMOS has a dry bias relative to other datasets and NCEP R1 has larger seasonal variations relative to other datasets over Asia and North America. The TC analysis indicates that MERRA-2, SMAP, GLDAS, JRA-55, and ERA-5 have better performance relative to other datasets. SMAP is not as good as GLDAS, MERRA-2 and JRA-55 in RZSM estimation over forest areas. The possible factors influencing RZSM performance are discussed, including precipitation forcing, assimilated observations, radio frequency interference issue and validation methods. These results and conclusions may provide new insights for the improvement of model-based RZSM estimation.

Opportunities and Threats of Mediterranean Evergreen Sclerophyllous Woody Species Subjected to Extreme Drought Events

Climate change and extreme drought and heat events impact the Mediterranean evergreen sclerophyllous vegetation in South Europe, especially in Iberian and Italian peninsula, where widespread crown defoliation and dieback have been observed since the 90s of the XX century. Field observations and long-term experiments showed different sensitivity of the various woody species, Quercus ilex and Arbutus unedo being prone to drought, whereas Phillyrea latifolia and Pistacia lentiscus appeared to be resistant. The present review aims at exploring the phylogenetic and evolutionary basis of the resistance (or susceptibility) to drought of Mediterranean vegetation and its possible mechanisms of resilience. The main findings are summarized as follows: (1) Mediterranean regions in the world are refuge areas for several plant evolutive lineages and migratory routes. Evergreen sclerophyllous species, currently presented in Mediterranean basin, evolved under different climatic conditions; (2) the evergreen habitus represents an adaptation to mild drought conditions. Deciduous (specially summer deciduous) species are better performing under severe drought and low air relative humidity than evergreen species; (3) severe drought events acts selectively by favouring the species evolved in the Quaternary era and those originated in drier regions; (4) the evergreen trees and shrubs are resilient to the severe drought events and can restore the pre-event condition by resprouting from dormant buds in the cambium tissue. This ability is related to the non-structural carbohydrate content in the parenchyma-rays in woody stems. The amount and availability of these strategic reserve can be compromised by frequent drought events; (5) plant seed regeneration can be affected by drought and seedling establishment may be limited by soil dryness and microenvironment conditions; (6) the role of phenotypic plasticity of the species and epigenetic responses in Mediterranean-type ecosystems, although discussed in few papers, is still poorly known. We hypothesize that instead of latitudinal (South to North) or altitudinal (lowland to upland) plant migrations, Mediterranean forest ecosystems may respond to climate change by modulating their species composition and community structure with genetic resources (i.e., taxonomic diversity) already present in loco. Changes in vegetation assemblages and community structure may lead changes in ecological and landscape ecosystem values, with changes in related ecosystem services. A redefinition of management criteria of natural resources and a pro-active silviculture to make forest ecosystems more resilient are required.

A remote sensing study of spatiotemporal variations in drought conditions in northern Asir, Saudi Arabia.

Changes in vegetation land cover are influenced by, and therefore an indicator of, climatic conditions. The aim of this study is to investigate the relationship between vegetation cover changes and drought events in a small-scale area. Six Landsat images during 1987-2019 were used to extract information about the vegetation land cover changes using the normalized difference vegetation index (NDVI) and the fractional vegetation cover (FVC) in Balqarn Governorate in the northern mountains of Asir, Saudi Arabia. Two climatic parameters, temperature and precipitation, were used as time series for the same period and were decomposed to investigate the seasonal and trend changes for each parameter. The two parameters were also used to calculate the standardized precipitation evapotranspiration index (SPEI) to conduct an in-depth analysis of the drought events influencing vegetation cover. The results showed that the state of the vegetation coverage of the study area remained at a medium level with an average NDVI value, but the FVC values showed evidence of dynamic variability associated with drought and moisture events. The SPEI showed that the study area has been undergoing a long-duration drought event since 2004, ranging from light to severe drought, which was consistent with the time series decomposition results. This investigation has revealed that drought drives changes in vegetation cover and is expressed on small geographic scales as changes in the vegetation cover structure. The framework described here is simple and can be used to evaluate and manage drought risks.

Comparing rotation forests and extreme gradient boosting for monitoring drought damage on KwaZulu-Natal commercial forests

This study explored the utilization of rotation forests (RTF) and extreme gradient boosting (XGBoost) machine learning algorithms (MLAs) to classify drought damage in commercial forests in KwaZulu-Natal (KZN). These algorithms were trained using information obtained from the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) derived vegetation and conditional drought indices. The results demonstrated that both algorithms were capable of accurately detecting trees that exhibit drought damage and those that did not, this was more apparent when classifying based on information derived from conditional drought indices that yielded an overall accuracy of 82% and 76% for XGBoost and RTF, respectively. However, the accuracy decreased when using vegetation indices data, resulting in an accuracy of 69% and 72% for XGBoost and RTF, respectively. Overall, the results demonstrated that MLAs could be utilized for classifying drought damage on forest vegetation. Additionally, the study showed that MODIS imagery could be used for MLA classification.

Drought monitoring of the maize planting areas in Northeast and North China Plain

Remote sensing technology can cost-effectively access to a wide range of real-time land surface spatial information, therefore it is widely used in agricultural crops monitoring. And timely assessment of drought stress using remote sensing is valuable for improving the crop management level. In this study, the drought stress was evaluated in the maize planting areas of the Northeast and North China Plain using satellite remote sensing technology. The absolute distance index was used to identify the spatial pattern of the maize planting areas using the moderate resolution imaging spectroradiometer (MODIS) derived enhanced vegetation index data that processed with Savitzky–Golay filter. The temperature vegetation drought index (TVDI) was derived from MODIS normalized difference vegetation index and land surface temperature data. Simultaneously, soil moisture content (SMC) was obtained for an eight-day period and matched with satellite remote sensing data to characterize the drought stress of maize. After estimating the field capacity and wilting point, the SMC was further transformed into the real available water content (RAWC) of the soil. Our results indicated that negative correlations between RAWC and TVDI was observed for the 0–10 cm (R2 = 0.594) and 20–30 cm (R2 = 0.641) soil layers, respectively, which was significantly better than the correlation between the SMC and TVDI in the 0–10 (R2 = 0.396) and 20–30 cm (R2 = 0.499) soil layers. Moreover, the sensitive period regarding the maize water demand were identified (day of year 161, 169, 233, 241, and 249) via the linear regression analysis of the measured maize yield and TVDI for eight-day intervals. Furthermore, the weights of the water demand of maize in different growth stages was determined via multiple linear regression analysis, and a comprehensive drought index (TVDISW) was established to evaluate the drought stress in the whole maize growth period. Eventually, a comprehensive analysis for the drought stress in the study area was conducted combined with various environmental factors. Furthermore, the performance of this method was independent of the meteorological and field survey data, which may facilitate the mapping of drought monitoring of crops planting areas at a large scale.

Assessment of drought frequency, severity, and duration and its impacts on vegetation greenness and agriculture production in Mediterranean dryland: A case study in Tunisia

Drought is among the highest-impact natural hazards affecting drylands around the world in a warming climate. The Mediterranean region, including Tunisia, is projected to experience the most predominant drying trends worldwide. However, a detailed regional scale study of drought for Tunisia has been limited, hampering an assessment of drought impact on the ecosystem and society. This study explored drought characteristics and its effect on vegetation greenness and agricultural productivity in three vegetation zones of Tunisia during 1982–2011, taking advantage of both meteorological and soil moisture drought indices and identifying the most appropriate index for each zone. The results revealed that meteorological droughts were short and frequent, triggering soil moisture droughts that were long-lasting and intense. The standardized precipitation index was identified as the best indicator of vegetation and agricultural droughts in the Northern forest, while the Palmer Drought Severity Index was best in the Central steppe and Southern desert (no crop data in the Southern desert). The lag-correlation analysis revealed that the response of both vegetation greenness and wheat productivity to droughts was more pronounced and had a longer significant lag in the Central steppe than in the other regions. These results suggest that arable land species (Central steppe and Southern desert), characterized by shallow roots, have a rapid response to rainfall variability when compared with forest species (Northern forest), which have deep roots allowing them an adequate supply of moisture. The region-specific indices identified here will provide a useful measure for drought monitoring and mitigation in Tunisia.

Detecting drought-induced GPP spatiotemporal variabilities with sun-induced chlorophyll fluorescence during the 2009/2010 droughts in China

In the parallel with the climate change, droughts have become one of the major climate extremes that induce losses in the terrestrial gross primary production (GPP). However, studying of the drought effects on GPP is still challenging, partly due to the lack of direct observations of GPP at large scales. Aiming to explore the potential of spaceborne sun-induced chlorophyll fluorescence (SIF) in monitoring vegetation droughts and quantifying the drought-induced GPP variabilities, we evaluated GOME-2 SIF with two state-of-art GPP products (i.e. FLUXCOM GPP and GLASS GPP) and flux tower observed GPP, using 2009 summer drought in Northeast China and 2010 spring drought in Southwest China as study cases. We found that SIF was a quantifying indicator of GPP and successfully characterized the temporal dynamics and spatial extents of GPP anomalies during the drought events, as well as accurately estimating the drought-induced GPP losses. Moreover, SIF (R2 = 0.87) performed better than enhanced vegetation index (R2 = 0.78) in capturing the photosynthesis changes induced by droughts, suggesting SIF was more sensitive to plant physiological changes. The strong positive correlations of SIF with Palmer drought severity index (PDSI) and root-zone soil moisture, further gave confidences on the capacity of SIF in vegetation drought monitoring. SIF had a time lag response to both PDSI and soil moisture, which might be attributable to the plants adaptation mechanisms for the drought occurrence and physiological recovery after water stress. Our study demonstrates that satellite-based SIF can achieve real-time vegetation drought monitoring and the assessment of drought-induced GPP anomalies at large scales, thus providing a unique opportunity to study the impacts of drought on vegetation carbon uptake.

Monitoring monthly soil moisture conditions in China with temperature vegetation dryness indexes based on an enhanced vegetation index and normalized difference vegetation index

Soil moisture is a key land surface parameter that reflects conditions of drought in agricultural areas, such that variations in soil moisture significantly impact agricultural production. At a large spatial scale, soil moisture can be indicated using the temperature vegetation drought index (TVDI), which is based on soil moisture monitoring. Such metrics allow us to know whether or not the soil in a region is experiencing drought. The TVDI can be estimated by using either the normalized difference vegetation index (NDVI) or the enhanced vegetation index (EVI) combined with land surface temperature (LST) data. Our objectives were to study the applicability of the NDVI-based TVDI (TVDINDVI) and EVI-based TVDI (TVDIEVI) in the monitoring of soil moisture at a large time-space scale. The TVDINDVI and TVDIEVI were calculated by constructing a feature space based on the NDVI (or EVI) and LST to obtain the corresponding dry/wet-edge equation. By establishing a correlation relationship between the TVDIs and soil moisture at soil depths of 0–10, 0–40, 0–100, and 0–200 cm, the applicability of the TVDIEVI and TVDINDVI was investigated for seven different climatic regions in mainland China. The relationships between soil moisture at the 0–10-cm depth and the NDVI (or EVI) in cold and warm seasons were obtained. Linear functions of soil moisture and the TVDINDVI (TVDIEVI) were constructed. From the results, both the TVDINDVI and TVDIEVI were more suitable for monitoring soil moisture at the 0–10-cm depth than at the other depths. The regions that had the best correlations between soil moisture and the TVDINDVI (TVDIEVI) were in northwestern China during the cold season and southeastern China during the warm season (from April to September), meaning that the TVDI was more applicable in these region/season combinations. The TVDINDVI showed better performance in reflecting soil moisture conditions than the TVDIEVI, especially in the warm season. The applicability of the TVDIEVI for soil moisture at the 0–10-cm depth was better than that of the TVDINDVI in northwestern China. However, in the other regions, the TVDINDVI performed better. The performance of TVDINDVI (TVDIEVI) was better than TVDIEVI (TVDINDVI) under different circumstances, which was related to the goodness of fit for wet/dry-edge equations, natural variability of NDVI/EVI/LST, depths, changes in seasons (vegetation cover), and regions. This research compared the applications of the TVDI in China comprehensively.

Effects of vegetation restoration on groundwater drought in the Loess Plateau, China

The Loess Plateau (LP) is a typical water-limited area. Since the revegetation plan started in 1999, the rapid growth of vegetation has not only significantly changed the local water cycle, but also probably affected regional groundwater drought. However, the effect of revegetation on groundwater drought remains largely unexplored. To this end, we isolated the groundwater anomalies from terrestrial water storage of the Gravity Recovery and Climate Experiment satellites and soil moisture of the Global Land Data Assimilation System, and further used the drought severity index to characterize groundwater drought. The evolution characteristics of groundwater drought in the LP were analyzed, and the effect of revegetation plan on groundwater drought were explored. Results indicated that: (1) Normalized Difference Vegetation Index (NDVI) during 2003 ~ 2015 in the LP was growing rapidly, meanwhile, groundwater storage significantly decreased (p < 0.01) and groundwater drought intensified in terms of its area and intensity; (2) compared with meteorological factors, NDVI is more strikingly correlated (p < 0.05) with groundwater drought on annual, seasonal and monthly scales, especially near the key areas of vegetation restoration; (3) the growth rate of vegetation is a dominant factor affecting groundwater drought in the LP, in which the groundwater consumption rate caused by vegetation dynamics in the significant area is higher than non-significant area. Our research results provide guidance for formulation scientific and sustainable ecological restoration policies in the LP, and also offer new ideas for the study of the relationship between vegetation and groundwater drought.

The use of combined soil moisture data to characterize agricultural drought conditions and the relationship among different drought types in China

Drought monitoring and assessment are of great importance due to the costly damage caused by drought. Datasets, drought indexes and drought relationships are three critical areas of drought research. Satellite-retrieved soil moisture (SM) products derived from the European Space Agency Climate Change Initiative (ESA CCI) show application potential in drought monitoring. However, the products are missing certain data in some areas. The model-assimilated SM product derived from the Global Land Data Assimilation System (GLDAS) was used to supplement these missing data. The main goals of this paper are to characterize agricultural drought after the utility and applicability of the combined SM product and the monthly scaled soil water deficit index (SWDI) have been evaluated and to investigate the relationships among meteorological, agricultural and vegetation droughts. First, we provided a long series of highly accurate SM products through simple calculations. The drought index, SWDI, was extended to a monthly scale for long-term drought analysis by using the combined SM product. The probability of detection (POD) between the SWDI and in situ drought records performed fairly well. Half of the 566 stations had PODs higher than 0.9, and one-third of these stations had POD values equal to 1. Through correlation analysis and grey incidence analysis (GIA) between the standardized precipitation index (SPI) and SWDI, we found that the propagation time from meteorological drought to agricultural drought was shorter under drier conditions than wetter conditions, and at the regional scale, the response time ranged from 1 month to 2.5 months. Correlation analysis between the SWDI and vegetation condition index (VCI) indicated that there was no delay effect from agricultural to vegetation drought on a monthly scale in most parts of China except in several provinces distributed in the South; additionally, there was a significant time lag in forests, while grassland and agriculture were more inclined to have no time lag or the response time was less than 1 month.

LAND COVER / LAND USE MONITORING FOR AGRICULTURE FEATURES CLASSIFICATION

Abstract. Remote sensing applications are directed to agricultural observation and monitoring. It has been huge of scientific papers are dedicated to the research of the contribution of remote sensing for agriculture studies. There are several global challenges needed to be considered within agriculture activities. It can be embraced by the main agriculture sector facing the obstacles impacting the production and productivity of the sector. These are the following options that can be pointed out: biomass and yield estimation; vegetation vigor and drought stress monitoring; assessment of crop phenological development; crop acreage estimation and cropland mapping; and mapping of disturbances and Land Use/Land Cover changes. In this study has been undertaken the realization of satellite-based Land Use/Land Cover monitoring based on various optical satellite data. It has been used satellite images taken from satellites AZERSKY, RapidEye, Sentinel-2B and further processed for Land Use/Land Cover classification. Following the complex approach of the supervised and unsupervised classification, the methodology has been used for satellite image processing. As the main satellite imagery for monitoring crop condition were AZERSKY taken during the growing season, from May to June of 2019 year. The study area was some part of the Sheki region, which covers the central part of the southern slope of the Greater Caucasus Mountain Range within Azerbaijan Republic. In this research work satellite imagery processing and mapping has been carried out on the basis of software application of ArcGIS Pro 2.5.

DESERTIFICATION ASSESSMENT USING MEDALUS MODEL IN UPPER LAMCHIENGKRAI WATERSHED, THAILAND

Abstract. Desertification is a global environmental problem. It affects harmful on economic, social and environmental that ultimately effects on quality of human life. Thailand is the 174th member of the UNCCD, according to the Thailand report of desertification by LDD (2004). It was found that the area of degraded land or desertification land in Thailand was 33.57 million hectares which were agricultural soil problem. Soil erosion and soil salinity are major problems for agricultural soil in Thailand. Thus, to prevent and fix such problems, assessment and evaluation of soil properties are essential. Lamchiengkrai watershed in Nakhon Ratchasima province presents soil salinity exposure area which is a major problem in the Northeast region of Thailand. This study aims to access a new approach for assessing the extent and the risk of desertification land by MEDALUS model based on geoinformatics technology in upper Lamchiengkrai watershed, Nakhon Ratchasima province. MEDALUS model is the factors of desertification assessment. Four groups of factors were examined, including vegetation (fire risk, erosion protection, and drought resistance), climatic (rainfall and rainfall erosivity), soil (soil texture, electrical conductivity, organic matter, soil depth, drainage, and slope), and human activity factor (land use and soil erosion). The results of the study indicated that 67.25% of the area was classified as high risk, 30.54% was classified as moderate risk and 2.22% was classified as low risk to desertification land. In addition, the factors affected on the high-risk area were climate and vegetation factors. Moderate risk area was influenced by the human activity factor and soil factors.