On the separate retrieval of soil and vegetation temperatures from ATSR data

The Along-Track Scanning Radiometer (ATSR) onboard the European Remote Sensing satellite (ERS) is presently the only one available to provide quasi-simultaneous thermal infrared measurements at two view angles. Such data represent an opportunity to explore the potential information on the directional observations in the thermal infrared region, in view of the preparation of a new generation of multi-angle satellite sensors. Based on the analysis of one ATSR image, the results of this work indicate that the magnitude of the directional effect on the brightness temperature (ground anisotropic radiance), although quite sensitive to errors in atmospheric conditions, may still be retrieved with acceptable uncertainty. In order to retrieve both vegetation and soil temperatures from directional brightness temperatures, it is shown that an appropriate description of the nature and content of the pixel is needed, otherwise this retrieval will be quite uncertain.

Dynamic Drought Monitoring in Guangxi Using Revised Temperature Vegetation Dryness Index

Moderate resolution imaging spectroradiometer （MODIS） data are very suitable for vast extent, long term and dynamic drought monitoring for its high temporal resolution, high spectral resolution and moderate spatial resolution. The composite Enhanced Vegetation Index （EVI） and composite land surface temperature （Ts） obtained from MODIS data MOD11A2 and MOD13A2 were used to construct the EVI-Ts space. And Temperature Vegetation Dryness Index （TVDI） was calculated to evaluate the agriculture drought in Guangxi province, China in October of 2006. The results showed that the drought area in Guangxi was evidently increasing and continuously deteriorating from the middle of September to the middle of November. The TVDI, coming from the EVI-Ts space, could effectively indicate the spatial distribution and temporal evolution of drought, so that it could provide a strong technical support for the forecasting agricultural drought in south China.

Vegetation dynamics and its response to climate change in Central Asia

The plant ecosystems are particularly sensitive to climate change in arid and semi-arid regions. However, the responses of vegetation dynamics to climate change in Central Asia are still unclear. In this study, we used the normalized difference vegetation index（NDVI） data to analyze the spatial-temporal changes of vegetation and the correlation of vegetation and climatic variables over the period of 1982–2012 in Central Asia by using the empirical orthogonal function and least square methods. The results showed that the annual NDVI in Central Asia experienced a weak increasing trend overall during the study period. Specifically, the annual NDVI showed a significant increasing trend between1982 and 1994, and exhibited a decreasing trend since 1994. The regions where the annual NDVI decreased were mainly distributed in western Central Asia, which may be caused by the decreased precipitation. The NDVI exhibited a larger increasing trend in spring than in the other three seasons. In mountainous areas, the NDVI had a significant increasing trend at the annual and seasonal scales; further, the largest increasing trend of NDVI mainly appeared in the middle mountain belt（1,700–2,650 m asl）. The annual NDVI was positively correlated with annual precipitation in Central Asia, and there was a weak negative correlation between annual NDVI and temperature. Moreover, a one-month time lag was found in the response of NDVI to temperature from June to September in Central Asia during 1982–2012.

Soil Moisture Monitoring Based on Land Surface Temperature-Vegetation Index Space Derived from MODIS Data

Soil moisture has been considered as one of the main indicators that are widely used in the fields of hydrology, climate, ecology and others. The land surface temperature-vegetation index （LST-VI） space has comprehensive information of the sensor from the visible to thermal infrared band and can well reflect the regional soil moisture conditions. In this study, 9 pairs of moderate-resolution imaging spectroradiometer （MODIS） products （MOD09A1 and MODllA2）, covering 5 provinces in Southwest China, were chosen to construct the LST-VI space, and then the spatial distribution of soil moisture in 5 provinces of Southwest China was monitored by the temperature vegetation dryness index （TVDI）. Three LST-VI spaces were constructed by normalized difference vegetation index （NDVI）, enhanced vegetation index （EVI）, and modified soil-adjusted vegetation index （MSAVI）, respectively. The correlations between the soil moisture data from 98 sites and the 3 TVDIs calculated by LST-NDVI, LST-EVI and LST-MSAVI, respectively, were analyzed. The results showed that TVDI was a useful parameter for soil surface moisture conditions. The TVDI calculated from the LST-EVI space （TVDIE） revealed a better correlation with soil moisture than those calculated from the LST-NDVI and LST-MSAVI spaces. From the different stages of the TVDIE space, it is concluded that TVDIE can effectively show the temporal and spatial differences of soil moisture, and is an effective approach to monitor soil moisture condition.

Spatio-temporal Variations in Drought with Remote Sensing from the Mongolian Plateau During 1982–2018

The Mongolian Plateau is one of the regions most sensitive to climate change,the more obvious increase of temperature in 21 st century here has been considered as one of the important causes of drought and desertification.It is very important to understand the multi-year variation and occurrence characteristics of drought in the Mongolian Plateau to explore the ecological environment and the response mechanism of surface materials to climate change.This study examines the spatio-temporal variations in drought and its frequency of occurrence in the Mongolian Plateau based on the Advanced Very High Resolution Radiometer(AVHRR)Normalized Difference Vegetation Index(NDVI)(1982–1999)and the Moderate-resolution Imaging Spectroradiometer(MODIS)(2000–2018)datasets;the Temperature Vegetation Dryness Index(TVDI)was used as a drought evaluation index.The results indicate that drought was widespread across the Mongolian Plateau between1982 and 2018,and aridification incremented in the 21 st century.Between 1982 and 2018,an area of 164.38×10^4 km^2/yr suffered from drought,accounting for approximately 55.28%of the total study area.An area of approximately 150.06×10^4 km^2(51.43%)was subject to more than 160 droughts during 259 months of the growing seasons between 1982 and 2018.We observed variable frequencies of drought occurrence depending on land cover/land use types.Drought predominantly occurred in bare land and grassland,both of which accounting for approximately 79.47%of the total study area.These terrains were characterized by low vegetation and scarce precipitation,which led to frequent and extreme drought events.We also noted significant differences between the areal distribution of drought,drought frequency,and degree of drought depending on the seasons.In spring,droughts were widespread,occurred with a high frequency,and were severe;in autumn,they were localized,frequent,and severe;whereas,in summer,droughts were the most widespread and frequent,but less severe.The increase in temperature,decrease in precipitation,continuous depletion of snow cover,and intensification of human activities have resulted in a water deficit.More severe droughts and aridification have affected the distribution and functioning of terrestrial ecosystems,causing changes in the composition and distribution of plants,animals,microorganisms,conversion between carbon sinks and carbon sources,and biodiversity.We conclude that regional drought events have to be accurately monitored,whereas their occurrence mechanisms need further exploration,taking into account nature,climate,society and other influencing factors.

Comprehensive drought monitoring in Yunnan Province, China using multisource remote sensing data

Development of drought monitoring techniques is important for understanding and mitigating droughts and for rational agricultural management. This study used data from multiple sources, including MOD13 A3, TRMM 3 B43, and SRTMDEM, for Yunnan Province, China from 2009 to 2018 to calculate the tropical rainfall condition index(TRCI), vegetation condition index(VCI), temperature condition index(TCI), and elevation factors. Principal component analysis(PCA) and analytic hierarchy process(AHP) were used to construct comprehensive drought monitoring models for Yunnan Province. The reliability of the models was verified, following which the drought situation in Yunnan Province for the past ten years was analysed. The results showed that:(1) The comprehensive drought index(CDI) had a high correlation with the standardized precipitation index, standardized precipitation evapotranspiration index, temperature vegetation dryness index, and CLDAS(China Meteorological Administration land data assimilation system), indicating that the CDI was a strong indicator of drought through meteorological, remote sensing and soil moisture monitoring.(2) The droughts from 2009 to 2018 showed generally consistent spatiotemporal changes. Droughts occurred in most parts of the province, with an average drought frequency of 29% and four droughtprone centres.(3) Monthly drought coverage during 2009 to 2014 exceeded that over 2015 to 2018. January had the largest average drought coverage over the study period(61.92%). Droughts at most stations during the remaining months except for October exhibited a weakening trend(slope > 0). The CDI provides a novel approach for drought monitoring in areas with complex terrain such as Yunnan Province.

Winter Wheat Drought Monitoring and Comprehensive Risk Assessment： Case Study of Xingtai Administrative District in North China

Drought monitoring is the base for drought coping and adaptation. Xingtai is located in North China＇s key winter wheat production areas where drought is severe and frequent. The rainfall during winter wheat growing season is just about 1/3 of total demand. Xingtai has typical mountainous, hilly and plain agricultural zones, compound rain-fed and irrigated farming patterns. The winter wheat irrigation has heavily depended on overdraw of groundwater in recent decades. In the study, the MODIS （Moderate-Resolution Imaging Spectroradiometer） images taken at the key winter wheat growing season （Mar. to May） in normal rainfall year （2006） were selected, extracted NDVI （Normalized Difference Vegetation Index） and LST （Land Surface Temperature） data, calculated TVDI （Temperature and Vegetation Drought Index）, classified and mapped winter wheat drought intensity. Further, based on TVDI, a CDRA （Comprehensive Drought Risk Assessment） model for winter wheat drought disaster risk assessment was constructed and zoning was made. Verified by winter wheat yield, the risk zoning by CDRA is consistent with actual crop failure space. This method can be used in drought risk management.

Spatial Distribution of Soil Moisture Content and Tree Volume Estimation in International Institute of Tropical Agriculture Forest, Ibadan, Nigeria

The role of soil moisture in the survival and growth of trees cannot be over-emphasized and it contributes to the net productivity of the forest. However, information on the spatial distribution of the soil moisture content regarding the tree volume in forest ecosystems especially in Nigeria is limited. Therefore, this study combined spatial and ground data to determine soil moisture distribution and tree volume in the International Institute of Tropical Agriculture (IITA) forest, Ibadan. Satellite images of 1989, 1999, 2009 and 2019 were obtained and processed using topographic and vegetation-based models to examine the soil moisture status of the forest. Satellite-based soil moisture obtained was validated with ground soil moisture data collected in 2019. Tree growth variables were obtained for tree volume computation using Newton’s formular. Forest soil moisture models employed in this study include Topographic Wetness Index (TWI), Temperature Dryness Vegetation Index (TDVI) and Modified Normalized Difference Wetness Index (MNDWI). Relationships between index-based and ground base Soil Moisture Content (SMC), as well as the correlation between soil moisture and tree volume, were examined. The study revealed strong relationships between tree volume and TDVI, SMC, TWI with R2 values of 0.91, 0.85, and 0.75, respectively. The regression values of 0.89 between in-situ soil data and TWI and 0.83 with TDVI ascertain the reliability of satellite data in soil moisture mapping. The decision of which index to apply between TWI and TDVI, therefore, depends on available data since both proved to be reliable. The TWI surface is considered to be a more suitable soil moisture prediction index, while MNDWI exhibited a weak relationship (R2 = 0.03) with ground data. The strong relationships between soil moisture and tree volume suggest tree volume can be predicted based on available soil moisture content. Any slight undesirable change in soil moisture could lead to severe forest conditions.