Responses of seasonal indicators to extreme droughts in southwest China

Significant impact of extreme droughts on human society and ecosystem has occurred in many places of the world, for example, Southwest China (SWC). Considerable research concentrated on analyzing causes and effects of droughts in SWC, but few studies have examined seasonal indicators, such as variations of surface water and vegetation phenology. With the ongoing satellite missions, more and more earth observation data become available to environmental studies. Exploring the responses of seasonal indicators from satellite data to drought is helpful for the future drought forecast and management. This study analyzed the seasonal responses of surface water and vegetation phenology to drought in SWC using the multi-source data including Seasonal Water Area (SWA), Permanent Water Area (PWA), Start of Season (SOS), End of Season (EOS), Length of Season (LOS), precipitation, temperature, solar radiation, evapotranspiration, the Palmer Drought Severity Index (PDSI), the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), Gross Primary Productivity (GPP) and data from water conservancy construction. The results showed that SWA and LOS effectively revealed the development and recovery of droughts. There were two obvious drought periods from 2000 to 2017. In the first period (from August 2003 to June 2007), SWA decreased by 11.81% and LOS shortened by 5 days. They reduced by 21.04% and 9 days respectively in the second period (from September 2009 to June 2014), which indicated that there are more severe droughts in the second period. The SOS during two drought periods delayed by 3~6 days in spring, while the EOS advanced 1~3 days in autumn. All of PDSI, SWA and LOS could reflect the period of droughts in SWC, but the LOS and PDSI were very sensitive to the meteorological events, such as precipitation and temperature, while the SWA performed a more stable reaction to drought and could be a good indicator for the drought periodicity. This made it possible for using SWA in drought forecast because of the strong correlation between SWA and drought. Our results improved the understanding of seasonal responses to extreme droughts in SWC, which will be helpful to the drought monitoring and mitigation for different seasons in this ecologically fragile region

Temperature-Vegetation-soil Moisture-Precipitation Drought Index (TVMPDI); 21-year drought monitoring in Iran using satellite imagery within Google Earth Engine

Remote Sensing (RS) offers efficient tools for drought monitoring, especially in countries with a lack of reliable and consistent in-situ multi-temporal datasets. In this study, a novel RS- based Drought Index (RSDI) named Temperature-Vegetation-soil Moisture-Precipitation Drought Index (TVMPDI) was proposed. To the best of our knowledge, TVMPDI is the first RSDI using four different drought indicators in its formulation. TVMPDI was then validated and compared with six conventional RSDIs including VCI, TCI, VHI, TVDI, MPDI and TVMDI. To this end, precipitation and soil temperature in-situ data have been used. Different time scales of meteorological Standardized Precipitation Index (SPI) index have also been used for the validation 2 of the RSDIs. TVMPDI was highly correlated with the monthly precipitation and soil temperature in-situ data at 0.76 and 0.81 values respectively. The correlation coefficients between the RSDIs and 3-month SPI ranged from 0.07 to 0.28, identifying the TVMPDI as the most suitable index for subsequent analyses. Since the proposed TVMPDI could considerably outperform the other selected RSDIs, all spatiotemporal drought monitoring analyses in Iran were conducted by TVMPDI over the past 21 years. In this study, different products of the Moderate Resolution Imaging Spectrometer (MODIS), Tropical Rainfall Measuring Mission (TRMM), and Global Precipitation Measurement (GPM) datasets containing 15206 images were used on the Google Earth Engine (GEE) cloud computing platform. According to the results, Iran experienced the most severe drought in 2000 with a 0.715 TVMPDI value lasting for almost two years. Conversely, the TVMPDI showed a minimum value equal to 0.6781 in 2019 as the lowest annual drought level. The drought severity and trend in the 31 provinces of Iran have also been mapped. Consequently, various levels of decrease over the 21 years were found for different provinces, while Isfahan and Gilan were the only provinces showing an ascending drought trend (with a 0.004% and 0.002% trendline slope respectively). Khuzestan also faced a worrying drought prevalence that occurred in several years. In summary, this study provides updated information about drought trends in Iran using an advanced and efficient RSDI implemented in the cloud computing GEE platform. These results are beneficial for decision-makers and officials responsible for environmental sustainability, agriculture and the effects of climate change.Peer ReviewedPostprint (author's final draft

Google Earth Engine cloud computing platform for remote sensing big data applications: a comprehensive review

Remote sensing (RS) systems have been collecting massive volumes of datasets for decades, managing and analyzing of which are not practical using common software packages and desktop computing resources. In this regard, Google has developed a cloud computing platform, called Google Earth Engine (GEE), to effectively address the challenges of big data analysis. In particular, this platformfacilitates processing big geo data over large areas and monitoring the environment for long periods of time. Although this platformwas launched in 2010 and has proved its high potential for different applications, it has not been fully investigated and utilized for RS applications until recent years. Therefore, this study aims to comprehensively explore different aspects of the GEE platform, including its datasets, functions, advantages/limitations, and various applications. For this purpose, 450 journal articles published in 150 journals between January 2010 andMay 2020 were studied. It was observed that Landsat and Sentinel datasets were extensively utilized by GEE users. Moreover, supervised machine learning algorithms, such as Random Forest, were more widely applied to image classification tasks. GEE has also been employed in a broad range of applications, such as Land Cover/land Use classification, hydrology, urban planning, natural disaster, climate analyses, and image processing. It was generally observed that the number of GEE publications have significantly increased during the past few years, and it is expected that GEE will be utilized by more users from different fields to resolve their big data processing challenges.Peer ReviewedPostprint (published version

Ekstraksi Perubahan Tutupan Vegetasi Di Kabupaten Gorontalo Menggunakan Google Earth Engine

Monitoring changes in vegetation cover is important for the restoration of ecosystems in the Gorontalo Regency area. The utilization of remote sensing technology makes it possible to detect the dynamics of changes in vegetation cover spatially and temporally. The Terra MODIS satellite image collection in the study area is available in large numbers and sizes. Therefore, cloud computing-based spatial technology support is needed. Google Earth Engine (GEE) as a geospatial computing device is an alternative to cover this shortfall. The aim of this study is to explore the condition of vegetation cover spatially and temporally using the GEE platform. A total of 43 MODIS images in the study area, recording periods 2000 and 2020, were used to quickly and effectively generate vegetation cover maps. The process of downloading, processing, and analyzing data was automated through the GEE interface. The results of the mapping in 2000 and 2020 are shown by maps of vegetation cover in two classes, namely; vegetation and non-vegetation. The accuracy of the vegetation cover map shows good results, namely an overall accuracy of 0.81 for 2000 and 0.85 for 2020. The area of the non-vegetation class increased by 2815.29 ha, and the vegetation class decreased by 2767.31 ha. The map of spatial changes in vegetation cover in the study area is classified into three classes, namely revegetation, devegetation, and unchanged. Based on these results, the extraction of vegetation cover changes in the study area using the GEE platform can be carried out well

A review of satellite-based global agricultural monitoring systems available for Africa

Abstract The increasing frequency and severity of extreme climatic events and their impacts are being realized in many regions of the world, particularly in smallholder crop and livestock production systems in Sub-Saharan Africa (SSA). These events underscore the need for timely early warning. Satellite Earth Observation (EO) availability, rapid developments in methodology to archive and process them through cloud services and advanced computational capabilities, continue to generate new opportunities for providing accurate, reliable, and timely information for decision-makers across multiple cropping systems and for resource-constrained institutions. Today, systems and tools that leverage these developments to provide open access actionable early warning information exist. Some have already been employed by early adopters and are currently operational in selecting national monitoring programs in Angola, Kenya, Rwanda, Tanzania, and Uganda. Despite these capabilities, many governments in SSA still rely on traditional crop monitoring systems, which mainly rely on sparse and long latency in situ reports with little to no integration of EO-derived crop conditions and yield models. This study reviews open-access operational agricultural monitoring systems available for Africa. These systems provide the best-available open-access EO data that countries can readily take advantage of, adapt, adopt, and leverage to augment national systems and make significant leaps (timeliness, spatial coverage and accuracy) of their monitoring programs. Data accessible (vegetation indices, crop masks) in these systems are described showing typical outputs. Examples are provided including crop conditions maps, and damage assessments and how these have integrated into reporting and decision-making. The discussion compares and contrasts the types of data, assessments and products can expect from using these systems. This paper is intended for individuals and organizations seeking to access and use EO to assess crop conditions who might not have the technical skill or computing facilities to process raw data into informational products

Use and Improvement of Remote Sensing and Geospatial Technologies in Support of Crop Area and Yield Estimations in the West African Sahel

In arid and semi-arid West Africa, agricultural production and regional food security depend largely on small-scale subsistence farming and rainfed crops, both of which are vulnerable to climate variability and drought. Efforts made to improve crop monitoring and our ability to estimate crop production (areas planted and yield estimations by crop type) in the major agricultural zones of the region are critical paths for minimizing climate risks and to support food security planning. The main objective of this dissertation research was to contribute to these efforts using remote sensing technologies. In this regard, the first analysis documented the low reliability of existing land cover products for cropland area estimation (Chapter 2). Then two satellite remote sensing-based datasets were developed that 1) accurately map cropland areas in the five countries of Sahelian West Africa (Senegal, Mauritania, Mali, Burkina Faso and Niger; Chapter 3), and 2) focus on the country of Mali to identify the location and prevalence of the major subsistence crops (millet, sorghum, maize and non-irrigated rice; Chapter 4). The regional cropland area product is distributed as the West African Sahel Cropland area at 30 m (WASC30). The development of the new dataset involved high density training data (380,000 samples) developed by USGS in collaboration with CILSS for training about 200 locally optimized random forest (RF) classifiers using Landsat 8 surface reflectances and vegetation indices and the Google Earth Engine platform. WASC30 greatly improves earlier estimates through inclusion of cropland information for both rainfed and irrigated areas mapped with a class-specific accuracy of 79% across the West Africa Sahel. Used as a mask in crop monitoring systems, the new cropland area data could bring critical insights by reducing uncertainties in xv identification of croplands as crop growth condition metrics are extracted. WASC30 allowed us to derive detailed statistics on cultivated areas in the Sahel, at country and agroclimatic scales. Intensive agricultural zones were highlighted as well. The second dataset, mapping crop types for the country of Mali, is meant to separate signals of different crop types for improved crop yield estimation. The crop type map was used to derive detailed agricultural statistics (e.g. acreage by crop types, spatial distribution) at finer administrative scales than has previously been possible. The crop fraction information by crop type extracted from the map, gives additional details on farmers preferences by regions, and the natural adaptability of different crop types. The final analysis of this dissertation explores the use of ensemble machine learning techniques to predict maize yield in Mali (Chapter 5). Climate data (precipitation and temperature), and vegetation indices (Normalized Difference Vegetation Index, NDVI, the Enhanced Vegetation Index, EVI, and the Normalized Difference Water Index, NDWI) are used as predictors, while actual yields collected in 2017 by the Malian Ministry of Agriculture are the reference data. Random forest presented better predictive performance as compared to boosted regression trees (BRT). Results showed that climate variables have more predictive power for maize yield compared to vegetation indices. Among vegetation indices, the NDWI appeared to be the most influential predictor, maybe because of water requirement of maize and the sensitivity of this index to water in semi-arid regions. Tested with two different independent datasets, one constituted by 20% of the reference information, and another including observed yields for year 2018 (a one-year-left analysis), maize yield predictions were promising for year 2017 (RMSE = 362 kg/ha), but showed higher error for 2018 (RMSE = 707 kg/ha). That is, the fitted model may not capture accurately year to year variabilities in predicted maize yield. In this analysis, predictions were limited to field samples (~600 fields) across the country of Mali. It would be valuable in the future to predict maize yield for each pixel of the new developed crop type map. That will lead to a detailed spatial analysis of maize yield, allowing identification of low yielding regions for targeted interventions which could improve food security. Keywords: Agricultural identification of croplands as crop growth condition metrics are extracted. WASC30 allowed us to derive detailed statistics on cultivated areas in the Sahel, at country and agroclimatic scales. Intensive agricultural zones were highlighted as well. The second dataset, mapping crop types for the country of Mali, is meant to separate signals of different crop types for improved crop yield estimation. The crop type map was used to derive detailed agricultural statistics (e.g. acreage by crop types, spatial distribution) at finer administrative scales than has previously been possible. The crop fraction information by crop type extracted from the map, gives additional details on farmers preferences by regions, and the natural adaptability of different crop types. The final analysis of this dissertation explores the use of ensemble machine learning techniques to predict maize yield in Mali (Chapter 5). Climate data (precipitation and temperature), and vegetation indices (Normalized Difference Vegetation Index, NDVI, the Enhanced Vegetation Index, EVI, and the Normalized Difference Water Index, NDWI) are used as predictors, while actual yields collected in 2017 by the Malian Ministry of Agriculture are the reference data. Random forest presented better predictive performance as compared to boosted regression trees (BRT). Results showed that climate variables have more predictive power for maize yield compared to vegetation indices. Among vegetation indices, the NDWI appeared to be the most influential predictor, maybe because of water requirement of maize and the sensitivity of this index to water in semi-arid regions. Tested with two different independent datasets, one constituted by 20% of the reference information, and another including observed yields for year 2018 (a one-year-left analysis), maize yield predictions were promising for year 2017 (RMSE = 362 kg/ha), but showed higher error for 2018 (RMSE = 707 kg/ha). That is, the fitted model may not capture accurately year to year variabilities in predicted maize yield. In this analysis, predictions were limited to field samples (~600 fields) across the country of Mali. It would be valuable in the future to predict maize yield for each pixel of the new developed crop type map. That will lead to a detailed spatial analysis of maize yield, allowing identification of low yielding regions for targeted interventions which could improve food security

Earth Observations and Integrative Models in Support of Food and Water Security

Global food production depends upon many factors that Earth observing satellites routinely measure about water, energy, weather, and ecosystems. Increasingly sophisticated, publicly-available satellite data products can improve efficiencies in resource management and provide earlier indication of environmental disruption. Satellite remote sensing provides a consistent, long-term record that can be used effectively to detect large-scale features over time, such as a developing drought. Accuracy and capabilities have increased along with the range of Earth observations and derived products that can support food security decisions with actionable information. This paper highlights major capabilities facilitated by satellite observations and physical models that have been developed and validated using remotely-sensed observations. Although we primarily focus on variables relevant to agriculture, we also include a brief description of the growing use of Earth observations in support of aquaculture and fisheries

Novel Satellite-Based Methodologies for Multi-Sensor and Multi-Scale Environmental Monitoring to Preserve Natural Capital

Global warming, as the biggest manifestation of climate change, has changed the distribution of water in the hydrological cycle by increasing the evapotranspiration rate resulting in anthropogenic and natural hazards adversely affecting modern and past human properties and heritage in different parts of the world. The comprehension of environmental issues is critical for ensuring our existence on Earth and environmental sustainability. Environmental modeling can be described as a simplified form of a real system that enhances our knowledge of how a system operates. Such models represent the functioning of various processes of the environment, such as processes related to the atmosphere, hydrology, land surface, and vegetation. The environmental models can be applied on a wide range of spatiotemporal scales (i.e. from local to global and from daily to decadal levels); and they can employ various types of models (e.g. process-driven, empirical or data-driven, deterministic, stochastic, etc.). Satellite remote sensing and Earth Observation techniques can be utilized as a powerful tool for flood mapping and monitoring. By increasing the number of satellites orbiting around the Earth, the spatial and temporal coverage of environmental phenomenon on the planet has in-creased. However, handling such a massive amount of data was a challenge for researchers in terms of data curation and pre-processing as well as required computational power. The advent of cloud computing platforms has eliminated such steps and created a great opportunity for rapid response to environmental crises. The purpose of this study was to gather state-of-the-art remote sensing and/or earth observation techniques and to further the knowledge concerned with any aspect of the use of remote sensing and/or big data in the field of geospatial analysis. In order to achieve the goals of this study, some of the water-related climate-change phenomena were studied via different mathematical, statistical, geomorphological and physical models using different satellite and in-situ data on different centralized and decentralized computational platforms. The structure of this study was divided into three chapters with their own materials, methodologies and results including: (1) flood monitoring; (2) soil water balance modeling; and (3) vegetation monitoring. The results of this part of the study can be summarize in: 1) presenting innovative procedures for fast and semi-automatic flood mapping and monitoring based on geomorphic methods, change detection techniques and remote sensing data; 2) modeling soil moisture and water balance components in the root zone layer using in-situ, drone and satellite data; incorporating downscaling techniques; 3) combining statistical methods with the remote sensing data for detecting inner anomalies in the vegetation covers such as pest emergence; 4) stablishing and disseminating the use of cloud computation platforms such as Google Earth Engine in order to eliminate the unnecessary steps for data curation and pre-processing as well as required computational power to handle the massive amount of RS data. As a conclusion, this study resulted in provision of useful information and methodologies for setting up strategies to mitigate damage and support the preservation of areas and landscape rich in cultural and natural heritage

Spatio-Temporal Analysis of Vegetation Dynamics as a Response to Climate Variability and Drought Patterns in the Semiarid Region, Eritrea

There is a growing concern over change in vegetation dynamics and drought patterns with the increasing climate variability and warming trends in Africa, particularly in the semiarid regions of East Africa. Here, several geospatial techniques and datasets were used to analyze the spatio-temporal vegetation dynamics in response to climate (precipitation and temperature) and drought in Eritrea from 2000 to 2017. A pixel-based trend analysis was performed, and a Pearson correlation coefficient was computed between vegetation indices and climate variables. In addition, vegetation condition index (VCI) and standard precipitation index (SPI) classifications were used to assess drought patterns in the country. The results demonstrated that there was a decreasing NDVI (Normalized Difference Vegetation Index) slope at both annual and seasonal time scales. In the study area, 57.1% of the pixels showed a decreasing annual NDVI trend, while the significance was higher in South-Western Eritrea. In most of the agro-ecological zones, the shrublands and croplands showed decreasing NDVI trends. About 87.16% of the study area had a positive correlation between growing season NDVI and precipitation (39.34%, p < 0.05). The Gash Barka region of the country showed the strongest and most significant correlations between NDVI and precipitation values. The specific drought assessments based on VCI and SPI summarized that Eritrea had been exposed to recurrent droughts of moderate to extreme conditions during the last 18 years. Based on the correlation analysis and drought patterns, this study confirms that low precipitation was mainly attributed to the slowly declining vegetation trends and increased drought conditions in the semi-arid region. Therefore, immediate action is needed to minimize the negative impact of climate variability and increasing aridity in vegetation and ecosystem services

Spatio-Temporal Analysis of Vegetation Dynamics as a Response to Climate Variability and Drought Patterns in the Semiarid Region, Eritrea

There is a growing concern over change in vegetation dynamics and drought patterns with the increasing climate variability and warming trends in Africa, particularly in the semiarid regions of East Africa. Here, several geospatial techniques and datasets were used to analyze the spatio-temporal vegetation dynamics in response to climate (precipitation and temperature) and drought in Eritrea from 2000 to 2017. A pixel-based trend analysis was performed, and a Pearson correlation coefficient was computed between vegetation indices and climate variables. In addition, vegetation condition index (VCI) and standard precipitation index (SPI) classifications were used to assess drought patterns in the country. The results demonstrated that there was a decreasing NDVI (Normalized Difference Vegetation Index) slope at both annual and seasonal time scales. In the study area, 57.1% of the pixels showed a decreasing annual NDVI trend, while the significance was higher in South-Western Eritrea. In most of the agro-ecological zones, the shrublands and croplands showed decreasing NDVI trends. About 87.16% of the study area had a positive correlation between growing season NDVI and precipitation (39.34%, p < 0.05). The Gash Barka region of the country showed the strongest and most significant correlations between NDVI and precipitation values. The specific drought assessments based on VCI and SPI summarized that Eritrea had been exposed to recurrent droughts of moderate to extreme conditions during the last 18 years. Based on the correlation analysis and drought patterns, this study confirms that low precipitation was mainly attributed to the slowly declining vegetation trends and increased drought conditions in the semi-arid region. Therefore, immediate action is needed to minimize the negative impact of climate variability and increasing aridity in vegetation and ecosystem services