Remote Sensing Techniques for Surface Water Detection

In water resource management, environmental monitoring, and disaster response, surface water detection utilizing remote sensing is a crucial task as the measurement techniques provide important insights into their spatial distribution and dynamics. This thesis begins with a study of estimating hyperspectral measurements from multispectral acquisitions, which could in turn be used to differentiate between multiple land surface types including surface water. Through false color composites and examples of spectrum from land surfaces, spectral super-resolution using dictionary learning is demonstrated. With an Inverse Relative Deviation (IRD) of about 32dB spectral upsampling is performed on the study area used for training. Upon testing on a separate study area, the algorithm explored in the thesis is able to do so with an IRD of 20dB. The study also shows the distinct features in a reflected radiance spectrum that could help identify inland water bodies. Since these measurements have a very low repeat cycle, the dynamics of inland water bodies cannot be studied effectively. In this thesis, therefore, the use of GNSS-R measurements along with optical remote sensing data are discussed to detect surface water bodies and identify windows of continuous samples over water. GNSS as a signal of opportunity is discussed. These measurements along with optical indicator NDVI are used to segregate reflections of GNSS tracks over water. With a true positive rate of approximately 70%, reflections from water bodies and land can be segregated. It is observed that carrier phase measurements alone cannot be used for segregation of reflections over water as they are intermittently straddling over land and water. Thus a window of coherency detection will have reflections over both land and water, thereby necessitating the use of GNSS-R along with optical measurements. After classification, windows with consecutive reflections over water are extracted and their distribution is determined for over a month, where more than 50% of the windows had a circular length of 0.97, showing highly coherent reflections.</p

Jekyll: Attacking Medical Image Diagnostics using Deep Generative Models

Advances in deep neural networks (DNNs) have shown tremendous promise in the medical domain. However, the deep learning tools that are helping the domain, can also be used against it. Given the prevalence of fraud in the healthcare domain, it is important to consider the adversarial use of DNNs in manipulating sensitive data that is crucial to patient healthcare. In this work, we present the design and implementation of a DNN-based image translation attack on biomedical imagery. More specifically, we propose Jekyll, a neural style transfer framework that takes as input a biomedical image of a patient and translates it to a new image that indicates an attacker-chosen disease condition. The potential for fraudulent claims based on such generated 'fake' medical images is significant, and we demonstrate successful attacks on both X-rays and retinal fundus image modalities. We show that these attacks manage to mislead both medical professionals and algorithmic detection schemes. Lastly, we also investigate defensive measures based on machine learning to detect images generated by Jekyll.Comment: Published in proceedings of the 5th European Symposium on Security and Privacy (EuroS&P '20

Enhancing livelihoods in farming communities through super-resolution agromet advisories using advanced digital agriculture technologies

Agricultural production in India is highly vulnerable to climate change. Transformational change to farming systems is required to cope with this changing climate to maintain food security, and ensure farming to remain economically viable. The south Asian rice-fallow systems occupying 22.3 million ha with about 88% in India, mostly (82%) concentrated in the eastern states, are under threat. These systems currently provide economic and food security for about 11 million people, but only achieve 50% of their yield potential. Improvement in productivity is possible through efficient utilization of these fallow lands. The relatively low production occurs because of sub-optimal water and nutrient management strategies. Historically, the Agro-met advisory service has assisted farmers and disseminated information at a district-level for all the states. In some instances, Agro-met delivers advice at the block level also, but in general, farmers use to follow the district level advice and develop an appropriate management plan like land preparation, sowing, irrigation timing, harvesting etc. The advisories are generated through the District Agrometeorology Unit (DAMU) and Krishi Vigyan Kendra (KVK) network, that consider medium-range weather forecast. Unfortunately, these forecasts advisories are general and broad in nature for a given district and do not scale down to the individual field or farm. Farmers must make complex crop management decisions with limited or generalised information. The lack of fine scale information creates uncertainty for farmers, who then develop risk-averse management strategies that reduce productivity. It is unrealistic to expect the Agro-met advisory service to deliver bespoke information to every farmer and to every field simply with the help of Kilometre-scale weather forecast. New technologies must be embraced to address the emerging crises in food security and economic prosperity. Despite these problems, Agro-met has been successful. New digital technologies have emerged though, and these digital technologies should become part of the Agro-met arsenal to deliver valuable information directly to the farmers at the field scale. The Agro-met service is poised to embrace and deliver new interventions through technology cross-sections such as satellite remote sensing, drone-based survey, mobile based data collection systems, IoT based sensors, using insights derived from a hybridisation of crop and AIML (Artificial Intelligence and Machine Learning) models. These technological advancements will generate fine-scale static and dynamic Agro-met information on cultivated lands, that can be delivered through Application Programming Interface (APIs) and farmers facing applications. We believe investment in this technology, that delivers information directly to the farmers, can reverse the yield gap, and address the negative impacts of a changing climate

A coupled ground heat flux-surface energy balance model of evaporation using thermal remote sensing observations

One of the major undetermined problems in evaporation (ET) retrieval using thermal infrared remote sensing is the lack of a physically based ground heat flux (G) model and its integration within the surface energy balance (SEB) equation. Here, we present a novel approach based on coupling a thermal inertia (TI)-based mechanistic G model with an analytical surface energy balance model, Surface Temperature Initiated Closure (STIC, version STIC1.2). The coupled model is named STIC-TI. The model is driven by noon–night (13:30 and 01:30 local time) land surface temperature, surface albedo, and a vegetation index from MODIS Aqua in conjunction with a clear-sky net radiation sub-model and ancillary meteorological information. SEB flux estimates from STIC-TI were evaluated with respect to the in situ fluxes from eddy covariance measurements in diverse ecosystems of contrasting aridity in both the Northern Hemisphere and Southern Hemisphere. Sensitivity analysis revealed substantial sensitivity of STIC-TI-derived fluxes due to the land surface temperature uncertainty. An evaluation of noontime G (Gi) estimates showed 12 %–21 % error across six flux tower sites, and a comparison between STIC-TI versus empirical G models also revealed the substantially better performance of the former. While the instantaneous noontime net radiation (RNi) and latent heat flux (LEi) were overestimated (15 % and 25 %), sensible heat flux (Hi) was underestimated (22 %). Overestimation (underestimation) of LEi (Hi) was associated with the overestimation of net available energy (RNi−Gi) and use of unclosed surface energy balance flux measurements in LEi (Hi) validation. The mean percent deviations in Gi and Hi estimates were found to be strongly correlated with satellite day–night view angle difference in parabolic and linear pattern, and a relatively weak correlation was found between day–night view angle difference versus LEi deviation. Findings from this parameter-sparse coupled G–ET model can make a valuable contribution to mapping and monitoring the spatiotemporal variability of ecosystem water stress and evaporation using noon–night thermal infrared observations from future Earth observation satellite missions such as TRISHNA, LSTM, and SBG

A baseline estimate of regional agricultural water demand from GEO-LEO satellite observations

Agricultural water demand (AWD) and irrigation water demand (IWD) were assessed (2009–2018) over India using geostationary and polar orbiting satellites. A novel concept of satellite based composite crop-coefficient was introduced to address bulk AWD from mixed agricultural landscape. Significant spatio-temporal variation of AWD was observed over India. The decadal mean of annual AWD was found to be 1521 km3 contributing around 52% (789 km3) and 48% (732 km3) in kharif and rabi seasons, respectively. The decadal average IWD over India was found to be 360 km3. At annual scale, around 75% of AWD was found fulfilled by effective rainfall and the rest 25% is the IWD. The decadal trend of AWD and IWD showed significant increasing trend over Indian region. The study provides a baseline reference for regional agricultural water management policy over diverse agro-climatic regions of India with an opportunity to optimize AWD and IWD at different locations

Satellite agromet products and their adaptation for advisory services to Indian farming community

Anomalous and erratic behaviour of weather pose various challenges for agricultural community from crop sowing to post harvest. The balance between turn-around-time for farm operations and resource optimization can limit the expected losses due to unfavourable weather. In the past, thrust was given to issue agromet advisories to farmers for a group of districts and blocks primarily using medium-range weather forecast with coarser grid resolution, crop records and point observation for crop condition. The current advisory framework under Gramin Krishi Mausam Seva (GKMS) of India Meteorological Department (IMD) lacks in, near real time assessment of crop and soil conditions to improve the quality and coverage of advisories. The spectral observations from polar and geostationary satellites provide agromet products for synoptic, real-time and continuous monitoring of crops. In order to strengthen the existing advisories under GKMS, the usage of satellite derived daily agromet products in six AFMUs (Agro-Met Field Units) (382 blocks of 60 districts) was initiated by Space Applications Centre, ISRO and IMD. Several agromet products such as Normalized Difference Vegetation Index (NDVI), Potential Evapotranspiration (PET), Surface Dryness Index (SDI), Minimum and Maximum Land Surface Temperature (LST) and Surface Soil Moisture (SSM) aggregated for block and district agricultural regions are provided to all six AFMUs in user friendly format since October 2019 through a dedicated web link from VEDAS (https://vedas.sac.gov.in) geoportal. Time series and near real-time agromet products during agricultural seasons are being used to interpret crop sowing prospect, crop condition, irrigation requirement, crop stress etc. at block and district scales. Regular evaluation of these products over respective AFMUs with measured ground data showed 9% and 10% difference for PET and SSM respectively, whereas, LST showed RMSE of 2.0 K. In future, crop specific agromet parameters and their short-term forecasting are primary focus to provide value-added quality advisories at Gram Panchayat level to all AFMUs

Radar Vegetation Index for assessing cotton crop condition using RISAT-1 data

Periodic crop condition monitoring is of prime importance in cotton belt of western India for water stress management. In this article, vegetation water content (VWC) is assessed using Radar Vegetation Index (RVI) derived from the RISAT-1 data during July to September, vegetative to first picking phase, for utilizing its potential for large area cotton condition assessment. The RVI estimation from dual-polarized data has been demonstrated for regional applications. Prediction models of VWC for cotton crop using RVI and in situ ground measurements depicts significant relationship, with R2 varying from 0.5 to 0.6 and RMSE of 0.3–0.7 kg m−2. High correlation exists between RVI with crop age and crop biomass with R2 varying from 0.55 to 0.7, this proves useful for sowing date prediction. The results showed good validation (R2 = 0.8) for operational applications. The estimated VWC was found with 30–35% error above 4 kg m−2 biomasses as compared to 20–25% in lower ranges

Spatial Disaggregation of Latent Heat Flux Using Contextual Models over India

Estimation of latent heat flux at the agricultural field scale is required for proper water management. The current generation thermal sensors except Landsat-8 provide data on the order of 1000 m. The aim of this study is to test three approaches based on contextual models using only remote sensing datasets for the disaggregation of latent heat flux over India. The first two approaches are, respectively, based on the estimation of the evaporative fraction (EF) and solar radiation ratio at coarser resolution and disaggregating them to yield the latent heat flux at a finer resolution. The third approach is based on disaggregation of the thermal data and estimating a finer resolution latent heat flux. The three approaches were tested using MODIS datasets and the validation was done using the Bowen Ratio energy balance observations at five sites across India. From the validation, it was observed that the first two approaches performed similarly and better than the third approach at all five sites. The third approach, based on the disaggregation of the thermal data, yielded larger errors. In addition to better performance, the second approach based on the disaggregation of solar radiation ratio was simpler and required lesser data processing than the other approaches. In addition, the first two approaches captured the spatial pattern of latent heat flux without introducing any artefacts in the final output

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