Application of machine learning techniques to derive sea water turbidity from Sentinel-2 imagery

Earth Observation (EO) from satellites has the potential to provide comprehensive, rapid and inexpensive information about water bodies, integrating in situ measurements. Traditional methods to retrieve optically active water quality parameters from satellite data are based on semiempirical models relying on few bands, which often revealed to be site and season specific. The use of machine learning (ML) for remotely sensed water quality estimation has spread in recent years thanks to the advances in algorithm development and computing power. These models allow to exploit the wealth of spectral information through more flexible relationships and are less affected by atmospheric and other background factors. The present study explores the use of Sentinel-2 MultiSpectral Instrument (MSI) Level-1C Top of Atmosphere spectral radiance to derive water turbidity, through application of machine learning techniques. A dataset of 222 combination of turbidity measurements, collected in the North Tyrrhenian Sea – Italy from 2015 to 2021, and values of the 13 spectral bands in the pixel corresponding to the sample location was used. Two regression techniques were tested and compared: a Stepwise Linear Regression (SLR) and a Polynomial Kernel Regression. The two models show accurate and similar performance (R2 = 0.736, RMSE = 2.03 NTU, MAE = 1.39 NTU for the SLR and R2 = 0.725, RMSE = 2.07 NTU, MAE = 1.40 NTU for the Kernel). A band importance analysis revealed the contribution of the different spectral bands and the main role of the red-edge range. The work shows that it is possible to reach a good accuracy in turbidity estimation from MSI TOA reflectance using ML models, fed by the whole spectrum of available bands, although the possible generation of errors related to atmospheric effect in turbidity estimates was not evaluated. Comparison between turbidity estimates obtained from the models with turbidity data from Copernicus CMEMS dataset named ‘Mediterranean Sea, Bio-Geo-Chemical, L3, daily observation’ produced consistent results. Finally, turbidity maps from satellite imagery were produced for the study area, showing the ability of the models to catch extreme events

고해상도 초분광영상을 활용한 하천 부유사농도 계측기법 개발

학위논문(박사) -- 서울대학교대학원 : 공과대학 건설환경공학부, 2022. 8. 서일원.기존의 하천 부유사 농도 계측은 샘플링 기반 직접계측 방식에 의존하여 시공간적 고해상도 자료 취득이 어려운 실정이다. 이러한 한계점을 극복하기 위해 최근 위성과 드론을 활용하여 촬영된 다분광 혹은 초분광 영상을 통해 고해상도의 부유사농도 시공간분포를 계측하는 기법에 대한 연구가 활발히 진행되고 있다. 하지만, 다른 하천 물리량 계측에 비해 부유사 계측 연구는 하천에 따라 부유사가 다양하게 분포하고 다른 부유물질 혹은 하상에 의한 바닥 반사의 영향 때문에 분광 자료를 통해 정확한 부유사농도 분포를 재현하기 어려운 실정이다. 특히, 부유사 분광 특성에 영향을 미치는 입도분포, 광물특성, 침강성 등이 하천에 따라 강한 지역성을 나타내기에 이러한 요인에서 야기되는 분광다양성으로 인해 특정 시기와 지역에만 적합한 원격탐사 기반 계측 모형들이 개발되어 왔다. 본 연구에서는 이러한 분광다양성을 반영하여 다양한 하천 및 유사 조건에서 적용 가능한 고해상도 초분광영상을 활용한 하천 부유사농도 계측방법을 개발하기 위해 초분광 군집화 기법과 다양한 파장대의 분광 밴드를 학습할 수 있는 기계학습 회귀 모형을 결합하여 CMR-OV라는 방법론을 제시하였다. CMR-OV 개발 및 검증은 1) 실험적 연구를 통한 하천 부유사 분광 특성의 주요 교란 요인 분석, 2) 최적 회귀모형 선정 및 초분광 클러스터링과의 결합, 3) 현장적용성 평가의 과정을 거쳐 수행되었다. 실험적 연구에서는 우선 실내 실험실에서 횡방향 혼합기를 활용하여 바닥 반사를 제거하고 완전 혼합된 상태에서 부유사의 고유 초분광 스펙트럼 자료를 수집하였다. 이를 바탕으로 실제 하천과 유사한 조건의 실규모 옥외 수로 실험에서 다양한 유사 특성(입도 및 광물)과 하상 특성(식생 및 모래)에 대한 초분광 자료를 수집하여 고유 초분광 스펙트럼과 비교하였다. 그 결과, 부유사의 분광 특성은 유사의 종류 및 입도에 따라 농도 증가에 따른 초분광 스펙트럼의 반사율 변화가 상이하게 나타났다. 또한, 1 m 이하의 얕은 수심 조건에서는 바닥 반사의 영향으로 하상 종류에 따라 초분광 스펙트럼의 개형이 크게 변화하였으며, 고농도의 부유사가 분포할 때도 바닥 반사가 크게 영향을 미치는 것을 확인하였다. 이러한 분광다양성이 반영된 부유사농도와 초분광 자료의 관계를 구축하기 위하여 기계학습 기반 랜덤포레스트 회귀 모형과 가우시안 혼합 모형 기반 초분광 군집 기법을 결합한 CMR-OV를 적용한 결과, 기존 연구들에서 주로 활용된 밴드비 기반의 모형과 단일 기계학습모형에 비해 정확도가 크게 향상하였다. 특히, 기존 최적 밴드비 분석 (OBRA) 방법은 비선형성을 고려해도 좁은 영역의 파장대만을 고려하는 한계점으로 인해 분광다양성을 반영하지 못하는 것으로 밝혀졌다. 하지만, CMR-OV는 폭 넓은 파장대 영역을 고려함과 동시에 높은 정확도를 산출하였다. 최종적으로 CMR-OV를 황강의 직선구간 및 사행구간과 낙동강과 황강의 합류부에 적용하여 현장검증을 수행한 결과, 기존 모형 대비 정확도와 부유사 농도 맵핑의 정밀성에서 큰 개선이 있었으며, 비학습지역에서도 높은 정확도를 산출하였다. 특히, 하천 합류부에서는 초분광 군집을 통해 두 하천 흐름의 경계층을 명확히 구별하였으며, 이를 바탕으로 지류와 본류에 대해 각각 분리된 회귀모형을 구축하여 복잡한 합류부 근역 경계층에서의 부유사 분포를 보다 정확하게 재현하였다. 또한, 나아가서 재현된 고해상도의 부유사 공간분포를 바탕으로 혼합도를 산정한 결과, 기존 점계측 대비 상세하게 부유사 혼합에 대한 정량적 평가가 가능한 것으로 나타났다. 따라서, 본 연구에서 개발한 초분광영상 기반 부유사 계측 기술을 통해 추후 하천 조사 및 관리 실무의 정확성 및 효율성을 크게 증진할 수 있을 것으로 기대된다.The conventional measurement method of suspended sediment concentration (SSC) in the riverine system is labor-intensive and time-consuming since it has been conducted using the sampling-based direct measurement method. For this reason, it is challenging to collect high-resolution datasets of SSC in rivers. In order to overcome this limitation, remote sensing-based techniques using multi- or hyper-spectral images from satellites or UAVs have been recently carried out to obtain high-resolution SSC distributions in water environments. However, suspended sediment in rivers is more dynamic and spatially heterogeneous than those in other fields. Moreover, the sediment and streambed properties have strong regional characteristics depending on the river type; thus, only models suitable for a specific period and region have been developed owing to the increased spectral variability of the water arising from various types of suspended matter in the water and the heterogeneous streambed properties. Therefore, to overcome the limitations of the existing monitoring system, this study proposed a robust hyperspectral imagery-based SSC measurement method, termed cluster-based machine learning regression with optical variability (CMR-OV). This method dealt with the spectral variability problem by combining hyperspectral clustering and machine learning regression with the Gaussian mixture model (GMM) and Random forest (RF) regression. The hyperspectral clustering separated the complex dataset into several homogeneous datasets according to spectral characteristics. Then, the machine learning regressors corresponding to clustered datasets were built to construct the relationship between the hyperspectral spectrum and SSC. The development and validation of the proposed method were carried out through the following processes: 1) analysis of confounding factors in the spectral variability through experimental studies, 2) selection of an optimal regression model and validation of hyperspectral clustering, and 3) evaluation of field applicability. In the experimental studies, the intrinsic hyperspectral spectra of suspended sediment were collected in a completely mixed state after removing the bottom reflection using a horizontal rotating cylinder. Then, hyperspectral data on various sediment properties (particle size and mineral contents) and river bed properties (sand and vegetation) were collected from sediment tracer experiments in field-scale open channels under different hydraulic conditions and compared with intrinsic hyperspectral spectra. Consequently, the change of the hyperspectral spectrum was different according to the sediment type and particle size distribution. In addition, under the shallow water depth condition of 1 m or less, the shape of the hyperspectral spectrum changed significantly depending on the bed type due to the bottom reflectance. The bottom reflectance substantially affected the hyperspectral spectrum even when the high SSC was distributed. As a result of combining the GMM and RF regression with building a relationship between the SSC and hyperspectral data reflecting the spectral variability, the accuracy was substantially improved compared to the other methods. In particular, even when nonlinearity is considered based on the existing optimal band ratio analysis (OBRA) method, spectral variability could not be reflected due to the limitation of considering only a narrow wavelength range. On the other hand, CMR-OV showed high accuracy while considering a wide range of wavelengths with clusters having distinct spectral characteristics. Finally, the CMR-OV was applied to the straight and meandering reaches of the Hwang River and the confluence of the Nakdong and Hwang Rivers in South Korea to assess field applicability. There was a remarkable improvement in the accuracy and precision of SSC mapping under various river conditions compared to the existing models, and CMR-OV showed robust performance even with non-calibrated datasets. At the river confluence, the mixing pattern between the main river and tributary was apparently retrieved from CMR-OV under optically complex conditions. Compared to the non-clustered model, hyperspectral clustering played a primary role in improving the performance by separating the water bodies originating from both rivers. It was also possible to quantitatively evaluate the complicated mixing pattern in detail compared to the existing point measurement. Therefore, it is expected that the accuracy and efficiency of river investigation will be significantly improved through the SSC measurement method presented in this study.Abstract of dissertation i List of figures ix List of tables xvii List of abbreviations xix List of symbols xxii 1. Introduction 1 2. Theoretical research 13 2.1.1 Pre-processing of hyperspectral image (HSI) 19 2.1.2 Optical characteristics of suspended sediment in rivers 28 2.1.2.1 Theory of solar radiation transfer in rivers 28 2.1.2.2 Heterogeneity of sediment properties 33 2.1.2.3 Effects of bottom reflectance 38 2.1.2.4 Vertical distribution of suspended sediment 41 2.1.3 Retrieval of suspended sediment from remote sensing data 46 2.1.3.1 Remote sensing-based regression approach 46 2.1.3.2 Clustering of remote sensing data 52 2.2 Mapping of suspended sediment concentration in rivers 56 2.2.1 Traditional method for spatial measurement 56 2.2.2 Spatial measurement at river confluences 57 2.2.2.1 Dynamics of flow and mixing at river confluences 57 2.2.2.2 Field experiments in river confluences 64 3. Experimental studies 68 3.1 Experimental cases 68 3.2 Laboratory experiment 74 3.2.1 Experimental setup 74 3.2.2 Experimental method 78 3.3 Field-scale experiments in River Experiment Center 83 3.3.1 Experiments in the straight channel 83 3.3.2 Experiments in the meandering channel 96 3.4 Field survey 116 3.4.1 Study area and field measurement 116 3.4.2 Hydraulic and sediment data in rivers with simple geometry 122 3.4.3 Hydraulic and sediment data in river confluences 126 3.5 Analysis of hyperspectral data of suspended sediment 141 3.5.1 Hyperspectral data of laboratory experiment 142 3.5.2 Hyperspectral data of field-scale experiments 146 3.5.2.1 Effect of bottom reflectance 146 3.5.2.2 Principal component analysis of the effect of suspended sediment properties 154 4. Development of suspended sediment concentration estimator using UAV-based hyperspectral imagery 164 4.1 Outline of Cluster-based Machine learning Regression with Optical Variability (CMR-OV) 164 4.2 Pre-processing of hyperspectral images 168 4.3 Regression models and clustering technique 173 4.3.1 Index-based regression models 173 4.3.2 Machine learning regression models 175 4.3.3 Relevant band selection 183 4.3.4 Gaussian mixture model for clustering 185 4.3.5 Performance criteria 188 4.4 Model development and evaluation 189 4.4.1 Comparison of regression models 189 4.4.1.1 OBRA-based explicit models 189 4.4.1.2 Machine learning-based implicit models 194 4.4.2 Assessment of hyperspectral clustering 200 4.4.3 Spatio-temporal SSCV mapping using CMR-OV 215 5. Evaluation of field applicability of CMR-OV 225 5.1 Outline of field applicability test 225 5.2 Cross-applicability validation of CMR-OV 227 5.3 Assessment of field applicability in rivers with simple geometry 234 5.4 Assessment of field applicability in river confluences 241 5.4.1 Classification of river regions using hyperspectral clustering 241 5.4.2 Retrievals of SSCV map 258 5.4.3 Mixing pattern extraction from SSCv map 271 6. Conclusions and future study 274 6.1 Conclusions 274 6.2 Future directions 278 Reference 280 Appendix 308 Appendix A. Breakthrough curve (BTC) analysis 308 Appendix B. Experimental data 310 Appendix B. 1. BTCs of in-situ measured SSC from field-scale experiments 310 Appendix B. 2. Dataset of spectra from hyperspectral images and corresponding SSC in rivers 330 Appendix C. CMR-OV code 331 국문초록 337박

Enhancing the usability of Satellite Earth Observations through Data Driven Models. An application to Sea Water Quality

Earth Observation from satellites has the potential to provide comprehensive, rapid and inexpensive information about land and water bodies. Marine monitoring could gain in effectiveness if integrated with approaches that are able to collect data from wide geographic areas, such as satellite observation. Integrated with in situ measurements, satellite observations enable to extend the punctual information of sampling campaigns to a synoptic view, increase the spatial and temporal coverage, and thus increase the representativeness of the natural diversity of the monitored water bodies, their inter-annual variability and water quality trends, providing information to support EU Member States’ action plans. Turbidity is one of the optically active water quality parameters that can be derived from satellite data, and is one of the environmental indicator considered by EU directives monitoring programmes. Turbidity is a visual property of water, related to the amount of light scattered by particles in water, and it can act as simple and convenient indirect measure of the concentration of suspended solids and other particulate material. A review of the state-of-the-art shows that most traditional methods to estimate turbidity from optical satellite images are based on semi-empirical models relying on few spectral bands. The choice of the most suitable bands to be used is often site and season specific, as it is related to the type and concentration of suspended particles. When investigating wide areas or long time series that include different optical water types, the application of machine learning algorithms seems to be promising due to their flexibility, responding to the need of a model that can adapt to varying water conditions with smooth transition, and their ability to exploit the wealth of spectral information. Moreover, machine learning models have shown to be less affected by atmospheric and other background factors. Atmospheric correction for water leaving reflectance, in fact, still remains one of the major challenges in aquatic remote sensing. The use of machine learning for remotely sensed water quality estimation has spread in recent years thanks to the advances in algorithm development, computing power, and availability of higher spatial resolution data. Among all existing algorithms, the choice of the complexity of the model derives from the nature and number of available data. The present study explores the use of Sentinel-2 MultiSpectral Instrument (MSI) Level-1C Top of Atmosphere spectral radiance to derive water turbidity, through application of a Polynomial Kernel Regularized Least Squares regression. This algorithms is characterized by a simple model structure, good generalization, global optimal solution, especially suitable for non-linear and high dimension problems. The study area is located in the North Tyrrhenian Sea (Italy), covering a coastline of about 100 km, characterized by a varied shoreline, embracing environments worthy of protection and valuable biodiversity, but also relevant ports, and three main river flow and sediment discharge. The coastal environment in this area has been monitored since 2001, according to the 2000/60/EC Water Framework Directive, and in 2008 EU Marine Strategy Framework Directive 2008/56/EC further strengthened the investigation in the area. A dataset of combination of turbidity measurements, expressed in nephelometric turbidity units (NTU), and values of the 13 spectral bands in the pixel corresponding to the sample location was used to calibrate and validate the model. The developed turbidity model shows good agreement of the estimated satellite-derived surface turbidity with the measured one, confirming that the use of ML techniques allows to reach a good accuracy in turbidity estimation from satellite Top of Atmosphere reflectance. Comparison between turbidity estimates obtained from the model with turbidity data from Copernicus CMEMS dataset named ’Mediterranean Sea, Bio-Geo-Chemical, L3, daily observation’, which was used as benchmark, produced consistent results. A band importance analysis revealed the contribution of the different spectral bands and the main role of the red-edge range. Finally, turbidity maps from satellite imagery were produced for the study area, showing the ability of the model to catch extreme events and, overall, how it represents an important tool to improve our understanding of the complex factors that influence water quality in our oceans

Estimating estuarine suspended sediment concentration through spectral indices and band ratios derived from Sentinel-2 data: a case of Umzimvubu Estuary, South Africa

The current study was aimed at evaluating the reliability and efficacy of selected remote sensing band ratios and indices in accurately estimating the spatial patterns of suspended sediment concentration level in Umzimvubu Estuary, Eastern Cape, South Africa. Sentinel-2 imagery was acquired on the 29th of March 2022. Band reflectance values were extracted from Sentinel -2 imagery, and laboratory measurements of suspended sediment concentration were obtained from samples collected from fifty (50) sampling points in the estuary on the 29th of March 2022. Sentinel-2 imagery was then validated with the field data in estimating and mapping the suspended sediment concentration. Several remote sensing band ratios Red/(Green plus Near-Infrared), Near-Infrared/Green, Red plus Near-Infrared/Green, Blue(Green plus Red)/Blue and Green plus Near-Infrared)/Blue and indices, that is the Normalised Difference Turbidity Index (NDTI), Normalized Difference Suspended Sediment Index (NDSSI) and Normalized Suspended Material Index (NSMI)) were then used to predict the suspended sediment concentration from Sentinel-2 imagery. The accuracy of band ratios and indices was evaluated by correlating the prediction against the observed suspended sediment concentration from Sentinel-2 imagery. A total of 50 points were randomly surveyed in the Umzimvubu estuary for analyzing suspended sediment concentration. Results indicate that the Blue (Green plus Red)/Blue, the Green plus Near-Infrared)/Blue and NMSI performed well based on their R-squared. The Blue (Green plus Red)/Blue and Green + Near-Infrared)/Blue band ratios had 0.86 and 0, 94, respectively. While NSMI yielded an R-squared of 0,76 and RMSE of 19,2 mg/L. The results in the current study indicate that Sentinel-2 imagery can reliably estimate the concentration of suspended sediment level in the Umzimvubu Estuary using band ratios and indices.Thesis (MSc) -- Faculty of Science and Agriculture, 202

Estimating estuarine suspended sediment concentration through spectral indices and band ratios derived from Sentinel-2 data: a case of Umzimvubu Estuary, South Africa

The current study was aimed at evaluating the reliability and efficacy of selected remote sensing band ratios and indices in accurately estimating the spatial patterns of suspended sediment concentration level in Umzimvubu Estuary, Eastern Cape, South Africa. Sentinel-2 imagery was acquired on the 29th of March 2022. Band reflectance values were extracted from Sentinel -2 imagery, and laboratory measurements of suspended sediment concentration were obtained from samples collected from fifty (50) sampling points in the estuary on the 29th of March 2022. Sentinel-2 imagery was then validated with the field data in estimating and mapping the suspended sediment concentration. Several remote sensing band ratios Red/(Green plus Near-Infrared), Near-Infrared/Green, Red plus Near-Infrared/Green, Blue(Green plus Red)/Blue and Green plus Near-Infrared)/Blue and indices, that is the Normalised Difference Turbidity Index (NDTI), Normalized Difference Suspended Sediment Index (NDSSI) and Normalized Suspended Material Index (NSMI)) were then used to predict the suspended sediment concentration from Sentinel-2 imagery. The accuracy of band ratios and indices was evaluated by correlating the prediction against the observed suspended sediment concentration from Sentinel-2 imagery. A total of 50 points were randomly surveyed in the Umzimvubu estuary for analyzing suspended sediment concentration. Results indicate that the Blue (Green plus Red)/Blue, the Green plus Near-Infrared)/Blue and NMSI performed well based on their R-squared. The Blue (Green plus Red)/Blue and Green + Near-Infrared)/Blue band ratios had 0.86 and 0, 94, respectively. While NSMI yielded an R-squared of 0,76 and RMSE of 19,2 mg/L. The results in the current study indicate that Sentinel-2 imagery can reliably estimate the concentration of suspended sediment level in the Umzimvubu Estuary using band ratios and indices.Thesis (MSc) -- Faculty of Science and Agriculture, 202

Multidecadal Remote Sensing of Inland Water Dynamics

Remote sensing approaches to measuring inland water dynamics date back more than 50 years. These approaches rely on the unique spectral properties of different waterbodies to delineate surface extents and estimate optically active water quality parameters. Until recently, inland water remote sensing focused largely on localized study domains due to limitations in modelling methods, computing power, and data access. Recent advances in these areas have created novel opportunities for data-driven-multidecadal remote sensing of inland waters at the landscape scale. Here, I highlight the history of inland water remote sensing along with the dominant methodologies, water quality constituents, and limitations involved. I then use this background to contextualize three macroscale inland water remote sensing studies of increasing complexity. The first combines field measurements with remotely sensed surface water extents to identify the impacts of small-scale gold mining in Peru. Our results suggest that mining is leading to synergistic increases in lake area and mercury loading that are significantly heightening exposure risk for people and wildlife. I move from measuring lake extents in Peru to measuring lake color in over 26,000 lakes across the United States. This analysis shows that lake color seasonality can be generalized into five distinct phenology groups that follow well-known patterns of algae growth and succession. The stability of a given lake (i.e. the likelihood it will move from one phenology group to another) is tied to lake and landscape level characteristics including climate and population density. Finally, I move from simple parameters such as quantity and color to estimating multidecadal changes in water clarity in U.S. lakes. I show that lake water clarity in the U.S. has increased by an average of 0.52 cm yr-1 since 1984, largely as a result of extensive U.S. freshwater pollution abatement measures. In combination, these three studies highlight that data intensive remote sensing approaches are expanding the capabilities of inland water remote sensing from local to global scales, and that macroscale remote sensing of inland waters reveals trends and processes that are unobservable using field data alone.Doctor of Philosoph

Quarterly literature review of the remote sensing of natural resources

The Technology Application Center reviewed abstracted literature sources, and selected document data and data gathering techniques which were performed or obtained remotely from space, aircraft or groundbased stations. All of the documentation was related to remote sensing sensors or the remote sensing of the natural resources. Sensors were primarily those operating within the 10 to the minus 8 power to 1 meter wavelength band. Included are NASA Tech Briefs, ARAC Industrial Applications Reports, U.S. Navy Technical Reports, U.S. Patent reports, and other technical articles and reports

Monitoring Sebaran Total Padatan Tersuspensi Tahun 2019 di Muara Sungai Tallo Kota Makassar Menggunakan Citra Sentinel 2

Penelitian ini bertujuan untuk menghasilkan metode alternatif dalam memonitor sebaran sedimen tersuspensi di perairan Kota Makassar menggunakan citra satelit. Lokasi studi di muara Sungai Tallo, Kota Makassar dengan waktu pengamatan dari Januari hingga Nopember 2019. Sampling lapangan dilakukan pada Maret 2019 dan Nopember 2019. Sejumlah sebelas citra Sentinel 2 telah diunduh dari situs https://earthexplorer.usgs.gov dengan penutupan awan kurang dari 10 persen. Sampling TSS menggunakan van dorn water sampler dan posisinya dicatat menggunakan GPS etrex 10. Pergerakan arus direkam menggunakan drifter langrange. Analisa TSS dilakukan di laboratorium oseanografi kimia, Universitas Hasanuddin. Pengolahan citra menggunakan perangkat Idrisi Terrset 18.31. Sejak tahun 2015 hingga 2019, curah hujan tertinggi umumnya terjadi pada bulan Januari (400 hingga 1000 mm) dan terendah terjadi pada Juli hingga Agustus (kurang dari 100 mm). Tahun 2017 merupakan tahun dengan rata-rata tahunan curah hujantertinggi (311 mm) dan tahun 2019 merupakan tahun dengan rata-rata tahunan curah hujan terendah (180 mm). Konsentrasi TSS yang sangat tinggi terjadi pada bulan Januari 2019 dan terendah terjadi pada April hingga Juli 2019. Secara rata-rata tahunan nilai TSS di Muara Tallo selama tahun 2019 adalah sekitar 144 mg/L. Konsentrasi TSS yang tinggi pada saat air pasang cenderung ditemukan di mulut muara dan pada saat surut menyebar ke arah Utara mengikuti pergerakan arus susur pantai.Kata kunci : monitoring, total padatan tersuspensi, Sentinel 2, Makassa

Earth Resources: A continuing bibliography with indexes, issue 13

This bibliography lists 524 reports, articles, and other documents introduced into the NASA scientific and technical information system between January 1977 and March 1977. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis