The acquisition of Hyperspectral Digital Surface Models of crops from UAV snapshot cameras

This thesis develops a new approach to capture information about agricultural crops by utilizing advances in the field of robotics, sensor technology, computer vision and photogrammetry: Hyperspectral digital surface models (HS DSMs) generated with UAV snapshot cameras are a representation of a surface in 3D space linked with hyperspectral information emitted and reflected by the objects covered by that surface. The overall research aim of this thesis is to evaluate if HS DSMs are suited for supporting a site-specific crop management. Based on six research studies, three research objectives are discussed for this evaluation. Firstly the influences of environmental effects, the sensing system and data processing of the spectral data within HS DSMs are discussed. Secondly, the comparability of HS DSMs to data from other remote sensing methods is investigated and thirdly their potential to support site-specific crop management is evaluated. Most data within this thesis was acquired at a plant experimental-plot experiment in Klein-Altendorf, Germany, with six different barley varieties and two different fertilizer treatments in the growing seasons of 2013 and 2014. In total, 22 measurement campaigns were carried out in the context of this thesis. HS DSMs acquired with the hyperspectral snapshot cameras Cubert UHD 185-Firefly show great potential for practical applications. The combination of UAVs and the UHD allowed data to be captured at a high spatial, spectral and temporal resolution. The spatial resolution allowed detection of small-scale heterogeneities within the plant population. Additionally, with the spectral and 3D information contained in HS DSMs, plant parameters such as chlorophyll, biomass and plant height could be estimated within individual, and across different growing stages. The techniques developed in this thesis therefore offer a significant contribution towards increasing cropping efficiency through the support of site-specific management

Unmanned Aerial Systems (UAS)-Based Methods for Solar Induced Chlorophyll Fluorescence (SIF) Retrieval with Non-Imaging Spectrometers: State of the Art

Chlorophyll fluorescence (ChlF) information offers a deep insight into the plant physiological status by reason of the close relationship it has with the photosynthetic activity. The unmanned aerial systems (UAS)-based assessment of solar induced ChlF (SIF) using non-imaging spectrometers and radiance-based retrieval methods, has the potential to provide spatio-temporal photosynthetic performance information at field scale. The objective of this manuscript is to report the main advances in the development of UAS-based methods for SIF retrieval with non-imaging spectrometers through the latest scientific contributions, some of which are being developed within the frame of the Training on Remote Sensing for Ecosystem Modelling (TRuStEE) program. Investigations from the Universities of Edinburgh (School of Geosciences) and Tasmania (School of Technology, Environments and Design) are first presented, both sharing the principle of the spectroradiometer optical path bifurcation throughout, the so called ‘Piccolo-Doppio’ and ‘AirSIF’ systems, respectively. Furthermore, JB Hyperspectral Devices’ ongoing investigations towards the closest possible characterization of the atmospheric interference suffered by orbital platforms are outlined. The latest approach focuses on the observation of one single ground point across a multiple-kilometer atmosphere vertical column using the high altitude UAS named as AirFloX, mounted on a specifically designed and manufactured fixed wing platform: ‘FloXPlane’. We present technical details and preliminary results obtained from each instrument, a summary of their main characteristics, and finally the remaining challenges and open research questions are addressed. On the basis of the presented findings, the consensus is that SIF can be retrieved from low altitude spectroscopy. However, the UAS-based methods for SIF retrieval still present uncertainties associated with the current sensor characteristics and the spatio-temporal mismatching between aerial and ground measurements, which complicate robust validations. Complementary studies regarding the standardization of calibration methods and the characterization of spectroradiometers and data processing workflows are also required. Moreover, other open research questions such as those related to the implementation of atmospheric correction, bidirectional reflectance distribution function (BRDF) correction, and accurate surface elevation models remain to be addressed

시공간 해상도 향상을 통한 식생 변화 모니터링

학위논문(박사) -- 서울대학교대학원 : 환경대학원 협동과정 조경학, 2023. 2. 류영렬.육상 생태계에서 대기권과 생물권의 상호 작용을 이해하기 위해서는 식생 변화의 모니터링이 필요하다. 이 때, 위성영상은 지표면을 관측하여 식생지도를 제공할 수 있지만, 지표변화의 상세한 정보는 구름이나 위성 이미지의 공간 해상도에 의해 제한되었다. 또한 위성영상의 시공간 해상도가 식생지도를 통한 광합성 모니터링에 미치는 영향은 완전히 밝혀지지 않았다. 본 논문에서는 고해상도 식생 지도를 일단위로 생성하기 위성 영상의 시공간 해상도를 향상시키는 것을 목표로 하였다. 고해상도 위성영상을 활용한 식생 변화 모니터링을 시공간적으로 확장하기 위해 1) 정지궤도 위성을 활용한 영상융합을 통해 시간해상도 향상, 2) 적대적생성네트워크를 활용한 공간해상도 향상, 3) 시공간해상도가 높은 위성영상을 토지피복이 균질하지 않은 공간에서 식물 광합성 모니터링을 수행하였다. 이처럼, 위성기반 원격탐지에서 새로운 기술이 등장함에 따라 현재 및 과거의 위성영상은 시공간 해상도 측면에서 향상되어 식생 변화의 모니터링 할 수 있다. 제2장에서는 정지궤도위성영상을 활용하는 시공간 영상융합으로 식물의 광합성을 모니터링 했을 때, 시간해상도가 향상됨을 보였다. 시공간 영상융합 시, 구름탐지, 양방향 반사 함수 조정, 공간 등록, 시공간 융합, 시공간 결측치 보완 등의 과정을 거친다. 이 영상융합 산출물은 경작관리 등으로 식생 지수의 연간 변동이 큰 두 장소(농경지와 낙엽수림)에서 평가하였다. 그 결과, 시공간 영상융합 산출물은 결측치 없이 현장관측을 예측하였다 (R2 = 0.71, 상대 편향 = 5.64% 농경지; R2 = 0.79, 상대 편향 = -13.8%, 활엽수림). 시공간 영상융합은 식생 지도의 시공간 해상도를 점진적으로 개선하여, 식물 생장기동안 위성영상이 현장 관측을 과소 평가를 줄였다. 영상융합은 높은 시공간 해상도로 광합성 지도를 일간격으로 생성하기에 이를 활용하여 위성 영상의 제한된 시공간 해상도로 밝혀지지 않은 식물변화의 과정을 발견하길 기대한다. 식생의 공간분포은 정밀농업과 토지 피복 변화 모니터링을 위해 필수적이다. 고해상도 위성영상으로 지구 표면을 관측하는 것을 용이하게 해졌다. 특히 Planet Fusion은 초소형위성군 데이터를 최대한 활용해 데이터 결측이 없는 3m 공간 해상도의 지표 표면 반사도이다. 그러나 과거 위성 센서(Landsat의 경우 30~60m)의 공간 해상도는 식생의 공간적 변화를 상세 분석하는 것을 제한했다. 제3장에서는 Landsat 데이터의 공간 해상도를 향상하기 위해 Planet Fusion 및 Landsat 8 데이터를 사용하여 이중 적대적 생성 네트워크(the dual RSS-GAN)를 학습시켜, 고해상도 정규화 식생 지수(NDVI)와 식물 근적외선 반사(NIRv)도를 생성하는 한다. 타워기반 현장 식생지수(최대 8년)와 드론기반 초분광지도로 the dual RSS-GAN의 성능을 대한민국 내 두 대상지(농경지와 활엽수림)에서 평가했다. The dual RSS-GAN은 Landsat 8 영상의 공간해상도를 향상시켜 공간 표현을 보완하고 식생 지수의 계절적 변화를 포착했다(R2> 0.96). 그리고 the dual RSS-GAN은 Landsat 8 식생 지수가 현장에 비해 과소 평가되는 것을 완화했다. 현장 관측에 비해 이중 RSS-GAN과 Landsat 8의 상대 편향 값 각각 -0.8% 에서 -1.5%, -10.3% 에서 -4.6% 였다. 이러한 개선은 Planet Fusion의 공간정보를 이중 RSS-GAN로 학습하였기에 가능했다. 헤당 연구 결과는 Landsat 영상의 공간 해상도를 향상시켜 숨겨진 공간 정보를 제공하는 새로운 접근 방식이다. 고해상도에서 식물 광합성 지도는 토지피복이 복잡한 공간에서 탄소 순환 모니터링시 필수적이다. 그러나 Sentinel-2, Landsat 및 MODIS와 같이 태양 동조 궤도에 있는 위성은 공간 해상도가 높거나 시간 해상도 높은 위성영상만 제공할 수 있다. 최근 발사된 초소형위성군은 이러한 해상도 한계을 극복할 수 있다. 특히 Planet Fusion은 초소형위성 자료의 시공간 해상도로 지표면을 관측할 수 있다. 4장에서, Planet Fusion 지표반사도를 이용하여 식생에서 반사된 근적외선 복사(NIRvP)를 3m 해상도 지도를 일간격으로 생성했다. 그런 다음 미국 캘리포니아주 새크라멘토-샌 호아킨 델타의 플럭스 타워 네트워크 데이터와 비교하여 식물 광합성을 추정하기 위한 NIRvP 지도의 성능을 평가하였다. 전체적으로 NIRvP 지도는 습지의 잦은 수위 변화에도 불구하고 개별 대상지의 식물 광합성의 시간적 변화를 포착하였다. 그러나 대상지 전체에 대한 NIRvP 지도와 식물 광합성 사이의 관계는 NIRvP 지도를 플럭스 타워 관측범위와 일치시킬 때만 높은 상관관계를 보였다. 관측범위를 일치시킬 경우, NIRvP 지도는 식물 광합성을 추정하는 데 있어 현장 NIRvP보다 우수한 성능을 보였다. 이러한 성능 차이는 플럭스 타워 관측범위를 일치시킬 때, 연구 대상지 간의 NIRvP-식물 광합성 관계의 기울기가 일관성을 보였기 때문이다. 본 연구 결과는 위성 관측을 플럭스 타워 관측범위와 일치시키는 것의 중요성을 보여주고 높은 시공간 해상도로 식물 광합성을 원격으로 모니터링하는 초소형위성군 자료의 잠재력을 보여준다.Monitoring changes in terrestrial vegetation is essential to understanding interactions between atmosphere and biosphere, especially terrestrial ecosystem. To this end, satellite remote sensing offer maps for examining land surface in different scales. However, the detailed information was hindered under the clouds or limited by the spatial resolution of satellite imagery. Moreover, the impacts of spatial and temporal resolution in photosynthesis monitoring were not fully revealed. In this dissertation, I aimed to enhance the spatial and temporal resolution of satellite imagery towards daily gap-free vegetation maps with high spatial resolution. In order to expand vegetation change monitoring in time and space using high-resolution satellite images, I 1) improved temporal resolution of satellite dataset through image fusion using geostationary satellites, 2) improved spatial resolution of satellite dataset using generative adversarial networks, and 3) showed the use of high spatiotemporal resolution maps for monitoring plant photosynthesis especially over heterogeneous landscapes. With the advent of new techniques in satellite remote sensing, current and past datasets can be fully utilized for monitoring vegetation changes in the respect of spatial and temporal resolution. In Chapter 2, I developed the integrated system that implemented geostationary satellite products in the spatiotemporal image fusion method for monitoring canopy photosynthesis. The integrated system contains the series of process (i.e., cloud masking, nadir bidirectional reflectance function adjustment, spatial registration, spatiotemporal image fusion, spatial gap-filling, temporal-gap-filling). I conducted the evaluation of the integrated system over heterogeneous rice paddy landscape where the drastic land cover changes were caused by cultivation management and deciduous forest where consecutive changes occurred in time. The results showed that the integrated system well predict in situ measurements without data gaps (R2 = 0.71, relative bias = 5.64% at rice paddy site; R2 = 0.79, relative bias = -13.8% at deciduous forest site). The integrated system gradually improved the spatiotemporal resolution of vegetation maps, reducing the underestimation of in situ measurements, especially during peak growing season. Since the integrated system generates daily canopy photosynthesis maps for monitoring dynamics among regions of interest worldwide with high spatial resolution. I anticipate future efforts to reveal the hindered information by the limited spatial and temporal resolution of satellite imagery. Detailed spatial representations of terrestrial vegetation are essential for precision agricultural applications and the monitoring of land cover changes in heterogeneous landscapes. The advent of satellite-based remote sensing has facilitated daily observations of the Earths surface with high spatial resolution. In particular, a data fusion product such as Planet Fusion has realized the delivery of daily, gap-free surface reflectance data with 3-m pixel resolution through full utilization of relatively recent (i.e., 2018-) CubeSat constellation data. However, the spatial resolution of past satellite sensors (i.e., 30–60 m for Landsat) has restricted the detailed spatial analysis of past changes in vegetation. In Chapter 3, to overcome the spatial resolution constraint of Landsat data for long-term vegetation monitoring, we propose a dual remote-sensing super-resolution generative adversarial network (dual RSS-GAN) combining Planet Fusion and Landsat 8 data to simulate spatially enhanced long-term time-series of the normalized difference vegetation index (NDVI) and near-infrared reflectance from vegetation (NIRv). We evaluated the performance of the dual RSS-GAN against in situ tower-based continuous measurements (up to 8 years) and remotely piloted aerial system-based maps of cropland and deciduous forest in the Republic of Korea. The dual RSS-GAN enhanced spatial representations in Landsat 8 images and captured seasonal variation in vegetation indices (R2 > 0.95, for the dual RSS-GAN maps vs. in situ data from all sites). Overall, the dual RSS-GAN reduced Landsat 8 vegetation index underestimations compared with in situ measurements; relative bias values of NDVI ranged from −3.2% to 1.2% and −12.4% to −3.7% for the dual RSS-GAN and Landsat 8, respectively. This improvement was caused by spatial enhancement through the dual RSS-GAN, which captured fine-scale information from Planet Fusion. This study presents a new approach for the restoration of hidden sub-pixel spatial information in Landsat images. Mapping canopy photosynthesis in both high spatial and temporal resolution is essential for carbon cycle monitoring in heterogeneous areas. However, well established satellites in sun-synchronous orbits such as Sentinel-2, Landsat and MODIS can only provide either high spatial or high temporal resolution but not both. Recently established CubeSat satellite constellations have created an opportunity to overcome this resolution trade-off. In particular, Planet Fusion allows full utilization of the CubeSat data resolution and coverage while maintaining high radiometric quality. In Chapter 4, I used the Planet Fusion surface reflectance product to calculate daily, 3-m resolution, gap-free maps of the near-infrared radiation reflected from vegetation (NIRvP). I then evaluated the performance of these NIRvP maps for estimating canopy photosynthesis by comparing with data from a flux tower network in Sacramento-San Joaquin Delta, California, USA. Overall, NIRvP maps captured temporal variations in canopy photosynthesis of individual sites, despite changes in water extent in the wetlands and frequent mowing in the crop fields. When combining data from all sites, however, I found that robust agreement between NIRvP maps and canopy photosynthesis could only be achieved when matching NIRvP maps to the flux tower footprints. In this case of matched footprints, NIRvP maps showed considerably better performance than in situ NIRvP in estimating canopy photosynthesis both for daily sum and data around the time of satellite overpass (R2 = 0.78 vs. 0.60, for maps vs. in situ for the satellite overpass time case). This difference in performance was mostly due to the higher degree of consistency in slopes of NIRvP-canopy photosynthesis relationships across the study sites for flux tower footprint-matched maps. Our results show the importance of matching satellite observations to the flux tower footprint and demonstrate the potential of CubeSat constellation imagery to monitor canopy photosynthesis remotely at high spatio-temporal resolution.Chapter 1. Introduction 2 1. Background 2 1.1 Daily gap-free surface reflectance using geostationary satellite products 2 1.2 Monitoring past vegetation changes with high-spatial-resolution 3 1.3 High spatiotemporal resolution vegetation photosynthesis maps 4 2. Purpose of Research 4 Chapter 2. Generating daily gap-filled BRDF adjusted surface reflectance product at 10 m resolution using geostationary satellite product for monitoring daily canopy photosynthesis 6 1. Introduction 6 2. Methods 11 2.1 Study sites 11 2.2 In situ measurements 13 2.3 Satellite products 14 2.4 Integrated system 17 2.5 Canopy photosynthesis 21 2.6 Evaluation 23 3. Results and discussion 24 3.1 Comparison of STIF NDVI and NIRv with in situ NDVI and NIRv 24 3.2 Comparison of STIF NIRvP with in situ NIRvP 28 4. Conclusion 31 Chapter 3. Super-resolution of historic Landsat imagery using a dual Generative Adversarial Network (GAN) model with CubeSat constellation imagery for monitoring vegetation changes 32 1. Introduction 32 2. Methods 38 2.1 Real-ESRGAN model 38 2.2 Study sites 40 2.3 In situ measurements 42 2.4 Vegetation index 44 2.5 Satellite data 45 2.6 Planet Fusion 48 2.7 Dual RSS-GAN via fine-tuned Real-ESRGAN 49 2.8 Evaluation 54 3. Results 57 3.1 Comparison of NDVI and NIRv maps from Planet Fusion, Sentinel 2 NBAR, and Landsat 8 NBAR data with in situ NDVI and NIRv 57 3.2 Comparison of dual RSS-SRGAN model results with Landsat 8 NDVI and NIRv 60 3.3 Comparison of dual RSS-GAN model results with respect to in situ time-series NDVI and NIRv 63 3.4 Comparison of the dual RSS-GAN model with NDVI and NIRv maps derived from RPAS 66 4. Discussion 70 4.1 Monitoring changes in terrestrial vegetation using the dual RSS-GAN model 70 4.2 CubeSat data in the dual RSS-GAN model 72 4.3 Perspectives and limitations 73 5. Conclusion 78 Appendices 79 Supplementary material 82 Chapter 4. Matching high resolution satellite data and flux tower footprints improves their agreement in photosynthesis estimates 85 1. Introduction 85 2. Methods 89 2.1 Study sites 89 2.2 In situ measurements 92 2.3 Planet Fusion NIRvP 94 2.4 Flux footprint model 98 2.5 Evaluation 98 3. Results 105 3.1 Comparison of Planet Fusion NIRv and NIRvP with in situ NIRv and NIRvP 105 3.2 Comparison of instantaneous Planet Fusion NIRv and NIRvP with against tower GPP estimates 108 3.3 Daily GPP estimation from Planet Fusion -derived NIRvP 114 4. Discussion 118 4.1 Flux tower footprint matching and effects of spatial and temporal resolution on GPP estimation 118 4.2 Roles of radiation component in GPP mapping 123 4.3 Limitations and perspectives 126 5. Conclusion 133 Appendix 135 Supplementary Materials 144 Chapter 5. Conclusion 153 Bibliography 155 Abstract in Korea 199 Acknowledgements 202박

Unmanned aerial vehicles (UAVs) for multi-temporal crop surface modelling. A new method for plant height and biomass estimation based on RGB-imaging

Data collection with unmanned aerial vehicles (UAVs) fills a gap on the observational scale in re-mote sensing by delivering high spatial and temporal resolution data that is required in crop growth monitoring. The latter is part of precision agriculture that facilitates detection and quan-tification of within-field variability to support agricultural management decisions such as effective fertilizer application. Biophysical parameters such as plant height and biomass are monitored to describe crop growth and serve as an indicator for the final crop yield. Multi-temporal crop surface models (CSMs) provide spatial information on plant height and plant growth. This study aims to examine whether (1) UAV-based CSMs are suitable for plant height modelling, (2) the derived plant height can be used for biomass estimation, and (3) the combination of plant height and vegetation indices has an added value for biomass estimation. To achieve these objectives, UAV-flight campaigns were carried out with a red-green-blue (RGB) camera over controlled field experiments on three study sites, two for summer barley in Western Germany and one for rice in Northeast China. High-resolution, multi-temporal CSMs were derived from the images by using computer vision software following the structure from motion (SfM) approach. The results show that plant height and plant growth can be accurately modelled with UAV-based CSMs from RGB imaging. To maximise the CSMs’ quality, accurate flight planning and well-considered data collection is necessary. Furthermore, biomass is successfully estimated from the derived plant height, with the restriction that results are based on a single-year dataset and thus require further validation. Nevertheless, plant height shows robust estimates in comparison with various vegetation indices. As for biomass estimation in early growth stages additional po-tential is found in exploiting visible band vegetation indices from UAV-based red-green-blue (RGB) imaging. However, the results are limited due to the use of uncalibrated images. Combining visible band vegetation indices and plant height does not significantly improve the performance of the biomass models. This study demonstrates that UAV-based RGB imaging delivers valuable data for productive crop monitoring. The demonstrated results for plant height and biomass estimation open new possi-bilities in precision agriculture by capturing in-field variability

Estimating Biomass and Nitrogen Amount of Barley and Grass Using UAV and Aircraft Based Spectral and Photogrammetric 3D Features

201

Multitemporal monitoring of plant area index in the Valencia Rice District with PocketLAI

Leaf area index (LAI) is a key biophysical parameter used to determine foliage cover and crop growth in environmental studies in order to assess crop yield. Frequently, plant canopy analyzers (LAI-2000) and digital cameras for hemispherical photography (DHP) are used for indirect effective plant area index (PAIeff ) estimates. Nevertheless, these instruments are expensive and have the disadvantages of low portability and maintenance. Recently, a smartphone app called PocketLAI was presented and tested for acquiring PAIeff measurements. It was used during an entire rice season for indirect PAIeff estimations and for deriving reference high-resolution PAIeff maps. Ground PAIeff values acquired with PocketLAI, LAI-2000, and DHP were well correlated (R2 = 0.95, RMSE = 0.21 m2/m2 for Licor-2000, and R2 = 0.94, RMSE = 0.6 m2/m2 for DHP). Complementary data such as phenology and leaf chlorophyll content were acquired to complement seasonal rice plant information provided by PAIeff. High-resolution PAIeff maps, which can be used for the validation of remote sensing products, have been derived using a global transfer function (TF) made of several measuring dates and their associated satellite radiances

Evaluation of the Influence of Field Conditions on Aerial Multispectral Images and Vegetation Indices

Remote sensing is a method used for monitoring and measuring agricultural crop fields. Unmanned aerial vehicles (UAV) are used to effectively monitor crops via different camera technologies. Even though aerial imaging can be considered a rather straightforward process, more focus should be given to data quality and processing. This research focuses on evaluating the influences of field conditions on raw data quality and commonly used vegetation indices. The aerial images were taken with a custom-built UAV by using a multispectral camera at four different times of the day and during multiple times of the season. Measurements were carried out in the summer seasons of 2019 and 2020. The imaging data were processed with different software to calculate vegetation indices for 10 reference areas inside the fields. The results clearly show that NDVI (normalized difference vegetation index) was the least affected vegetation index by the field conditions. The coefficient of variation (CV) was determined to evaluate the variations in vegetation index values within a day. Vegetation index TVI (transformed vegetation index) and NDVI had coefficient of variation values under 5%, whereas with GNDVI (green normalized difference vegetation index), the value was under 10%. Overall, the vegetation indices that include near-infrared (NIR) bands are less affected by field condition changes

Evaluation of the Influence of Field Conditions on Aerial Multispectral Images and Vegetation Indices

Remote sensing is a method used for monitoring and measuring agricultural crop fields. Unmanned aerial vehicles (UAV) are used to effectively monitor crops via different camera technologies. Even though aerial imaging can be considered a rather straightforward process, more focus should be given to data quality and processing. This research focuses on evaluating the influences of field conditions on raw data quality and commonly used vegetation indices. The aerial images were taken with a custom-built UAV by using a multispectral camera at four different times of the day and during multiple times of the season. Measurements were carried out in the summer seasons of 2019 and 2020. The imaging data were processed with different software to calculate vegetation indices for 10 reference areas inside the fields. The results clearly show that NDVI (normalized difference vegetation index) was the least affected vegetation index by the field conditions. The coefficient of variation (CV) was determined to evaluate the variations in vegetation index values within a day. Vegetation index TVI (transformed vegetation index) and NDVI had coefficient of variation values under 5%, whereas with GNDVI (green normalized difference vegetation index), the value was under 10%. Overall, the vegetation indices that include near-infrared (NIR) bands are less affected by field condition changes

Crop Disease Detection Using Remote Sensing Image Analysis

Pest and crop disease threats are often estimated by complex changes in crops and the applied agricultural practices that result mainly from the increasing food demand and climate change at global level. In an attempt to explore high-end and sustainable solutions for both pest and crop disease management, remote sensing technologies have been employed, taking advantages of possible changes deriving from relative alterations in the metabolic activity of infected crops which in turn are highly associated to crop spectral reflectance properties. Recent developments applied to high resolution data acquired with remote sensing tools, offer an additional tool which is the opportunity of mapping the infected field areas in the form of patchy land areas or those areas that are susceptible to diseases. This makes easier the discrimination between healthy and diseased crops, providing an additional tool to crop monitoring. The current book brings together recent research work comprising of innovative applications that involve novel remote sensing approaches and their applications oriented to crop disease detection. The book provides an in-depth view of the developments in remote sensing and explores its potential to assess health status in crops