Advances in Radar Remote Sensing of Agricultural Crops: A Review

There are enormous advantages of a review article in the field of emerging technology like radar remote sensing applications in agriculture. This paper aims to report select recent advancements in the field of Synthetic Aperture Radar (SAR) remote sensing of crops. In order to make the paper comprehensive and more meaningful for the readers, an attempt has also been made to include discussion on various technologies of SAR sensors used for remote sensing of agricultural crops viz. basic SAR sensor, SAR interferometry (InSAR), SAR polarimetry (PolSAR) and polarimetric interferometry SAR (PolInSAR). The paper covers all the methodologies used for various agricultural applications like empirically based models, machine learning based models and radiative transfer theorem based models. A thorough literature review of more than 100 research papers indicates that SAR polarimetry can be used effectively for crop inventory and biophysical parameters estimation such are leaf area index, plant water content, and biomass but shown less sensitivity towards plant height as compared to SAR interferometry. Polarimetric SAR Interferometry is preferable for taking advantage of both SAR polarimetry and SAR interferometry. Numerous studies based upon multi-parametric SAR indicate that optimum selection of SAR sensor parameters enhances SAR sensitivity as a whole for various agricultural applications. It has been observed that researchers are widely using three models such are empirical, machine learning and radiative transfer theorem based models. Machine learning based models are identified as a better approach for crop monitoring using radar remote sensing data. It is expected that the review article will not only generate interest amongst the readers to explore and exploit radar remote sensing for various agricultural applications but also provide a ready reference to the researchers working in this field

Applications of satellite ‘hyper-sensing’ in Chinese agriculture:Challenges and opportunities

Ensuring adequate food supplies to a large and increasing population continues to be the key challenge for China. Given the increasing integration of China within global markets for agricultural products, this issue is of considerable significance for global food security. Over the last 50 years, China has increased the production of its staple crops mainly by increasing yield per unit land area. However, this has largely been achieved through inappropriate agricultural practices, which have caused environmental degradation, with deleterious consequences for future agricultural productivity. Hence, there is now a pressing need to intensify agriculture in China using practices that are environmentally and economically sustainable. Given the dynamic nature of crops over space and time, the use of remote sensing technology has proven to be a valuable asset providing end-users in many countries with information to guide sustainable agricultural practices. Recently, the field has experienced considerable technological advancements reflected in the availability of ‘hyper-sensing’ (high spectral, spatial and temporal) satellite imagery useful for monitoring, modelling and mapping of agricultural crops. However, there still remains a significant challenge in fully exploiting such technologies for addressing agricultural problems in China. This review paper evaluates the potential contributions of satellite ‘hyper-sensing’ to agriculture in China and identifies the opportunities and challenges for future work. We perform a critical evaluation of current capabilities in satellite ‘hyper-sensing’ in agriculture with an emphasis on Chinese sensors. Our analysis draws on a series of in-depth examples based on recent and on-going projects in China that are developing ‘hyper-sensing’ approaches for (i) measuring crop phenology parameters and predicting yields; (ii) specifying crop fertiliser requirements; (iii) optimising management responses to abiotic and biotic stress in crops; (iv) maximising yields while minimising water use in arid regions; (v) large-scale crop/cropland mapping; and (vi) management zone delineation. The paper concludes with a synthesis of these application areas in order to define the requirements for future research, technological innovation and knowledge exchange in order to deliver yield sustainability in China

Effects of changing cultural practices on C-band SAR backscatter using Envisat ASAR data in the Mekong River Delta

International audienceChanges in rice cultivation systems have been observed in the Mekong River Delta, Vietnam. Among the changes in cultural practices, the change from transplanting to direct sowing, the use of water-saving technology, and the use of high production method could have impacts on radar remote sensing methods previously developed for rice monitoring. Using Envisat (Environmental Satellite) ASAR (Advanced Synthetic Aperture Radar) data over the province of An Giang, this study showed that the radar backscattering behaviour is much different from that of the reported traditional rice. At the early stage of the season, direct sowing on fields with rough and wet soil surface provides very high backscatter values for HH (Horizontal transmit - Horizontal receive polarisation) and VV (Vertical transmit - Vertical receive polarisation) data, as a contrast compared to the very low backscatter of fields covered with water before emergence. The temporal increase of the backscatter is therefore not observed clearly over direct sowing fields. Hence, the use of the intensity temporal change as a rice classifier proposed previously may not apply. Due to the drainage that occurs during the season, HH, VV and HH/VV are not strongly related to biomass, in contrast with past results. However, HH/VV ratio could be used to derive the rice/non-rice classification algorithm for all conditions of rice fields in the test province. The mapping results using the HH/VV polarization ratio at a single date in the middle period of the rice season were assessed using statistical data at different districts in the province, where very high accuracy was found. The method can be applied to other regions, provided that the synthetic aperture radar data are acquired during the peak period of the rice season, and that few training fields provide adjusted threshold values used in the method

Mapping of multitemporal rice (Oryza sativa L.) growth stages using remote sensing with multi-sensor and machine learning : a thesis dissertation presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Earth Science at Massey University, Manawatū, New Zealand

Figure 2.1 is adapted and re-used under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.Rice (Oryza Sativa) plays a pivotal role in food security for Asian countries, especially in Indonesia. Due to the increasing pressure of environmental changes, such as land use and climate, rice cultivation areas need to be monitored regularly and spatially to ensure sustainable rice production. Moreover, timely information of rice growth stages (RGS) can lead to more efficient of inputs distribution from water, seed, fertilizer, and pesticide. One of the efficient solutions for regularly mapping the rice crop is using Earth observation satellites. Moreover, the increasing availability of open access satellite images such as Landsat-8, Sentinel-1, and Sentinel-2 provides ample opportunities to map continuous and high-resolution rice growth stages with greater accuracy. The majority of the literature has focused on mapping rice area, cropping patterns and relied mainly on the phenology of vegetation. However, the mapping process of RGS was difficult to assess the accuracy, time-consuming, and depended on only one sensor. In this work, we discuss the use of machine learning algorithms (MLA) for mapping paddy RGS with multiple remote sensing data in near-real-time. The study area was Java Island, which is the primary rice producer in Indonesia. This study has investigated: (1) the mapping of RGS using Landsat-8 imagery and different MLAs, and their rigorous performance was evaluated by conducting a multitemporal analysis; (2) the temporal consistency of predicting RGS using Sentinel-2, MOD13Q1, and Sentinel-1 data; (3) evaluating the correlation of local statistics data and paddy RGS using Sentinel-2, PROBA-V, and Sentinel-1 with MLAs. The ground truth datasets were collected from multi-year web camera data (2014-2016) and three months of the field campaign in different regions of Java (2018). The study considered the RGS in the analysis to be vegetative, reproductive, ripening, bare land, and flooding, and MLAs such as support vector machines (SVMs), random forest (RF), and artificial neural network (ANN) were used. The temporal consistency matrix was used to compare the classification maps within three sensor datasets (Landsat-8 OLI, Sentinel-2, and Sentinel-2, MOD13Q1, Sentinel-1) and in four periods (5, 10, 15, 16 days). Moreover, the result of the RGS map was also compared with monthly data from local statistics within each sub-district using cross-correlation analysis. The result from the analysis shows that SVM with a radial base function outperformed the RF and ANN and proved to be a robust method for small-size datasets (< 1,000 points). Compared to Sentinel-2, Landsat-8 OLI gives less accuracy due to the lack of a red-edge band and larger pixel size (30 x 30 m). Integration of Sentinel-2, MOD13Q1, and Sentinel-1 improved the classification performance and increased the temporal availability of cloud-free maps. The integration of PROBA-V and Sentinel-1 improved the classification accuracy from the Landsat-8 result, consistent with the monthly rice planting area statistics at the sub-district level. The western area of Java has the highest accuracy and consistency since the cropping pattern only relied on rice cultivation. In contrast, less accuracy was noticed in the eastern area because of upland rice cultivation due to limited irrigation facilities and mixed cropping. In addition, the cultivation of shallots to the north of Nganjuk Regency interferes with the model predictions because the cultivation of shallots resembles the vegetative phase due to the water banks. One future research idea is the auto-detection of the cropping index in the complex landscape to be able to use it for mapping RGS on a global scale. Detection of the rice area and RGS using Google Earth Engine (GEE) can be an action plan to disseminate the information quickly on a planetary scale. Our results show that the multitemporal Sentinel-1 combined with RF can detect rice areas with high accuracy (>91%). Similarly, accurate RGS maps can be detected by integrating multiple remote sensing (Sentinel-2, Landsat-8 OLI, and MOD13Q1) data with acceptable accuracy (76.4%), with high temporal frequency and lower cloud interference (every 16 days). Overall, this study shows that remote sensing combined with the machine learning methodology can deliver information on RGS in a timely fashion, which is easy to scale up and consistent both in time and space and matches the local statistics. This thesis is also in line with the existing rice monitoring projects such as Crop Monitor, Crop Watch, AMIS, and Sen4Agri to support disseminating information over a large area. To sum up, the proposed workflow and detailed map provide a more accurate method and information in near real-time for stakeholders, such as governmental agencies against the existing mapping method. This method can be introduced to provide accurate information to rice farmers promptly with sufficient inputs such as irrigation, seeds, and fertilisers for ensuring national food security from the shifting planting time due to climate change

Combining Multitemporal Microwave and Optical Remote Sensing Data. Mapping of Land Use / Land Cover, Crop Type, and Crop Traits

Humanity has changed the earth’s surface to a dramatic extent. This is especially true for the area used for agricultural production. Against the background of a growing world population and the associated increased demand for food, it is precisely this area that will become even more important in the future. In order not to have to allocate even more land to agricultural use, optimization and intensification is the only way out of the dilemma. In this context, precise Geoinformation of the agriculturally used area is of central importance. It is utilized for improving land use, producing yield forecasts for more stable food security, and optimizing agricultural management. Rapid developments in the field of satellite-based remote sensing sensors make it possible to monitor agricultural areas with increased spatial, spectral and temporal resolution. However, to retrieve the needed information from this data, new methods are needed. Furthermore, the quality of the data has to be verified. Only then can the presented geodata help to grow crops more sustainably and more efficiently. This thesis develops new approaches for monitoring agricultural areas using the technology of microwave remote sensing in combination with optical remote sensing and existing geodata. It is framed by the overall objective to obtain knowledge on how this combination of data can provide the necessary geoinformation for land use studies, precision farming, and agricultural monitoring systems. Hundreds of remote sensing images from more than eight different satellites were analyzed in six research studies from two different Areas of Interest (AOIs). The studies guide through various spatial scales. First, the general Land Use / Land Cover (LULC) on a regional level in a multi-sensor scenario is derived, evaluating different sensor combinations of varying resolutions. Next, an innovative method is proposed, through which the high geometric accuracy of radar-imaging satellite sensors is exploited to update the spatial accuracy of any external geodata of lower spatial accuracy. Such external data is then used in the next two studies, which focus on cost-effective crop type mapping using Synthetic Aperture Radar (SAR) images. The resulting enhanced LULC maps present the annually changing crop types of the region alongside external, official geoinformation that is not retrievable from remote sensing sensors. The last two research studies deal with a single maize field, on which high resolution optical WorldView-2 images and experimental bistatic SAR observations from TanDEM-X are assessed and combined with ground measurements. As a result, this thesis shows that, depending on the AOI and the application, different resolution demands need to be fulfilled before LULC, crop type, and crop traits mapping can be performed with adequate accuracy. The spatial resolution needs to be adapted to the particularities of the AOI. Evaluation of the sensors showed that SAR sensors proved beneficial for the study objective. Processing the SAR images is complicated, and the images are unintuitive at first sight. However, the advantage of SAR sensors is that they work even in cloudy conditions. This results in an increased temporal resolution, which is particularly important for monitoring the highly dynamic agricultural area. Furthermore, the high geometric accuracy of the SAR images proved ideal for implementing the Multi-Data Approach (MDA). Thus information-rich external geodata could be used to lower the remote sensing resolution needs, improve the accuracy of the LULC-maps, and to provide enhanced LULC-maps. The first study of the maize field demonstrates the potential of the WorldView-2 data in predicting in-field biomass variations, and its increased accuracy when fused with plant height measurements. The second study shows the potential of the TanDEM-X Constellation (TDM) to retrieve plant height from space. LULC, crop type and information on the spatial distribution of biomass can thus be derived efficiently and with high accuracy from the combination of SAR, optical satellites and external geodata. The shown analyses for acquiring such geoinformation represent a high potential for helping to solve the future challenges of agricultural production

Polarimetric Synthetic Aperture Radar

This open access book focuses on the practical application of electromagnetic polarimetry principles in Earth remote sensing with an educational purpose. In the last decade, the operations from fully polarimetric synthetic aperture radar such as the Japanese ALOS/PalSAR, the Canadian Radarsat-2 and the German TerraSAR-X and their easy data access for scientific use have developed further the research and data applications at L,C and X band. As a consequence, the wider distribution of polarimetric data sets across the remote sensing community boosted activity and development in polarimetric SAR applications, also in view of future missions. Numerous experiments with real data from spaceborne platforms are shown, with the aim of giving an up-to-date and complete treatment of the unique benefits of fully polarimetric synthetic aperture radar data in five different domains: forest, agriculture, cryosphere, urban and oceans

QUANTIFYING GRASSLAND NON-PHOTOSYNTHETIC VEGETATION BIOMASS USING REMOTE SENSING DATA

Non-photosynthetic vegetation (NPV) refers to vegetation that cannot perform a photosynthetic function. NPV, including standing dead vegetation and surface plant litter, plays a vital role in maintaining ecosystem function through controlling carbon, water and nutrient uptake as well as natural fire frequency and intensity in diverse ecosystems such as forest, savannah, wetland, cropland, and grassland. Due to its ecological importance, NPV has been selected as an indicator of grassland ecosystem health by the Alberta Public Lands Administration in Canada. The ecological importance of NPV has driven considerable research on quantifying NPV biomass with remote sensing approaches in various ecosystems. Although remote images, especially hyperspectral images, have demonstrated potential for use in NPV estimation, there has not been a way to quantify NPV biomass in semiarid grasslands where NPV biomass is affected by green vegetation (PV), bare soil and biological soil crust (BSC). The purpose of this research is to find a solution to quantitatively estimate NPV biomass with remote sensing approaches in semiarid mixed grasslands. Research was conducted in Grasslands National Park (GNP), a parcel of semiarid mixed prairie grassland in southern Saskatchewan, Canada. Multispectral images, including newly operational Landsat 8 Operational Land Imager (OLI) and Sentinel-2A Multi-spectral Instrument (MSIs) images and fine Quad-pol Radarsat-2 images were used for estimating NPV biomass in early, middle, and peak growing seasons via a simple linear regression approach. The results indicate that multispectral Landsat 8 OLI and Sentinel-2A MSIs have potential to quantify NPV biomass in peak and early senescence growing seasons. Radarsat-2 can also provide a solution for NPV biomass estimation. However, the performance of Radarsat-2 images is greatly affected by incidence angle of the image acquisition. This research filled a critical gap in applying remote sensing approaches to quantify NPV biomass in grassland ecosystems. NPV biomass estimates and approaches for estimating NPV biomass will contribute to grassland ecosystem health assessment (EHA) and natural resource (i.e. land, soil, water, plant, and animal) management

Crop Growth Monitoring by Hyperspectral and Microwave Remote Sensing

Methoden und Techniken der Fernerkundung fungieren als wichtige Hilfsmittel im regionalen Umweltmanagement. Um diese zu optimieren, untersucht die folgende Arbeit sowohl die Verwendung als auch Synergien verschiedener Sensoren aus unterschiedlichen Wellenlängenbereichen. Der Fokus liegt auf der Modellentwicklung zur Ableitung von Pflanzenparametern aus fernerkundlichen Bestandsmessungen sowie auf deren Bewertung. Zu den verwendeten komplementären Fernerkundungssystemen zählen die Sensoren EO-1 Hyperion und ALI, Envisat ASAR sowie TerraSAR-X. Für die optischen Hyper- und Multispektralsysteme werden die Reflexion verschiedener Spektralbereiche sowie die Performanz der daraus abgeleiteten Vegetationsindizes untersucht und bewertet. Im Hinblick auf die verwendeten Radarsysteme konzentriert sich die Untersuchung auf Parameter wie Wellenlänge, Einfallswinkel, Radarrückstreuung und Polarisation. Die Eigenschaften verschiedener Parameterkombinationen werden hierbei dargestellt und der komplementäre Beitrag der Radarfernerkundung zur Wachstumsüberwachung bewertet. Hierzu wurden zwei Testgebiete, eines für Winterweizen in der Nordchinesischen Tiefebene und eines für Reis im Nordosten Chinas ausgewählt. In beiden Gebieten wurden während der Wachstumsperioden umfangreiche Feldmessungen von Bestandsparametern während der Satellitenüberflüge oder zeitnah dazu durchgeführt. Mit Hilfe von linearen Regressionsmodellen zwischen Satellitendaten und Biomasse wird die Sensitivität hyperspektraler Reflexion und Radarrückstreuung im Hinblick auf das Wachstum des Winterweizens untersucht. Für die optischen Daten werden drei verschiedene Modelvarianten untersucht: traditionelle Vegetationsindices berechnet aus Multispektraldaten, traditionelle Vegetationsindices berechnet aus Hyperspektraldaten sowie die Berechnung von Normalised Ratio Indices (NRI) basierend auf allen möglichen 2-Band Kombinationen im Spektralbereich zwischen 400 und 2500 nm. Weiterhin wird die gemessene Biomasse mit der gleichpolarisierten (VV) C-Band Rückstreuung des Envisat ASAR Sensors linear in Beziehung gesetzt. Um den komplementären Informationsgehalt von Hyperspektral und Radardaten zu nutzen, werden optische und Radardaten für die Parameterableitung kombiniert eingesetzt. Das Hauptziel für das Reisanbaugebiet im Nordosten Chinas ist das Verständnis über die kohärente Dualpolarimetrische X-Band Rückstreuung zu verschiedenen phänologischen Wachstumsstadien. Hierfür werden die gleichpolarisierte TerraSAR-X Rückstreuung (HH und VV) sowie abgeleitete polarimetrische Parameter untersucht und mit verschiedenen Ebenen im Bestand in Beziehung gesetzt. Weiterhin wird der Einfluss der Variation von Einfallswinkel und Auflösung auf die Bestandsparameterableitung quantifiziert. Neben der Signatur von HH und VV ermöglichen vor allem die polarimetrischen Parameter Phasendifferenz, Ratio, Koherenz und Entropy-Alpha die Bestimmung bestimmter Wachstumsstadien. Die Ergebnisse der Arbeit zeigen, dass die komplementären Fernerkundungssysteme Optik und Radar die Ableitung von Pflanzenparametern und die Bestimmung von Heterogenitäten in den Beständen ermöglichen. Die Synergien diesbezüglich müssen auch in Zukunft weiter untersucht werden, da neue und immer variablere Fernerkundungssysteme zur Verfügung stehen werden und das Umweltmanagement weiter verbessern können

A Novel Phenology Based Feature Subset Selection Technique Using Random Forest for Multitemporal PolSAR Crop Classification

Feature selection techniques intent to select a subset of features that minimizes redundancy and maximizes relevancy for classification problems in machine learning. Standard methods for feature selection in machine learning seldom take into account the domain knowledge associated with the data. Multitemporal crop classification studies with full-polarimetric synthetic aperture radar (PolSAR) data ought to consider the changes in the scattering mechanisms with their phenological growth stages. Hence, it is desirable to incorporate these changes while determining a feature subset for classification. In this study, a random forest (RF) based feature selection technique is proposed that takes into account the changes in the physical scattering mechanism with crop phenological stages for multitemporal PolSAR classification. The partial probability plot, which is an attribute of RF, provides information about the marginal effect of a polarimetric parameter on the desired crop class. Moreover, it is used to identify the specific range of a parameter where the probability of the presence of a particular crop class is high. The proposed technique identifies features that change significantly with crop phenology. The selected features are the ones whose ranges show maximum separation amongst crop classes. Additionally, the feature subset is refined by eliminating correlated features. The E-SAR L-band dataset of the AgriSAR-2006 campaign over the Demmin test site in Germany is used in this study. The classification accuracy using the novel feature selection technique is 99.12%. This is nominally better than using the features obtained from a standard feature selection method used in RF, such as mean decrease Gini (98.73%) and mean decrease accuracy (98.68%) that do not take into account the information based on crop phenology.This work was supported in part by the Spanish Ministry of Economy, Industry and Competitiveness, in part by the State Agency of Research (AEI), and in part by the European Funds for Regional Development under Projects TIN2014-55413-C2-2-P and TEC2017-85244-C2-1-P

Polarimetric Synthetic Aperture Radar, Principles and Application

Demonstrates the benefits of the usage of fully polarimetric synthetic aperture radar data in applications of Earth remote sensing, with educational and development purposes. Includes numerous up-to-date examples with real data from spaceborne platforms and possibility to use a software to support lecture practicals. Reviews theoretical principles in an intuitive way for each application topic. Covers in depth five application domains (forests, agriculture, cryosphere, urban, and oceans), with reference also to hazard monitorin