article Vegetation indices are important remotely sensed metrics for ecosystem monitoring and land surface process assessment, among which Normalized Difference Vegetation Index (NDVI) has been most widely used. The newly launched Landsat 8 Operational Land Imager (OLI) sensor, together with its predecessor Landsat 7 Enhanced Thematic Mapper Plus (ETM+), provides continuous earth observations with an 8-day interval. The design improvements of the new sensor, including narrower near-infrared waveband, higher signal-to-noise ratio (SNR), and greater radiometric sensitivity highlight the need for investigating the land surface observation properties, especially its consistency with data from its predecessors and other satellite sensors. This study aims to evaluate the characteristics of Landsat 8 OLI-derived NDVI against Landsat 7 ETM+ by cross-comparison and by comparing with Moderate Resolution Imaging Spectroradiometer (MODIS) and Geostationary Ocean Color Imager (GOCI)-derived NDVIs as well as in-situ NDVI measurements. Simulations of Top of Atmosphere (TOA) reflectance and surface reflectance of broadleaf trees and water were conducted for Landsat 8 OLI, Landsat 7 ETM+, and MODIS in order to evaluate the impact of band pass difference on NDVI calculation. Four consecutive pairs of Landsat 8 OLI and Landsat 7 ETM+ data over China and Korea were examined, and NDVIs derived from TOA reflectance and surface reflectance by three atmospheric correction methods were evaluated. Both simula- tions and comparisons showed that NDVIs derived from atmospherically-corrected surface reflectance had good consistency, while the simulation showed that the agreement varied with atmospheric characteristics. The four pairs of Landsat 8 OLI and Landsat 7 ETM+ NDVI had a mean bias error within ±0.05, and R 2 from 0.84 to 0.98.

Characteristics of Landsat 8 OLI-derived NDVI by comparison with

The severity of plant diseases, traditionally the proportion of the plant tissue exhibiting symptoms, is a key quantitative variable to know for many diseases and is prone to error. Good quality disease severity data should be accurate (close to the true value). Earliest quantification of disease severity was by visual estimates. Sensor-based image analysis including visible spectrum and hyperspectral and multispectral sensors are established technologies that promise to substitute, or complement visual ratings. Indeed, these technologies have measured disease severity accurately under controlled conditions but are yet to demonstrate their full potential for accurate measurement under field conditions. Sensor technology is advancing rapidly, and artificial intelligence may help overcome issues for automating severity measurement under hyper-variable field conditions. The adoption of appropriate scales, training, instruction and aids (standard area diagrams) has contributed to improved accuracy of visual estimates. The apogee of accuracy for visual estimation is likely being approached, and any remaining increases in accuracy are likely to be small. Due to automation and rapidity, sensor-based measurement offers potential advantages compared with visual estimates, but the latter will remain important for years to come. Mobile, automated sensor-based systems will become increasingly common in controlled conditions and, eventually, in the field for measuring plant disease severity for the purpose of research and decision making.

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From visual estimates to fully automated sensor-based measurements of plant disease severity: status and challenges for improving accuracy

Two multispectral scanner subsystems (MSS) have been fabricated and tested by an American aerospace company for the NASA LANDSAT program. One MSS, designated the protoflight (PF) model, was integrated into the LANDSAT-4 spacecraft, which was launched on July 16, 1982. The second, designated the flight (F) model, has been integrated into the LANDSAT-4 backup satellite, which is scheduled for possible launch in 1985. Each MSS has four bands in the reflective portion of the electromagnetic spectrum. The engineering test deta which were collected included channel-by-channel spectral response curves. A description of the test procedure is included in Appendix A. This document is to make available to the LANDSAT user community data on the spectral characteristics of the two sensors. The LANDSAT-4 PF and F scanners were found to be essentially identical in mean spectral response.

Spectral characterization of the LANDAT-4 MSS sensors

Grasslands in Aso caldera, Japan, are a type of land cover that is integral for biodiversity, tourist attractions, agriculture, and groundwater recharge. However, the area of grasslands has been decreasing in recent years as a result of natural disasters and changes in social conditions surrounding agriculture. The question of whether the decrease in spring water discharge in Aso caldera is related to the decrease in grasslands remains unanswered. To clarify this relationship, a water circulation model that considers land covers with different hydrological features is needed. In this study, by integrating Normalized Difference Vegetation Index (NDVI) time series and Geographic Information System (GIS) data, we generated land cover maps from the past (in 1981 and 1991) to the present (in 2015 and 2016), before and after the 2016 Kumamoto earthquake, and then for the future (in the 2040s); these maps formed the dataset for building a water circulation model. The results show that the area of grasslands, which are reported to have a higher groundwater recharge rate than that of forests, in 2016 had decreased to 68% of the area in 1981 as a result of afforestation and transformation into forests, as well as landslides induced by the earthquake. The area of grasslands is predicted to further drop to 60% by the 2040s. On the other hand, the area of forests (conifers and hardwoods) in 2016 had increased by 119% relative to that in 1981 because of the transformation of grasslands into forests, although these areas decreased as a result of landslides due to the 2016 Kumamoto earthquake. Quantification of groundwater recharge from grasslands and forests using the land cover maps generated for 1981, 1996, 2015, and 2016 shows that the annual increase in precipitation in these years significantly affected groundwater recharge; these effects were greater than those associated with the type of land cover. Thus, the groundwater recharge increased, despite the decrease in grasslands. However, when constant precipitation was assumed, the groundwater recharge presented a decreasing trend, indicating the importance of maintaining and conserving grasslands from the viewpoint of groundwater conservation.

https://www.mdpi.com/1660-4601/17/18/6605/pdf

Land Cover Classification by Integrating NDVI Time Series and GIS Data to Evaluate Water Circulation in Aso Caldera, Japan.

The Landsat 1–5 multispectral scanner system (MSS) collected records of land surface mainly during 1972–1992. Investigations on MSS have been relatively limited compared with the numerous investigations on its successors, such as Thematic Mapper (TM) and Enhanced TM Plus (ETM+). The benefits of the Landsat program are not fully accomplished without the inclusion of MSS archives. Investigations on the Landsat 1–5 MSS channel reflectance characteristics wereperformed followed by derived vegetation spectral indices and the Tasseled Cap (TC) transformed features mainly using a collection of synthesized records. On average, the Landsat 4 MSS is generally comparable to the Landsat 5 MSS. The Landsat 1–3 MSSs show disagreement in channel reflectance compared with the Landsat 5 MSS, especially for the red channel (600–700 nm) and the near-infrared channel (700–800 nm). Meanwhile, the relative differences for vegetation spectral indices of the Landsat 3 MSS are mainly from −16% to −5% with the median about −11.5%, while those of the Landsat 2 MSS are mainly from −15% to −7%. Cross-validation tests and two case applications suggested that between-sensor consistency was improved generally through the transformation models generated by ordinary least-squares regression. To improve the consistency of the vegetation indices and the TC greenness, direct strategy employing respective transformation models was more effective than calculations based on the transformed channel reflectance. Considering the shortages of the Landsat MSS archives, further efforts are needed to improve its comparability with observations by other successive Landsat sensors.

Characterization of MSS Channel Reflectance and Derived Spectral Indices for Building Consistent Landsat 1–5 Data Record

Landsat 4-5, built at the same time and with the same design, carrying the Multispectral Scanner System (MSS) and the Thematic Mapper (TM) simultaneously, jointly provided observation service for about 30 years (1982–2013). Considering the importance of data continuity for time series analyses, investigations on the continuity of the Landsat 4-5 MSS and TM are required. In this paper, characterization differences between the Landsat 4-5 MSS and TM were initially discussed using the synthesized reflectance records generated from a collection of Hyperion hyperspectral profiles which were well calibrated and widely distributed. The difference in near-infrared region mostly contributed to the difference in normalized difference vegetation index (NDVI) between MSS and TM, while the between-sensor difference in red spectrum was relatively minor. Models for transforming MSS NDVI to TM NDVI were proposed, and validated subsequently through cross-validation tests. Furthermore, effectiveness of the transformation models was investigated using eight synchronous observation pairs of the Landsat 5 MSS and TM. On average, the univariate models through ordinary least squares regression (OLS) regression resulted in a decrease about 10% of the median relative difference (MdRD). Meanwhile, the bivariate models improved the NDVI comparability in most cases, especially when the transformation models through ridge regression were implemented. The univariate model through OLS regression could be the only solution for cases when problems of data quality are encountered (e.g., problem in the MSS near-infrared channel (800–1000 nm)). In conclusion, the findings on NDVI transformation models from MSS to TM are valuable for reference, because of the collection of diverse Hyperion hyperspectral profiles used.

Normalized Difference Vegetation Index Continuity of the Landsat 4-5 MSS and TM: Investigations Based on Simulation

Comprehensive investigations on the between-sensor comparability among Landsat sensors have been relatively limited compared with the increasing use of multi-temporal Landsat records in time series analyses. More seriously, the sensor-related difference has not always been considered in applications. Accordingly, comparisons were conducted among all Landsat sensors available currently, including Multispectral Scanner (MSS), Thematic Mappers (TM), Enhanced Thematic Mappers (ETM+), and Operational Land Imager (OLI)) in land cover mapping, based on a collection of synthesized, multispectral data. Compared to TM, OLI showed obvious between-sensor differences in channel reflectance, especially over the near infrared (NIR) and shortwave infrared (SWIR) channels, and presented positive bias in vegetation spectral indices. OLI did not always outperform TM and ETM+ in classification, which related to the methods used. Furthermore, the channels over SWIR of TM and its successors contributed largely to enhancement of inter-class separability and to improvement of classification. Currently, the inclusion of MSS data is confronted with significant challenges regarding the consistency of surface mapping. Considering the inconsistency among the Landsat sensors, it is applicable to generate a consistent time series of spectral indices through proper transformation models. Meanwhile, it suggests the generation of specific class(es) based on interest instead of including all classes simultaneously.

Inconsistency among Landsat Sensors in Land Surface Mapping: A Comprehensive Investigation Based on Simulation

The wide field of view (WFV) sensor as one of the imaging systems onboard Gaofen-l (GF-1) satellite acquires multispectral imagery with a spatial resolution of 16 m. The medium spatial resolution as well as high temporal resolution and wide swath makes the GF-1 WFV multispectral imagery more suitable for monitoring over large-scale surface. Currently, investigations mainly focusing on processing and application of the GF-1 WFV data have been widely reported. There are four GF-1 WFV sensors (namely including WFV1, WFV2, WFV3, and WFV4) designed with similar characterization, but having discrepancies among them. However, comparability among the four WFV sensors has not been discussed fully. In this paper, preliminary findings on comparison of the GF-1 WFV sensors are presented. Relatively significant discrepancies among the GF-1 WFV sensors are observed in spectral response function and the derived indicators (i.e. channel effective wavelength and the percentage of overlap) for the red and green channels, whereas the differences are less obvious for the blue channel. The between-sensor differences generally vary with the channel, which are also associated with the sensors in comparison. Overall, the WFV1 and the WFV2 show comparability in all channels (i.e. the blue, green, red, and near-infrared channels). Furthermore, the findings based on a collection of synthesized GF-1 WFV data suggest that a general linear model may not be sufficient to improve between-sensor comparability for all individual land use/cover classes, although it is effective to reduce the overall between-sensor difference. Potential uncertainty in channel reflectance introduced through transformation model should be taken into account for the applications followed by.

Chenxing Wang

Papers

Normalized Difference Vegetation Index Continuity of the Landsat 4-5 MSS and TM: Investigations Based on Simulation

Characterization of MSS Channel Reflectance and Derived Spectral Indices for Building Consistent Landsat 1–5 Data Record

Inconsistency among Landsat Sensors in Land Surface Mapping: A Comprehensive Investigation Based on Simulation

Preliminary comparisons among the Gaofen-1 wide field of view sensors