Remote Sens.2015, 7(2), 1504-1528; doi:10.3390/rs70201504 - published 29 January 2015 Show/Hide Abstract
Abstract: Satellite precipitation products (SPPs) potentially constitute an alternative to sparse rain gauge networks for assessing the spatial distribution of precipitation. However, applications of these products are still limited due to the lack of robust quality assessment. This study compares daily, monthly, seasonal, and annual rainfall amount at 342 rain gauges over Malaysia to estimations using five SPPs (3B42RT, 3B42V7, GPCP-1DD, PERSIANN-CDR, and CMORPH) and a ground-based precipitation product (APHRODITE). The performance of the precipitation products was evaluated from 2003 to 2007 using continuous (RMSE, R2, ME, MAE, and RB) and categorical (ACC, POD, FAR, CSI, and HSS) statistical approaches. Overall, 3B42V7 and APHRODITE performed the best, while the worst performance was shown by GPCP-1DD. 3B42RT, 3B42V7, and PERSIANN-CDR slightly overestimated observed precipitation by 2%, 4.7%, and 2.1%, respectively. By contrast, APHRODITE and CMORPH significantly underestimated precipitations by 19.7% and 13.2%, respectively, whereas GPCP-1DD only slightly underestimated by 2.8%. All six precipitation products performed better in the northeast monsoon than in the southwest monsoon. The better performances occurred in eastern and southern Peninsular Malaysia and in the north of East Malaysia, which receives higher rainfall during the northeast monsoon, whereas poor performances occurred in the western and dryer Peninsular Malaysia. All precipitation products underestimated the no/tiny (<1 mm/day) and extreme (≥20 mm/day) rainfall events, while they overestimated low (1–20 mm/day) rainfall events. 3B42RT and 3B42V7 showed the best ability to detect precipitation amounts with the highest HSS value (0.36). Precipitations during flood events such as those which occurred in late 2006 and early 2007 were estimated the best by 3B42RT and 3B42V7, as shown by an R2 value ranging from 0.49 to 0.88 and 0.52 to 0.86, respectively. These results on SPPs’ uncertainties and their potential controls might allow sensor and algorithm developers to deliver better products for improved rainfall estimation and thus improved water management.
Remote Sens.2015, 7(2), 1482-1503; doi:10.3390/rs70201482 - published 29 January 2015 Show/Hide Abstract
Abstract: Agriculture is a highly dynamic process in space and time, with many applications requiring data with both a relatively high temporal resolution (at least every 8 days) and fine-to-moderate (FTM < 100 m) spatial resolution. The relatively infrequent revisit of FTM optical satellite observatories coupled with the impacts of cloud occultation have translated into a barrier for the derivation of agricultural information at the regional-to-global scale. Drawing upon the Group on Earth Observations Global Agricultural Monitoring (GEOGLAM) Initiative’s general satellite Earth observation (EO) requirements for monitoring of major production areas, Whitcraft et al. (this issue) have described where, when, and how frequently satellite data acquisitions are required throughout the agricultural growing season at 0.05°, globally. The majority of areas and times of year require multiple revisits to probabilistically yield a view at least 70%, 80%, 90%, or 95% clear within eight days, something that no present single FTM optical observatory is capable of delivering. As such, there is a great potential to meet these moderate spatial resolution optical data requirements through a multi-space agency/multi-mission constellation approach. This research models the combined revisit capabilities of seven hypothetical constellations made from five satellite sensors—Landsat 7 Enhanced Thematic Mapper (Landsat 7 ETM+), Landsat 8 Operational Land Imager and Thermal Infrared Sensor (Landsat 8 OLI/TIRS), Resourcesat-2 Advanced Wide Field Sensor (Resourcesat-2 AWiFS), Sentinel-2A Multi-Spectral Instrument (MSI), and Sentinel-2B MSI—and compares these capabilities with the revisit frequency requirements for a reasonably cloud-free clear view within eight days throughout the agricultural growing season. Supplementing Landsat 7 and 8 with missions from different space agencies leads to an improved capacity to meet requirements, with Resourcesat-2 providing the largest incremental improvement in requirements met. The best performing constellation can meet 71%–91% of the requirements for a view at least 70% clear, and 45%–68% of requirements for a view at least 95% clear, varying by month. Still, gaps exist in persistently cloudy regions/periods, highlighting the need for data coordination and for consideration of active EO for agricultural monitoring. This research highlights opportunities, but not actual acquisition rates or data availability/access; systematic acquisitions over actively cropped agricultural areas as well as a policy which guarantees continuous access to high quality, interoperable data are essential in the effort to meet EO requirements for agricultural monitoring.
Remote Sens.2015, 7(2), 1461-1481; doi:10.3390/rs70201461 - published 29 January 2015 Show/Hide Abstract
Abstract: Global agricultural monitoring utilizes a variety of Earth observations (EO) data spanning different spectral, spatial, and temporal resolutions in order to gather information on crop area, type, condition, calendar, and yield, among other applications. Categorical requirements for space-based monitoring of major agricultural production areas have been articulated based on best practices established by the Group on Earth Observation’s (GEO) Global Agricultural Monitoring Community (GEOGLAM) of Practice, in collaboration with the Committee on Earth Observation Satellites (CEOS). We present a method to transform generalized requirements for agricultural monitoring in the context of GEOGLAM into spatially explicit (0.05°) Earth observation (EO) requirements for multiple resolutions of data. This is accomplished through the synthesis of the necessary remote sensing-based datasets concerning where (crop mask, when (growing calendar, and how frequently imagery is required (considering cloud cover impact throughout the agricultural growing season. Beyond this provision of the framework and tools necessary to articulate these requirements, investigated in depth is the requirement for reasonably clear moderate spatial resolution (10–100 m) optical data within 8 days over global within-season croplands of all sizes, a data type prioritized by GEOGLAM and CEOS. Four definitions of “reasonably clear” are investigated: 70%, 80%, 90%, or 95% clear. The revisit frequency required (RFR) for a reasonably clear view varies greatly both geographically and throughout the growing season, as well as with the threshold of acceptable clarity. The global average RFR for a 70% clear view within 8 days is 3.9–4.8 days (depending on the month), 3.0–4.1 days for 80% clear, 2.2–3.3 days for 90% clear, and 1.7–2.6 days for 95% clear. While some areas/times of year require only a single revisit (RFR = 8 days) to meet their reasonably clear requirement, generally the RFR, regardless of clarity threshold, is below to greatly below the 8 day mark, highlighting the need for moderate resolution optical satellite systems or constellations with revisit capabilities more frequent than 8 days. This analysis is providing crucial input for data acquisition planning for agricultural monitoring in the context of GEOGLAM.
Remote Sens.2015, 7(2), 1441-1460; doi:10.3390/rs70201441 - published 29 January 2015 Show/Hide Abstract
Abstract: During the spring of 2011 an unprecedented “Super” algal bloom formed in the Indian River Lagoon (IRL), with Chlorophyll a (Chl a) concentrations over eight times the historical mean in some areas and lasted for seven months across the IRL. The European Space Agency’s MEdium Resolution Imaging Spectrometer (MERIS) platform provided multispectral data at 665 and 708 nm, which was used to quantify the phytoplankton Chl a by fluorescence while minimizing the effects of other water column constituents. The three objectives were to: (1) calibrate and validate two Chl a algorithms using all available MERIS data of the IRL from 2002 to 2012; (2) determine the accuracy of the algorithms estimation of Chl a before, during, and after the 2011 super bloom; and (3) map the 2011 algal bloom using the Chl a algorithm that was proven to be effective in other similar estuaries. The chosen algorithm, Normalized Difference Chlorophyll Index (NDCI), was positively correlated with the in-situ measurements, with an R2 value of 0.798. While there was a significant (62.9 ± 25%) underestimation of Chl a using MERIS NDCI, the underestimation appears to be consistent across the data and mostly in the estimations of lower concentrations, suggesting that a qualitative or ratio analysis is still valid. Analysis of the application of the NDCI processed MERIS data provided additional insights that the in-situ measurements were unable to record. The time series MERIS Chl a maps along with in-situ water quality monitoring data depicted that the 2011 IRL bloom started after a heavy rainfall in March 2011 and peaked in October 2011 after a decrease in temperature. The bloom collapse also coincided with heavy rainfall and rapidly decreasing temperatures and salinity through October to November 2011.
Remote Sens.2015, 7(2), 1422-1440; doi:10.3390/rs70201422 - published 29 January 2015 Show/Hide Abstract
Abstract: Defense Meteorological Satellite Program/Operational Linescan System (DMSP-OLS) nighttime light has proved to be an effective tool to monitor human activities, especially in mapping urban areas. However, the inherent defects of DMSP-OLS light including saturation and blooming effects remain to be tackled. In this study, the Normalized Difference Vegetation Index (NDVI) product of the Moderate-resolution Imaging Spectroradiometer/Normalized Difference Vegetation Index 1-Month (MODND1M), the temperature product of Moderate-resolution Imaging Spectroradiometer/Land Surface Temperature 1-Month (MODLT1M) and DMSP-OLS light were integrated to establish the Vegetation Temperature Light Index (VTLI), aiming at weakening the saturation and blooming effects of DMSP-OLS light. In comparison with DMSP-OLS nighttime light, this new methodology achieved the following improvements: (1) the high value (30%–100%) range of VTLI was concentrated in the urban areas; (2) VTLI could effectively enhance the variation of DMSP-OLS light, especially in the urban center; and (3) VTLI reached convergence faster than Vegetation Adjusted Normalized Urban Index (VANUI). Results showed that the urban areas extracted by VTLI were closer to those from Landsat TM images with the accuracy of kappa coefficients in Beijing (0.410), Shanghai (0.718), Lanzhou (0.483), and Shenyang (0.623), respectively. Thus, it can be concluded that the proposed index is able to serve as a favorable option for urban areas mapping.
Remote Sens.2015, 7(2), 1397-1421; doi:10.3390/rs70201397 - published 29 January 2015 Show/Hide Abstract
Abstract: The validation process for a moderate resolution leaf area index (LAI) product (i.e., MODIS) involves the creation of a high spatial resolution LAI reference map (Lai-RM), which when scaled to the moderate LAI resolution (i.e., > 1 km) allows for comparison and analysis with this LAI product. This research addresses two major sources of uncertainty in the creation of the LAI-RM: (1) the uncertainty associated with the indirect in situ optical measurements of southeastern United States needle-leaf LAI and (2) the uncertainty in the process of classifying land cover (LC). Uncertainty within the loblolly pine (Pinus taeda) in situ data collection was highest for the assessment of the plant area index (PAI), Le (27.2%), and the woody-to-total ratio, α, (30.6%). The needle-to-shoot ratio, λE, and the element clumping index, ΩE, contributed 14.9% and 9.3%, respectively, to the uncertainty in the calculation of LAI. Combining LC differences (3.4%) with the uncertainty within the loblolly pine component resulted in doubling the LAI-RM variability (σ = 0.50 to σ = 0.97) at the 1 km2 validation site located in Appomattox, Virginia, USA.