We compared 10 established and 2 new satellite reflectance algorithms 36 for estimating chlorophyll-a (Chl-a) in a temperate reservoir in southwest Ohio 37 using coincident hyperspectral aircraft imagery and dense coincident surface 38 observations collected within one hour of image acquisition to develop simple 39 proxies for algal blooms in water bodies sensitive to algal blooms (especially toxic 40 or harmful algal blooms (HABs)) and to facilitate portability between multispectral 41 satellite imagers for regional algal bloom monitoring. All algorithms were 42 compared with narrow band hyperspectral aircraft images. These images were 43 subsequently upscaled spectrally and spatially to simulate 5 current and near future 44 satellite imaging systems. Established and new Chl-a algorithms were then applied 45 to the synthetic satellite images and compared to coincident surface observations of 46Chl-a collected from 44 sites within one hour of aircraft acquisition of the imagery. 47We found several promising algorithm/satellite imager combinations for routine 48Chl-a estimation in smaller inland water bodies with operational and near-future 49 satellite systems. The CI, MCI, FLH, NDCI, 2BDA and 3 BDA Chl-a algorithms 50 worked well with CASI imagery. The NDCI, 2BDA, and 3BDA Chl-a algorithms 51 worked well with simulated WorldView-2 and 3, Sentinel-2, and MERIS-like 52 imagery. NDCI was the most widely applicable Chl-a algorithm with good 53 performance for CASI, WorldView 2 and 3, Sentinel-2 and MERIS-like imagery 54 and limited performance with MODIS imagery. A new fluorescence line height 55 "greenness" algorithm yielded the best Chl-a estimates with simulated Landsat-8 56 imagery. 57 ARTICLE INFO 58 Article history: 59 Received ….. 60 Submission to Remote Sensing of Environment 3 Keywords: chorophyll-a, algal bloom, harmful algal bloom, algorithm, satellite, 61 hyperspectral, multispectral 62 63 64 65

Abstract:Previous research studies have demonstrated that the relationship between remote sensing-derived parameters and aboveground biomass (AGB) could vary across different species types. However, there are few studies that calibrate reliable statistical models for mangrove AGB. This study quantifies the differences of accuracy in AGB estimation between the results obtained with and without the consideration of species types using Worldview-2 images and field surveys. A Back Propagation Artificial Neural Network (BP ANN) based model is developed for the accurate estimation of uneven-aged and dense mangrove forest biomass. The contributions of the input variables are further quantified using a "Weights" method based on BP ANN model. Two types of mangrove species, Sonneratia apetala (S. apetala) and Kandelia candel (K. candel), are examined in this study. Results show that the species type information is the most important variable for AGB estimation, and the red edge band and the associated vegetation indices from WorldView-2 images are more sensitive to mangrove AGB than other bands and vegetation indices. The RMSE of biomass estimation at the incorporation of species as a dummy variable is 19.17% OPEN ACCESSRemote Sens. 2015, 7 12193 lower than that of the mixed species level. The results demonstrate that species type information obtained from the WorldView-2 images can significantly improve of the accuracy of the biomass estimation.

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Editors note: For easy download the posted pdf of the State of the Climate for 2014 is a very low-resolution file. A high-resolution copy of the report is available by clicking here. Please be patient as it may take a few minutes for the high-resolution file to download.

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Dike-ponds have experienced significant changes in the Pearl River Delta region over the past several decades, especially since China’s economic reform, which has seriously affected the construction of ecological environments. In order to monitor the evolution of dike-ponds, in this study we use multi-source remote sensing images from 1978 to 2016 to extract dike-ponds in several periods using the nearest neighbor classification method. A corresponding area weighted dike-pond invasion index (AWDII) is proposed to describe the spatial evolution of dike-ponds, both qualitatively and quantitatively. Furthermore, the evolution mechanisms of dike-ponds are determined, which can be attributed to both natural conditions and human factors. Our results show that the total area of dike-ponds in 2016 was significantly reduced and fragmentation had increased compared with the situation in 1978. The AWDII reveals that Shunde District has experienced three main phases, including steady development, rapid invasion and a reduction of invasion by other land use types. Most dike-ponds have now converted into built-up areas, followed by cultivated lands, mainly due to government policies, rural area depopulation, and river networks within Shunde. Our study indicates that the AWDII is applicable towards the evaluation of the dynamic changes of dike-ponds. The rational development, and careful protection, of dike-ponds should be implemented for better land and water resource management.

Existing multi-exposure fusion (MEF) algorithms for gray images under low-illumination cannot preserve details in dark and highlighted regions very well, and the fusion image noise is large. To address these problems, an MEF method is proposed. First, the latent low-rank representation (LatLRR) is used on low-dynamic images to generate low-rank parts and saliency parts to reduce noise after fusion. Then, two components are fused separately in Laplace multi-scale space. Two different weight maps are constructed according to features of gray images under low illumination. At the same time, an energy equation is designed to obtain the optimal ratio of different weight factors. An improved guided filtering based on an adaptive regularization factor is proposed to refine the weight maps to maintain spatial consistency and avoid artifacts. Finally, a high dynamic image is obtained by the inverse transform of low-rank part and saliency part. The experimental results show that the proposed method has advantages both in subjective and objective evaluation over state-of-the-art multi-exposure fusion methods for gray images under low-illumination imaging.

Downscaling microwave soil moisture (SM) with optical/thermal remote sensing data has considerable application potential. Spatial correlations between SM and land surface temperature (LST) or LST-derived SM indexes (SMIs) are vital to the current optical/thermal and microwave fusion downscaling methods. In this study, the spatial correlations were evaluated at the same spatial scale using SMAPVEX12 SM data and MODIS day/night LST products. LST-derived SMIs was calculated using NLDAS-2 gridded meteorological data with conventional trapezoid and two-stage trapezoid models. Results indicated that (1) SM agrees better with daytime LST than the nighttime or the day-night differential LST; (2) the daytime LSTs on Aqua and Terra present very similar spatial agreement with SM and they have very similar performances as downscaling factors in simulating SM; (3) decoupling effect among SM, LST, and LST-derived SMIs occurs not only in very wet but also in very dry condition; and (4) the decoupling effect degrades the performance of LST as a downscaling factor. The future downscaling algorithms should consider net surface radiation and soil type to tackle the decoupling effect.