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The present study assesses the areal extent and health condition of the mangroves in Bhitarkanika along the east coast of India, using routine remote sensing spectral index – normalized difference vegetation index (NDVI). Selected Landsat-5/8 images from three decades (1991–2021) were used for the calculation of NDVI (mean, SD, range) values for the assessment of mangrove health. The results indicate that the mean NDVI value slightly decreased from 0.56 (1991) to 0.43 (2021). Out of the total mangrove area, unhealthy mangroves increased from 10.91 sq. km in 1991 to 38.26 sq. km by 2021, while moderately healthy mangroves decreased from 40.15 sq. km in 1991 to 12.98 sq. km by 2021, due to various anthropogenic activities. The study also identified specific positive gains in some parts of the mangrove (north, east, and some patches of the southwest) due to the effective implementation of afforestation and reforestation efforts through government and local initiatives.

Many time-series smoothing methods can be used for reducing noise and extracting plant phenological parameters from remotely-sensed data, but there is still no conclusive evidence in favor of one method over others. Here we use moderate-resolution imaging spectroradiometer (MODIS) derived normalized difference vegetation index (NDVI) to investigate five smoothing methods: Savitzky-Golay fitting (SG), locally weighted regression scatterplot smoothing (LO), spline smoothing (SP), asymmetric Gaussian function fitting (AG), and double logistic function fitting (DL). We use ground tower measured NDVI (10 sites) and gross primary productivity (GPP, 4 sites) to evaluate the smoothed satellite-derived NDVI time-series, and elevation data to evaluate phenology parameters derived from smoothed NDVI. The results indicate that all smoothing methods can reduce noise and improve signal quality, but that no single method always performs better than others. Overall, the local filtering methods (SG and LO) can generate very accurate results if smoothing parameters are optimally calibrated. If local calibration cannot be performed, cross validation is a way to automatically determine the smoothing parameter. However, this method may in some cases generate poor fits, and when calibration is not possible the function fitting methods (AG and DL) provide the most robust description of the seasonal dynamics.

Geostationary (GEO) satellite sensors provide earth observation data with a high temporal frequency and can complement low earth orbit (LEO) sensors in monitoring terrestrial vegetation. Consistency between GEO and LEO observation data is thus critical to the synergistic use of the sensors; however, mismatch between the sun–target–sensor viewing geometries in the middle-to-high latitude region and the sensor-specific spectral response functions (SRFs) introduce systematic errors into GEO–LEO products such as the Normalized Difference Vegetation Index (NDVI). If one can find a parameter in which the value is less influenced by geometric conditions and SRFs, it would be invaluable for the synergistic use of the multiple sensors. This study attempts to develop an algorithm to obtain such parameters (NDVI-based indices), which are equivalent to fraction of vegetation cover (FVC) computed from NDVI and endmember spectra. The algorithm was based on a linear mixture model (LMM) with automated computation of the parameters, i.e., endmember spectra. The algorithm was evaluated through inter-comparison between NDVI-based indices using off-nadir GEO observation data from the Himawari 8 Advanced Himawari Imager (AHI) and near-nadir LEO observation data from the Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) as a reference over land surfaces in Japan at middle latitudes. Results showed that scene-dependent biases between the NDVI-based indices of sensors were −0.0004±0.018 (mean ± standard deviation). Small biases were observed in areas in which the fractional abundances of vegetation were likely less sensitive to the view zenith angle. Agreement between the NDVI-based indices of the sensors was, in general, better than the agreement between the NDVI values. Importantly, the developed algorithm does not require regression analysis for reducing biases between the indices. The algorithm should assist in the development of algorithms for performing inter-sensor translations of vegetation indices using the NDVI-based index as a parameter.

The recent deployment of ESA’s Sentinel operational satellites has established a new paradigm for remote sensing applications. In this context, Sentinel-1 radar images have made it possible to retrieve surface soil moisture with a high spatial and temporal resolution. This paper presents two methodologies for the retrieval of soil moisture from remotely-sensed SAR images, with a spatial resolution of 100 m. These algorithms are based on the interpretation of Sentinel-1 data recorded in the VV polarization, which is combined with Sentinel-2 optical data for the analysis of vegetation effects over a site in Urgell (Catalunya, Spain). The first algorithm has already been applied to observations in West Africa by Zribi et al., 2008, using low spatial resolution ERS scatterometer data, and is based on change detection approach. In the present study, this approach is applied to Sentinel-1 data and optimizes the inversion process by taking advantage of the high repeat frequency of the Sentinel observations. The second algorithm relies on a new method, based on the difference between backscattered Sentinel-1 radar signals observed on two consecutive days, expressed as a function of NDVI optical index. Both methods are applied to almost 1.5 years of satellite data (July 2015–November 2016), and are validated using field data acquired at a study site. This leads to an RMS error in volumetric moisture of approximately 0.087 m3/m3 and 0.059 m3/m3 for the first and second methods, respectively. No site calibrations are needed with these techniques, and they can be applied to any vegetation-covered area for which time series of SAR data have been recorded.

Accurate and frequent updates of surface water have been made possible by remote sensing technology. Index methods are mostly used for surface water estimation which separates the water from the background based on a threshold value. Generally, the threshold is a fixed value, but can be challenging in the case of environmental noise, such as shadow, forest, built-up areas, snow, and clouds. One such challenging scene can be found in Nepal where no such evaluation has been done. Taking that in consideration, this study evaluates the performance of the most widely used water indices: Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), and Automated Water Extraction Index (AWEI) in a Landsat 8 scene of Nepal. The scene, ranging from 60 m to 8848 m, contains various types of water bodies found in Nepal with different forms of environmental noise. The evaluation was conducted based on measures from a confusion matrix derived using validation points. Comparing visually and quantitatively, not a single method was able to extract surface water in the entire scene with better accuracy. Upon selecting optimum thresholds, the overall accuracy (OA) and kappa coefficient (kappa) was improved, but not satisfactory. NDVI and NDWI showed better results for only pure water pixels, whereas MNDWI and AWEI were unable to reject snow cover and shadows. Combining NDVI with NDWI and AWEI with shadow improved the accuracy but inherited the NDWI and AWEI characteristics. Segmenting the test scene with elevations above and below 665 m, and using NDVI and NDWI for detecting water, resulted in an OA of 0.9638 and kappa of 0.8979. The accuracy can be further improved with a smaller interval of categorical characteristics in one or multiple scenes.

In this study, the updated Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) dataset for growing season (April to October), which can better reflect the vegetation vigor, was used to investigate the interannual variations in NDVI and its relationship with climatic factors, in order to preliminarily understand the climate impact on vegetation and provide theoretical basis for the response of ecosystem to climate change. Multivariate linear regression models, including the Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR), were adopted to analyze the correlation between NDVI and climatic factors (temperature and precipitation) together. Average growing-season NDVI significantly increased at a rate of 0.0015/year from 1982 to 2013, larger than several regions in China. On the whole, its relationship with temperature is positive and also stronger than precipitation, which indicated that temperature may be a limiting factor for the vegetation growth in the Karst region. Moreover, the correlation coefficients between grassland NDVI and climatic factors are the largest. Under the background of NDVI increasing trend from 1982 to 2013, the period of 2009–2012 was chosen to investigate the influencing factors of a sharp decline in NDVI. It can be found that the reduced temperature and solar radiation, caused by the increase in cloud cover and precipitation, may play important roles in the vegetation cover change. All in all, the systematic research on the interannual variations of growing-season NDVI and its relationship with climate revealed the heterogeneity and variability in the complicated climate change in the Karst ecosystem for the study area. It is the Karst characteristics that hinder obtaining more representative conclusions and tendencies in this region. Hence, more attention should be paid to promoting Karst research in the future.

Disadvantaged groups worldwide, such as low-income and racially/ethnically minoritized people, experience worse health outcomes than more privileged groups, including wealthier and white people. Such health disparities are a major public health issue in several countries around the world. In this systematic review, we examine whether green space shows stronger associations with physical health for disadvantaged groups than for privileged groups. We hypothesize that disadvantaged groups have stronger protective effects from green space because of their greater dependency on proximate green space, as they tend to lack access to other health-promoting resources. We use the preferred reporting items for systematic reviews and meta-analyses (PRISMA) method and search five databases (CINAHL, Cochrane, PubMed, Scopus, and Web of Science) to look for articles that examine whether socioeconomic status (SES) or race/ethnicity modify the green space-health associations. Based on this search, we identify 90 articles meeting our inclusion criteria. We find lower-SES people show more beneficial effects than affluent people, particularly when concerning public green spaces/parks rather than green land covers/greenness. Studies in Europe show stronger protective effects for lower-SES people versus higher-SES people than do studies in North America. We find no notable differences in the protective effects of green space between racial/ethnic groups. Collectively, these results suggest green space might be a tool to advance health equity and provide ways forward for urban planners, parks managers, and public health professionals to address health disparities.

We conducted a study in a large almond farm in Merced County, California, to monitor water status by using high-resolution multi-spectral imagery accquired by a Small Unmanned Aerial System (sUAS). More specifically, we would like to predict Stem Water Potential (SWP) via canopy Normalized Difference Vegetation Index (NDVI). During 2014, an aircraft equipped with multi-spectral cameras flew over the orchard weekly throughout the growing season. At the same time, SWP was measured for the sample trees under five different water treatment levels. Instead of averaging pixels in an orchard level, a block level or a canopy level, pixels were analyzed in the sub-canopy level to obtain canopy NDVI. An improved correlation between SWP and canopy NDVI was obtained by applying lower NDVI threshold. The relationship between SWP and canopy NDVI was also discussed at different growing stages–fruit development and post-harvest. However, tests of equality of distribution indicated that canopy NDVI distributions from different flights within a day were significantly different. Therefore, further calibration regarding the effects of solar motion on canopy NDVI is necessary.

The insurance hypothesis, stating that biodiversity can increase ecosystem stability, has received wide research and political attention. Recent experiments suggest that climate change can impact how plant diversity influences ecosystem stability, but most evidence of the biodiversity–stability relationship obtained to date comes from local studies performed under a limited set of climatic conditions. Here, we investigate how climate mediates the relationships between plant (taxonomical and functional) diversity and ecosystem stability across the globe. To do so, we coupled 14 years of temporal remote sensing measurements of plant biomass with field surveys of diversity in 123 dryland ecosystems from all continents except Antarctica. Across a wide range of climatic and soil conditions, plant species pools, and locations, we were able to explain 73% of variation in ecosystem stability, measured as the ratio of the temporal mean biomass to the SD. The positive role of plant diversity on ecosystem stability was as important as that of climatic and soil factors. However, we also found a strong climate dependency of the biodiversity–ecosystem stability relationship across our global aridity gradient. Our findings suggest that the diversity of leaf traits may drive ecosystem stability at low aridity levels, whereas species richness may have a greater stabilizing role under the most arid conditions evaluated. Our study highlights that to minimize variations in the temporal delivery of ecosystem services related to plant biomass, functional and taxonomic plant diversity should be particularly promoted under low and high aridity conditions, respectively.

Globally, estimating crop acreage and yield is one of the most critical issues that policy and decision makers need for assessing annual crop productivity and food supply. Nowadays, satellite remote sensing and geographic information system (GIS) can enable the estimation of these crop production parameters over large geographic areas. The present work aims to estimate the wheat (Triticum aestivum) acreage and yield of Maharajganj, Uttar Pradesh, India, using satellite-based data products and the Carnegie-Ames-Stanford Approach (CASA) model. Uttar Pradesh is the largest wheat-producing state in India, and this district is well known for its quality organic wheat. India is the leader in wheat grain export, and, hence, its monitoring of growth and yield is one of the top economic priorities of the country. For the calculation of wheat acreage, we performed supervised classification using the Random Forest (RF) and Support Vector Machine classifiers and compared their classification accuracy based on ground-truthing. We found that RF performed a significantly accurate acreage assessment (kappa coefficient 0.84) compared to SVM (0.68). The CASA model was then used to calculate the winter crop (Rabi, winter-sown, and summer harvested) wheat net primary productivity (NPP) in the study area for the 2020–2021 growth season using the RF-based acreage product. The model used for wheat NPP-yield conversion (CASA) showed 3100.27 to 5000.44 kg/ha over 148,866 ha of the total wheat area. The results showed that in the 2020–2021 growing season, all the districts of Uttar Pradesh had similar wheat growth trends. A total of 30 observational data points were used to verify the CASA model-based estimates of wheat yield. Field-based verification shows that the estimated yield correlates well with the observed yield (R2 = 0.554, RMSE = 3.36 Q/ha, MAE −0.56 t ha−1, and MRE = −4.61%). Such an accuracy for assessing regional wheat yield can prove to be one of the promising methods for calculating the whole region’s agricultural yield. The study concludes that RF classifier-based yield estimation has shown more accurate results and can meet the requirements of a regional-scale wheat grain yield estimation and, thus, can prove highly beneficial in policy and decision making.