Statistical Databases

The importance of land surface-atmosphere interactions, principally the effects of soil moisture, on hydrological, meteorological, and ecological processes has gained widespread recognition over recent decades. Its high spatial and temporal variability however, makes soil moisture a difficult parameter to measure and monitor effectively using traditional methods. Microwave remote sensing technology has demonstrated the potential to map and monitor relative soil moisture changes over large areas at regular intervals in time and also the opportunity of measuring, through inverse modelling, absolute soil moisture values. This ability has been demonstrated under a variety of topographic and land cover conditions using both active and passive microwave instruments. The purpose of this paper is to review the current status of soil moisture determination from active microwave remote sensing systems and to highlight the key areas of research that will have to be addressed to achieve routine use of the proposed retrieval approaches.

Soil moisture retrieval from active spaceborne microwave observations: an evaluation of current techniques.

Combining medium and coarse spatial resolution satellite data to improve the estimation of sub-pixel NDVI time series

The perfect knowledge of yield before harvest has been a wish puzzling human being since the beginning of agriculture because seasonal forecast of crop yield plays a critical role in decision making for different stakeholders—from farmers to policy makers to governments for food security, to commodities traders. Different methods have been used to forecast yield with different levels of granularity, accuracy and timing. This chapter presents a critical review of the current seasonal crop yield forecasting methods found in the scientific literature. Extensive research has been conducted on crop yield forecast, particularly for wheat, maize, rice, barley, and soybean. Yield forecast are mainly based on field surveys, statistical regressions between historical yield and in-season variables (agrometeorological, or remotely sensed data), crop simulation models, or on integration between statistical modeling with dynamic process-based crop simulation models. A low number of studies rely on field surveys as a means to forecast yield, but they remain the main methods of yield forecast and estimation in several countries (i.e., USA). This chapter aims to report results found in peer-review journals for different crops, methods, geographies, and accuracies, and to end with a critical perspective on the advantage and disadvantage of the different methods currently employed by researchers and stakeholders.

Seasonal crop yield forecast: methods, applications, and accuracies.

Statistical modelling of crop yield in Central Europe using climate data and remote sensing vegetation indices

Yield estimation using SPOT-VEGETATION products: A case study of wheat in European countries

Time series data of vegetation indices derived from medium-resolution satellite data arewidely used for vegetation monitoring on a global or regional level. Unquestionably, the mostpopular indicator is Normalized Difference Vegetation Index, which derived from NOAA/AVHRR, SPOT/VGT, TERRA/MODIS or ENVISAT/MERIS with short revisit periods, canbe used as continuous daily time-series. Characterized vegetation condition can be furtherused for many environmental issues including yield forecasting, which is of special interestto the authors of this paper. Independently of further application, time series of NDVI have tobe prepared passing all the steps of processing. Noise reduction needs to be one of thosesteps, but it is often wrongly omitted. Numerous researches comparing methods of noisereduction in NDVI time series prove there is no one ideal method (Hird, McDermid 2009; VanDijk et al., 1987; Jonson, Eklundh, 2002; Chen et al., 2004; Beck et al., 2006). All of themassume that NDVI time series are related to vegetation changes and follow the annual cycleof vegetation. There is also agreement that clouds and other atmospheric conditions decreasethe NDVI value, so all sudden falls in the time series can be removed.Methods of noise removal in NDVI time series can be grouped into Fourier based fitting(Andres et al., 1994; Olsson, Eklundh, 1994), thresholds based (Viovy et al., 1992) functionfitting (Jonson, Eklundh, 2002; Beck et al., 2006) and several filtering methods (Chen et al.,2004; Velleman, 1980; Ma and Veroustraete, 2006). The most recent and exhaustive comparison

Noise reduction of ndvi time-series: a robust method based on savitzky-golay filter redukcja szum"w w szeregach czasowych ndvi z zastosowaniem filtra savitzky-golay

The study has been carried out at Biebrza Wetlands situated in the N-E part of Poland, a NATURA 2000 and Ramsar Convention test site in 2003-2009. It is one of the largest in Europe natural rich biotope with the large amount of unique species of flora and important zone for nesting and wintering for fauna. Data from microwave and optical satellite images and soil-vegetation ground measurements were analyzed to develop methods for monitoring and mapping biophysical parameters. Satellite data applied for the study included: ENVISAT.ASAR, ENVISAT.MERIS, ALOS.PALSAR, ALOS.AVNIR-2, and NOAA.AVHRR data. Optical images were used for classification of wetlands communities and calculation of vegetation index NDVI. Also, latent heat flux has been calculated using NOAA.AVHRR data and meteorological data. Microwave images acquired in different modes (ASAR IS2 and IS6, ALPSR.FBD) and polarizations (HH, HV, VV) were used for assessment and mapping of Leaf Area Index (LAI) and soil moisture (SM) for every habitat classified from optical images. Backscattering coefficient calculated from ALOS.PALSAR HV and ENVISAT.ASAR IS6 VV was applied for assessment of vegetation bio-parameters. Backscattering coefficient calculated from ALOS.PALSAR.FBD HH and ENVISAT.ASAR HH IS2 was used for SM assessment. The study was conducted in the framework of ESA PECS project No 98101 and ESA PI projects: C1P.7389 and AOALO.3742. Keywords—ENVISAT, ALOS, NOAA, LAI, biomass, heat fluxes, H/LE, soil moisture.

http://cejsh.icm.edu.pl/cejsh/element/bwmeta1.element.oai-journals-pan-pl-87108/c/oai-journals-pan-pl-87108_full-text_13.pdf

Wanda Kowalik

Papers

Yield estimation using SPOT-VEGETATION products: A case study of wheat in European countries

Noise reduction of ndvi time-series: a robust method based on savitzky-golay filter redukcja szum"w w szeregach czasowych ndvi z zastosowaniem filtra savitzky-golay

Biophysical Parameters Assessed from Microwave and Optical Data

The status of agricultural ecosystem examined by microwave data

Characterizing Status of Selected Ecosystems Using Optical and Microwave Remote Sensing Data