High Granularity Remote Sensing and Crop Production over Space and Time: NDVI over the Growing Season and Prediction of Cotton Yields at the Farm Field Level in Texas

2008 ◽  
Author(s):  
Bert Little ◽  
Michael Schucking ◽  
Brandon Gartrell ◽  
Bing Chen ◽  
Kenton Ross ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Crop Production ◽  
Growing Season ◽  
Space And Time ◽  
Field Level

scholarly journals Analysis of Drought Impact on Croplands from Global to Regional Scale: A Remote Sensing Approach

2020 ◽  
Vol 12 (24) ◽  
pp. 4030
Author(s):  
Gohar Ghazaryan ◽  
Simon König ◽  
Ehsan Eyshi Rezaei ◽  
Stefan Siebert ◽  
Olena Dubovyk
Keyword(s):  
Remote Sensing ◽  
Crop Production ◽  
Regional Scale ◽  
Wheat Yield ◽  
Growing Season ◽  
Early Warning Systems ◽  
Agricultural Drought ◽  
Eastern Africa ◽  
Stress Index ◽  
Moderate Resolution

Drought is one of the extreme climatic events that has a severe impact on crop production and food supply. Our main goal is to test the suitability of remote sensing-based indices to detect drought impacts on crop production from a global to regional scale. Moderate resolution imaging spectroradiometer (MODIS) based imagery, spanning from 2001 to 2017 was used for this task. This includes the normalized difference vegetation index (NDVI), land surface temperature (LST), and the evaporative stress index (ESI), which is based on the ratio of actual to potential evapotranspiration. These indices were used as indicators of drought-induced vegetation conditions for three main crops: maize, wheat, and soybean. The start and end of the growing season, as observed at 500 m resolution, were used to exclude the time steps that are outside of the growing season. Based on the three indicators, monthly standardized anomalies were estimated, which were used for both analyses of spatiotemporal patterns of drought and the relationship with yield anomalies. Anomalies in the ESI had higher correlations with maize and wheat yield anomalies than other indices, indicating that prolonged periods of low ESI during the growing season are highly correlated with reduced crop yields. All indices could identify past drought events, such as the drought in the USA in 2012, Eastern Africa in 2016–2017, and South Africa in 2015–2016. The results of this study highlight the potential of the use of moderate resolution remote sensing-based indicators combined with phenometrics for drought-induced crop impact monitoring. For several regions, droughts identified using the ESI and LST were more intense than the NDVI-based results. We showed that these indices are relevant for agricultural drought monitoring at both global and regional scales. They can be integrated into drought early warning systems, process-based crop models, as well as can be used for risk assessment and included in advanced decision-support frameworks.

scholarly journals Interaction of Seasonal Sun-Angle and Savanna Phenology Observed and Modelled using MODIS

2019 ◽  
Vol 11 (12) ◽  
pp. 1398 ◽  
Author(s):  
Xuanlong Ma ◽  
Alfredo Huete ◽  
Ngoc Nguyen Tran
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Growing Season ◽  
The Sun ◽  
Space And Time ◽  
Ndvi Time Series ◽  
Angle Effect ◽  
Phenological Metrics

Remote sensing of phenology usually works at the regional and global scales, which imposes considerable variations in the solar zenith angle (SZA) across space and time. Variations in SZA alters the shape and profile of the surface reflectance and vegetation index (VI) time series, but this effect on remote-sensing-derived vegetation phenology has not been adequately evaluated. The objective of this study is to understand the behaviour of VIs response to SZA, and to further improve the interpretation of satellite observed vegetation dynamics, across space and time. In this study, the sensitivity of two widely used VIs—the normalised difference vegetation index (NDVI) and the enhanced vegetation index (EVI)—to SZA was investigated at four northern Australian savanna sites, over a latitudinal distance of 9.8° (~1100 km). Complete time series of surface reflectances, as acquired with different SZA configurations, were simulated using Bidirectional Reflectance Distribution Function (BRDF) parameters provided by MODerate Resolution Imaging Spectroradiometer (MODIS). The sun-angle dependency of the four phenological transition dates were assessed. Results showed that while NDVI was very sensitive to SZA, such sensitivity was nearly absent for EVI. A negative correlation was also observed between NDVI sensitivity to SZA and vegetation cover, with sensitivity declining to the same level as EVI when vegetation cover was high. Different sun-angle configurations resulted in considerable variations in the shape and magnitude of the phenological profiles. The sensitivity of VIs to SZA was generally greater during the dry season (with only active trees present) than in the wet season (with both active trees and grasses), thus, the sun-angle effect on VIs was phenophase-dependent. The sun-angle effect on NDVI time series resulted in considerable differences in the phenological metrics across different sun-angle configurations. Across four sites, the sun-angle effect caused 15.5 days, 21.6 days, and 20.5 days differences in the start, peak, and the end of the growing season derived from NDVI time series, with seasonally varying SZA at local solar noon, as compared to those metrics derived from NDVI time series with fixed SZA. In comparison, those differences in the start, peak, and end of the growing season for EVI were significantly smaller, with only 4.8 days, 4.9 days, and 3 days, respectively. Our results suggest the potential importance of considering the seasonal SZA effect on VI time series prior to the retrieval of phenological metrics.

A simple method to improve field-level rice identification: toward operational monitoring with satellite remote sensing

2003 ◽  
Vol 43 (4) ◽  
pp. 379 ◽  
Author(s):  
T. G. Van Niel ◽  
T. R. McVicar
Keyword(s):  
Remote Sensing ◽  
Agricultural Land ◽  
Atmospheric Correction ◽  
Critical Time ◽  
Growing Season ◽  
Simple Method ◽  
Rule Based ◽  
Irrigation Area ◽  
Field Level ◽  
The Impact

Discriminating crops by remote sensing remains reasonably complex and expensive for many agricultural land managers. The current study was conducted to facilitate the operational use of remote sensing for field-level rice monitoring in Australia by determining (i) whether existing methods relating to simple moisture-based rice classification could be further simplified, and (ii) whether the high accuracies resulting from that moisture-based methodology could be further increased. First, the impact of removing the most complicated processing step, atmospheric correction, on rice classification accuracies was assessed for the 2000–01 summer growing season at the Coleambally Irrigation Area, New South Wales. The primary error sources of rice classification were then identified and simple rules developed in an attempt to reduce errors associated with confusion between unharvested winter cereals and flooded rice paddies early in the summer growing season. These newly defined rules were then used on imagery acquired in the subsequent summer growing season (2001–02) in order to assess their repeatability. The assessment of atmospheric correction showed that during the critical time frame associated with high rice identification (October–November), using non-atmospherically corrected data increased overall accuracy, although the improvement was small (about 1%). Overall accuracy also increased for every case tested for both growing seasons as a result of the rule-based classification (ranging from about 1 to 14%), revealing that the methods were sufficiently repeatable. This study moves per-field rice monitoring at the Coleambally Irrigation Area closer to an operational application and shows that simple rule-based remote sensing classifications can be very effective when site practices are known.

scholarly journals Sun-Angle Effects on Remote-Sensing Phenology Observed and Modelled Using Himawari-8

2020 ◽  
Vol 12 (8) ◽  
pp. 1339 ◽  
Author(s):  
Xuanlong Ma ◽  
Alfredo Huete ◽  
Ngoc Tran ◽  
Jian Bi ◽  
Sicong Gao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Vegetation Change ◽  
Vegetation Index ◽  
Spectral Band ◽  
Growing Season ◽  
Phenological Stages ◽  
Space And Time ◽  
Quantitative Remote Sensing ◽  
Solar Noon

Satellite remote sensing of vegetation at regional to global scales is undertaken at considerable variations in solar zenith angle (SZA) across space and time, yet the extent to which these SZA variations matter for the retrieval of phenology remains largely unknown. Here we examined the effect of seasonal and spatial variations in SZA on retrieving vegetation phenology from time series of the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) across a study area in southeastern Australia encompassing forest, woodland, and grassland sites. The vegetation indices (VI) data span two years and are from the Advanced Himawari Imager (AHI), which is onboard the Japanese Himawari-8 geostationary satellite. The semi-empirical RossThick-LiSparse-Reciprocal (RTLSR) bidirectional reflectance distribution function (BRDF) model was inverted for each spectral band on a daily basis using 10-minute reflectances acquired by H-8 AHI at different sun-view geometries for each site. The inverted RTLSR model was then used to forward calculate surface reflectance at three constant SZAs (20°, 40°, 60°) and one seasonally varying SZA (local solar noon), all normalised to nadir view. Time series of NDVI and EVI adjusted to different SZAs at nadir view were then computed, from which phenological metrics such as start and end of growing season were retrieved. Results showed that NDVI sensitivity to SZA was on average nearly five times greater than EVI sensitivity. VI sensitivity to SZA also varied among sites (biome types) and phenological stages, with NDVI sensitivity being higher during the minimum greenness period than during the peak greenness period. Seasonal SZA variations altered the temporal profiles of both NDVI and EVI, with more pronounced differences in magnitude among NDVI time series normalised to different SZAs. When using VI time series that allowed SZA to vary at local solar noon, the uncertainties in estimating start, peak, end, and length of growing season introduced by local solar noon varying SZA VI time series, were 7.5, 3.7, 6.5, and 11.3 days for NDVI, and 10.4, 11.9, 6.5, and 8.4 days for EVI respectively, compared to VI time series normalised to a constant SZA. Furthermore, the stronger SZA dependency of NDVI compared with EVI, resulted in up to two times higher uncertainty in estimating annual integrated VI, a commonly used remote-sensing proxy for vegetation productivity. Since commonly used satellite products are not generally normalised to a constant sun-angle across space and time, future studies to assess the sun-angle effects on satellite applications in agriculture, ecology, environment, and carbon science are urgently needed. Measurements taken by new-generation geostationary (GEO) satellites offer an important opportunity to refine this assessment at finer temporal scales. In addition, studies are needed to evaluate the suitability of different BRDF models for normalising sun-angle across a broad spectrum of vegetation structure, phenological stages and geographic locations. Only through continuous investigations on how sun-angle variations affect spatiotemporal vegetation dynamics and what is the best strategy to deal with it, can we achieve a more quantitative remote sensing of true signals of vegetation change across the entire globe and through time.

Application of Remote Sensing in Agriculture

2014 ◽  
Vol 13 (1) ◽  
Author(s):  
Jan Piekarczyk
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Crop Production ◽  
Satellite Images ◽  
Crop Yields ◽  
Spatial Scales ◽  
Strong Relationship ◽  
Physical Parameters ◽  
Agricultural Crop ◽  
Remote Sensing Methods

AbstractWith increasing intensity of agricultural crop production increases the need to obtain information about environmental conditions in which this production takes place. Remote sensing methods, including satellite images, airborne photographs and ground-based spectral measurements can greatly simplify the monitoring of crop development and decision-making to optimize inputs on agricultural production and reduce its harmful effects on the environment. One of the earliest uses of remote sensing in agriculture is crop identification and their acreage estimation. Satellite data acquired for this purpose are necessary to ensure food security and the proper functioning of agricultural markets at national and global scales. Due to strong relationship between plant bio-physical parameters and the amount of electromagnetic radiation reflected (in certain ranges of the spectrum) from plants and then registered by sensors it is possible to predict crop yields. Other applications of remote sensing are intensively developed in the framework of so-called precision agriculture, in small spatial scales including individual fields. Data from ground-based measurements as well as from airborne or satellite images are used to develop yield and soil maps which can be used to determine the doses of irrigation and fertilization and to take decisions on the use of pesticides.

Estimating forage crop production: a technique adaptable to remote sensing

1978 ◽  
Vol 7 (1) ◽  
pp. 77-80 ◽  
Author(s):  
Robert J. Reginato ◽  
Sherwood B. Idso ◽  
Ray D. Jackson
Keyword(s):  
Remote Sensing ◽  
Crop Production ◽  
Forage Crop

scholarly journals Segmentation of Optical Remote Sensing Images for Detecting Homogeneous Regions in Space and Time

2018 ◽  
Vol 70 (5) ◽  
pp. 1779-1801
Author(s):  
Wanderson Costa ◽  
Leila Maria Fonseca ◽  
Thales Korting ◽  
Margareth Simões ◽  
Hugo Bendini ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Space And Time ◽  
Homogeneous Regions

scholarly journals Research of rice crops in Krasnodar region by remote sensing data

2020 ◽  
Vol 175 ◽  
pp. 01004
Author(s):  
Sergey Garkusha ◽  
Mikhail Skazhennik ◽  
Evgeny Kiselev ◽  
Vitaliy Chizhikov ◽  
Alexey Petrushin
Keyword(s):  
Remote Sensing ◽  
Crop Production ◽  
Remote Sensing Data ◽  
Test Site ◽  
Experimental Methodology ◽  
Federal State ◽  
Automated Mapping ◽  
Yield Information ◽  
Geoinformation Modeling ◽  
Sentinel 2A

The concept of digitalization of agricultural production in the Russian Federation provides for the implementation of measures to develop and create a system of geographic information monitoring and decision support in crop production. The aim of the research was to conduct geoinformation monitoring of rice crops to develop methods for automated mapping of their condition and yield forecasting. The studies were carried out on a test site of the Federal State Budgetary Scientific Institution “Federal Scientific Rice Centre” with an area of 274 hectares. The survey was performed by a quadcopter with a MicaSense RedEdge-M multispectral camera mounted on a fixed suspension. The shooting period using an unmanned aerial vehicle (UAV) was limited to early June and additionally used the Sentinel-2A satellite. To assess the state of rice crops, the normalized relative vegetative index NDVI was used. Based on the NDVI distribution and yield information from the combine TUCANO 580 (CLAAS), a statistical analysis was carried out in fields 7 and 9. Testing of the experimental methodology for monitoring crops in 2019 on the basis of remote sensing of test plots and geoinformation modeling and the statistical apparatus should be considered satisfactory.

scholarly journals Using remote sensing products to predict recovery of vegetation across space and time following energy development

2020 ◽  
Vol 110 ◽  
pp. 105872 ◽  
Author(s):  
Adrian P. Monroe ◽  
Cameron L. Aldridge ◽  
Michael S. O'Donnell ◽  
Daniel J. Manier ◽  
Collin G. Homer ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Energy Development ◽  
Space And Time
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