Estimating and mapping crop residues cover on agricultural lands using hyperspectral and IKONOS data

Bannari, A.; Pacheco, Anna; Stáenz, K.; McNairn, Heather; Omari, Khalid

doi:10.1016/j.rse.2006.05.018

Cited by 135 publications

(77 citation statements)

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“…Murray and Williams [39] suggested that a broad absorption feature near 2100 nm is also evident in the reflectance spectra of all dry crop residues, which is probably associated with lignin and cellulose in crop residue. Thus, spectral information at 1450-1960 nm and near 2100 nm is highly correlated with CRC and can be used to estimate CRC [4,10,14,16,18,19]. NDTI and STI were calculated from bands six (1560-1660 nm) and seven (2100-2300 nm) of the Landsat-8 OLI image data, and the NDTI-MRC and STI-MRC regressions yielded R 2 values of 0.84 and 0.78, respectively (Table 3).…”

Section: Discussionmentioning

confidence: 99%

“…Remote sensing is a useful tool to efficiently quantify CRC on a regional scale and provides spatial information of CRC within crop fields [7][8][9][10][11]. Early attempts to estimate CRC using remote sensing techniques began in 1975 [12].…”

Section: Introductionmentioning

confidence: 99%

“…Early attempts to estimate CRC using remote sensing techniques began in 1975 [12]. Over the past few decades, several methods have been used to estimate CRC in different regions based on multispectral and hyperspectral remote sensing data and spectroradiometric ground measurements of soil type, residue type, and residue percentage [7][8][9][10][11]13,14]. McNairn and Protz [15] analyzed the relationship between CRC and the normalized difference index (NDI) under different soil conditions using Landsat-5 Thematic Mapper (TM) data; the results presented a strong linear relationship between NDI and maize residue cover (R 2 = 0.65-0.84).…”

Section: Introductionmentioning

confidence: 99%

“…High estimation accuracy (R 2 = 0.91) between the normalized difference senescent vegetation index (NDSVI) and fractional senescent vegetation cover was achieved using a two-component linear unmixing analysis [17]. Crop residue cover has been estimated and mapped on agricultural land using hyperspectral and IKONOS data to demonstrate the strong relationship between predicted and measured CRC (index of agreement (D) = 0.92) [10]. The shortwave infrared normalized difference residue index (SINDRI) derived from ASTER bands six and seven is strongly related to CRC (R 2 = 0.74) [14].…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Maize Residue Cover Using Landsat-8 OLI Image Spectral Information and Textural Features

Jin

Wen

et al. 2015

Remote Sensing

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Abstract:The application of crop residue has become increasingly important for providing a barrier against water and wind erosion and improving soil organic matter content, infiltration, evaporation, temperature, and soil structure. The objectives of this work were to: (i) estimate maize residue cover (MRC) from Landsat-8 OLI images using seven vegetation indices (VIs) and eight textural features; and (ii) compare the VI method, textural feature method, and combination method (integration of textural features and spectral information) for estimating MRC with partial least squares regression (PLSR). The results showed that the normalized difference tillage index (NDTI), simple tillage index (STI), normalized difference index 7 (NDI7), and shortwave red normalized difference index (SRNDI) were significantly correlated with MRC. The MRC model based on NDTI outperformed (R 2 = 0.84 and RMSE = 12.33%) the models based on the other VIs. Band3mean, Band4mean, and Band5mean were highly correlated with MRC. The regression between Band3mean and MRC was stronger (R 2 = 0.71 and RMSE = 15.21%) than those between MRC and the other textural features. The MRC estimation accuracy using the combination method (R 2 = 0.96 and RMSE = 8.11%) was better than that based on only the VI (R 2 = 0.88 and RMSE = 11.34%) or textural feature (R 2 = 0.90 and RMSE = 9.82%) methods. The results suggest that the combination method can be used to estimate MRC on a regional scale. OPEN ACCESSRemote Sens. 2015, 7 14560

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimation of Maize Residue Cover Using Landsat-8 OLI Image Spectral Information and Textural Features

Jin

Wen

et al. 2015

Remote Sensing

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“…Nonetheless, the usage of sensors at a fine-scale enable greater precision in processed data (e.g. hyperspectral) and with high spatial resolution data, as demonstrated by BANNARI et al (2006), which achieved worthy results in the identification of agricultural residues. On the other hand, there are also researches looking for potential assessments regardless of data precision, which is an indirect evaluation of residues whereby crop performance is investigated to estimate the quantity of residues.…”

Section: Geotechnologies In Agro-energy Planningmentioning

confidence: 99%

Identification, assessment and ordering of concepts for agro-energy planning and possibilities of spatial analysis

et al. 2015

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ABSTRACT:The present study aims to present the main concepts of the sugarcane straw to energy planning. Throughout the study, the subject is contextualized highlighting broader aspects of sustainability, which is considered the main driver towards agro-energy modernization. Concerning sugarcane straw, we first evaluated its availability regarding technical and economic aspects, and then it summarized the straw production chain for energy supply purposes. As a proposal to support agro-energy planning, it is presented some spatial tools that have been barely used in the Brazilian energy planning context so far. Therefore, working on straw to electricity associated with supply chain basis, we developed a conceptual model to spatially assess this bioenergy system. Using the model proposed, it is described the whole supply chain at state level, which accounted the potential of a single mill to explore straw, as well as main costs associated with straw acquisition, investments on the straw recovery routes and electricity transmission. Bearing these concepts in mind, it is fully believed that spatial analysis can bring important information for agro-energy action plans.KEYWORDS: sugarcane, straw, agro-energy planning, geographic information system. IDENTIFICAÇÃO, AVALIAÇÃO E ORDENAMENTO DE CONCEITOS PARA O PLANEJAMENTO AGROENERGÉTICO E AS POSSIBILIDADES DA ANÁLISE ESPACIALRESUMO: O presente artigo busca apresentar os principais conceitos envolvendo um planejamento energético específico da palha da cana-de-açúcar. Ao longo do trabalho, procurou-se contextualizar o tema por meio de questões de sustentabilidade, considerada um dos principais indutores em direção à modernização da agroenergia. Considerando a palha da cana de açúcar, avaliou-se primeiramente sua disponibilidade envolvendo aspetos técnicos e econômicos, e posteriormente caracterizou-se a cadeia de produção da palha para energia. Como proposta para auxílio ao planejamento agroenergetico, apresentou-se algumas ferramentas espaciais que até então pouco têm sido utilizadas para planejamento energético no Brasil. Deste modo, trabalhando o potencial de palha para eletricidade associado à sua cadeia de produção, desenvolveu-se um modelo conceitual para se avaliar espacialmente esse sistema bioenergético. Utilizando o modelo proposto, foi possível descrever toda a cadeia de produção da palha para energia em um nível estadual, envolvendo o potencial que cada usina pode explorar, bem como os principais custos associados à sua aquisição, investimentos relacionados à rota de recuperação e em linhas de transmissão. Por meio dos conceitos abordados, acredita-se que análise espacial pode trazer importantes informações para planos de ação no âmbito agroenergético. PALAVRAS-CHAVE:cana-de-açúcar, palha, planejamento energético, sistema de informação geográfica.Identification, assessment and ordering of concepts for agro-energy planning and possibilities of sp atial analysisEng.

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Application of Hyperspectral Remote Sensing Role in Precision Farming and Sustainable Agriculture Under Climate Change: A Review

Pande

Moharir

2023

Springer Climate

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Estimating and mapping crop residues cover on agricultural lands using hyperspectral and IKONOS data

Cited by 135 publications

References 38 publications

Estimation of Maize Residue Cover Using Landsat-8 OLI Image Spectral Information and Textural Features

Estimation of Maize Residue Cover Using Landsat-8 OLI Image Spectral Information and Textural Features

Identification, assessment and ordering of concepts for agro-energy planning and possibilities of spatial analysis

Application of Hyperspectral Remote Sensing Role in Precision Farming and Sustainable Agriculture Under Climate Change: A Review

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